<div>The Generative AI trend is booming and transforming abilities to utilize AI. Large Language Models (LLM's) have become freely available through various tech companies, including ChatGPT by OpenAI and Bard by Google. Testing, experimenting and exploring the possibilities of AI are endless, and it only requires a browser, curious mind and some creativity to get started. Most recently, ChatGPT has released connections to real-time internet sources through Bing and brings more transparency to the sources of information. Corporations can start building their own OpenAI environments that allow more secure use of company's own data for model training and prompting. <br><br>This has been a transformative period also for data teams and experts as the focus has shifted from building to buying. Suddenly, an AI experimentation project can be carried out without a data scientist. Has this become even an identity crisis for some? We dare to say "yes, but..." and here is why:<br><br></div><h1><strong>1. The need for data scientists</strong></h1><blockquote><em>"Data Scientists are not needed anymore as we can use the OpenAI and similar Generative AI models"</em></blockquote><div>Yes, the need for data scientists changes. A data scientist, especially one that has NLP (Natural Language Processing) as a specialty area, needs to learn new tricks and how to apply the freely available LLM's. For example, help the business to decide when to use Generative or Foundational models, how to finetune them and when tailored model is needed. Data scientists are also needed to review and validate the output of the models, open up the basic functionalities and possibilities of these models to the business users and help them to innovate new use cases.<br><br></div><h1><strong>2. Self service AI development</strong></h1><blockquote><em>"Business can start using and developing the AI solutions independently"</em></blockquote><div>Yes, the independence grows in a similar way as in any self service analytics project. If you have high quality and trustworthy data available, and if you know what the input and output of the model is for it to behave in an expected way, then go for it! But if the data used to train or prompt the model is not complete, accurate, up-to-date or fit for the purpose, the end result might be worse than not using it at all. We have seen similar development with no code - low code analytics platforms that offer possibility to build and utilize models without coding skills. With these self service tools it is equally important to understand how the model works, what it can be used for and what is expected as an end result.<br><br></div><h1><strong>3. Developing AI competence and skills</strong></h1><blockquote><em>"Everybody can use ChatGPT today, we don't need any education how to use it"</em></blockquote><div>Actually, prompt engineering and model finetuning has become an important skill for any data scientist, analyst, software developer or business user. Additionally, learning how to apply AI in business in an ethical, sustainable and effective way and generate value to the business, customers and employees with it has become even more important. The use of AI should always have a clear purpose, the business needs to be aware of the risks and regulations, and the users some level of understanding how it works and can be applied. That is why AI education in companies has become even more important, covering the basics of how AI works and how it can be applied.<br><br></div><h1><strong>4. Importance of transparency, ethics and AI governance</strong></h1><blockquote><em>"With Generative AI, we can skip the boring data governance stuff and jump right into cool AI projects"</em></blockquote><div>When applying AI, also Generative AI and Foundational AI models, the need to understand data lineage and how data is processed, gathered and can be used still remains. When using company's own documents, information and data sources to train the model or as a model input, the business risks should be always carefully evaluated. With these Generative models, it can be difficult to have transparency to model outputs. This requires continuous testing and monitoring of the outputs, even if the models have been finetuned for better fit to the purpose. <br><br></div><h1><strong>5. AI boosting data development effectiveness</strong></h1><blockquote><em>"AI can replace all coding work in the future"</em></blockquote><div>Nice thing about the Generative AI solutions is that it can also be used to make the data engineering work more effective. Data engineers can start generating or validating code or test scripts with AI. AI can help detect data quality issues, automate data labeling or improve classification of data. As in any AI project, the models should be assistive to humans instead of targeting for full automation and one should always have ability to question the model output and performance.</div><div><br>If your organization or industry has not yet figured out how to utilize AI, the best thing that you can do is to start building the AI innovation and utilization skills. Organization that has solid data governance practices, basic guidelines how to use AI safely in the company context and has been able to get people excited to explore the possibilities of AI is already quite far. Let us help you figure out the areas to focus on to improve your AI readiness and ability to get the best out from the AI boom!<br><br></div>

Gen AI changing the role of data teams and experts

<blockquote>"Imagine a situation where your employer does not have an idea how the data flows in the organization, how it ends up to reports or feeding algorithms, if the data can be trusted or in case of issues, from whom to ask. Now imagine a situation where your employer has clear data ownership and contact persons, a data catalog for searching data assets, data quality monitoring that helps to identify potential data issues and clear guidelines for responsible, ethical and sustainable data usage. Now consider which one of these companies would you want to work for?"</blockquote><div><br>We’ve seen numerous waves of data hype in businesses and are reaching a point where data is produced from numerous sources in our organizations. Conventional wisdom states that data in itself is worthless – but how do we wield data to our advantage? <br><br>There is no silver bullet for capitalizing on the value of data, and the issue needs to be addressed at the systemic level. Many organizations need data governance, whether they realize it or not.<br><br>Here’s how to build working data governance in organisations.<br> </div><h1><strong>Why data governance?</strong> </h1><div><br>As data becomes ubiquitous, we need processes for making data work for our business goals. Data governance takes us beyond just producing data – it enables organizations to make data a success factor for them.<br><br>Fundamentally, data governance is about assigning the roles and responsibilities, building the tools and defining the processes for data in an organization's. It enables organizations to build an unified set of data that can be utilized in various parts and functions of the organization.<br><br></div><h1><strong>Data governance issues</strong></h1><div><br>Typical issues with data governance in organizations is the lack of responsibility and roles relating to data ownership.<br><br><strong>When you’re building data governance, the first question is: </strong><strong><em> "Is the ownership of data clear?"</em></strong><br>Like any other aspect of your business, data needs to be owned and managed. This happens at two levels:</div><ul><li>Data engineers are responsible for collecting, processing and making data available, in a technical sense. Business stakeholders need to understand the way data is collected and used in the organisation, and use it as a tool in their work and decision making. </li><li>Data strategists bridge the gap between IT and business. They can increase understanding and competence, fostering ownership of data and building workable processes. <br><br></li></ul><h1><strong>Quality of data</strong></h1><div><br></div><div><strong>The second big question of data governance is: </strong><strong><em>“What’s the quality of your data?”</em></strong><em><br></em>’I don’t know’ is the worst possible answer. It means that governance practices and ownership of data are not clear, or that the tools to manage data are not sufficient. <br><br>Data catalogs and data mesh platforms are often seen as solutions to various data challenges, but but either is a poor stand-in for proper data governance. Especially data mesh – the concept of the data platform as a distributed network of interlinked platforms specific to various business functions – is on the minds of many organizations looking to boost their use of data. <br><br></div><blockquote>As a rule of thumb: "The more distributed your data is, the stronger the governance models need to be."</blockquote><div><br>Proper data governance should be seen as a prerequisite for building a data mesh or data catalog and utilizing them properly.<br><br></div><h1><strong>How to build data governance</strong></h1><div><br>Building functional data governance is practical work. Each company is unique and needs an approach that fits their needs.<br><br>The unifying feature in successful data governance initiatives is that the change has been cultural, and fundamental. The ubiquity of data means that data governance can’t simply be tasked to IT – it needs business ownership, as well, and ownership and responsibility over data and its governance needs to be there throughout the organisation. This is the way to make data work for your organisation and reaping the benefits.<br><strong><br>The process is typically as follows:</strong><br><br><strong>1. Model data flows</strong></div><ul><li>This is Ground Zero. your organization needs understand how data flows through your organization and who own it. In the best scenario, company as a Data Catalog tool that support automated or assisted data lineage visualizations. However, organizations should not limit themselves in case a proper Catalog is missing – this can be done in any documentation tool, as long as it increases the user understanding of the data flows and origins.</li></ul><div><br><strong>2. Clarify responsibilities</strong></div><ul><li>Many organizations have given the ball to IT when it comes to data, but it needs to be a shared responsibility. Data strategists and data engineers help organizations build data governance, but assigning responsibility to both business and technical personnel is key. Many organizations name data stewards – specialists who know where data originates from and how business specialists can use it in a responsible way. Often data is also governed by data domain owners. For instance, the sales director might be the data domain owner for sales data – they ensure data is produced and utilized in sales operations. </li></ul><div><strong><br>3. Build the capabilities</strong></div><ul><li>In addition to ownership, data governance requires the competence to gather, sort, analyse and draw conclusions from data. This might mean educating people, building collaboration models and building or fixing tools for data management. </li></ul><div><br><strong>4. Clarify development roadmap</strong></div><ul><li>A clear development roadmap is key. This means that an experienced data strategist builds a roadmap and development plan for improving data quality. This is then put into practice by data engineers and developers. </li></ul><div><br><strong>5. Build data practices</strong></div><ul><li>Active management and documentation of data is key in a data platform, and automation helps ensure effective handling of data. Strong DevOps practices concerning data help make data an effective tool for your organisation.</li></ul><div><br><br></div><div><br></div>

Data governance unlocks the value of data

<div>Olemme perustamassa uudenlaista teknologia-, data- ja digitaalista konsultointiliiketoimintaa, AI Rootsin ollessa yksi perustajista. Sevendoksen ydinideana on koota yhteen seitsemän erikoistunutta asiantuntijayritystä, jolloin voimme tarjota asiakkaillemme tiimin eri osaamisen huippuasiantuntijoista ja tukea näin asiakkaitamme digitaalisten tuotteiden ja palveluiden kehityksessä niiden koko elinkaaren ajan.<br><br>Missiomme on haastaa markkinat hybridikonsultointimallilla: Erikoistuneet asiantuntijayritykset, jotka toimivat yhtenä tiiminä. Visiomme on siirtää perinteinen yksikokoinen konsultointilähestymistapa "Kaikki mitä tarvitset, ei mitään jota et".<br><br>Sen perustaa seitsemän erikoistunutta teknologiayritystä, joilla on omalla alallaan korkea asiantuntemus:<br><br>AI Roots Oy – Data-alan huoppuosaajien, -kehittäjien ja yrittäjien asiantuntijatoimisto<br><a href="https://www.cyberdo.fi/">CyberDo</a> – Agency tarjoaa freelance-kyberturvallisuusasiantuntijoita projekteihin, joissa omistautuminen ja tulokset ovat tärkeitä<br><a href="https://www.finitec.fi/">Finitec</a> – Johtava freelance-agentuuri IT-ammattilaisille.<br>Hidden Trail – Uudenlaista laadunvarmistusta keskittymällä laatuongelmien perimmäisiin syihin pelkän testauksen sijaan<br><a href="https://orangit.fi/">Orangit</a> – Ohjelmistojen ylläpitopalvelut, jotka auttavat asiakkaiden palveluita toimimaan kitkattomasti, pysymään ajan tasalla ja kehittymään<br><a href="http://polarsquad.fi/">Polar Squad</a> – DevOps-konsultointia, jonka avulla tehdään teknologiatyöstä mielekästä rakentamalla parempaa tenologiaa, kulttuuria ja organisaatioita<br><a href="https://www.wunderdog.fi/">Wunderdog</a> – Kestäviä projekteja ja tuloksia: suunnittelusta ja teknologiasta strategiaan ja toteutukseen.<br><strong><br>Mitä tämä tarkoittaa data-freelance-verkostollemme ja asiakkaillemme?</strong><br>Tietojen kehittäjät työskentelevät harvoin yksin, mutta monissa tapauksissa heidät on integroitu muihin teknologiakehitys- tai liiketoiminta-alueisiin, kuten myyntiin, markkinointiin, tuotekehitykseen tai tuotantoon. Datakehittäjät tekevät yleensä yhteistyötä ohjelmistokehittäjien, suunnittelijoiden tai markkinoijien kanssa. Siksi on hienoa, että voimme tarjota kaiken tämän asiantuntemuksen asiakkaillemme saman katon alla, yhteistyössä muiden Sevendos-yritysten kanssa.</div>

AI Roots is one of the seven founding companies of Sevendos

<div>The beginning of the year 2023 was a time of change for our network and operations, as we became a subsidiary of Finland's leading technology agency Finitec Oy. AI Roots brand remains independent and the existing core team continues in their current roles. This is great news to our network members, as we become stronger in solving our customers' versatile technology and data resourcing needs, and offering the entrepreneurs in our network better service and project opportunities.  </div><h1>Our ability to serve our network members improves </h1><div>We have developed a unique concept that brings entrepreneurs and companies together in the data industry, which enables companies' data teams to scale flexibly and with high quality. At the same time, we actively collaborate with our customers to search for roles and projects that match the skills of the entrepreneurs in our network. We offer entrepreneurs the freedom to choose projects and customer relationships that best develop their own expertise. In addition, we offer support to entrepreneurs with a community of other top experts in the data industry.  <em>"Our goal from the very beginning at AI Roots has been to create a new and scalable solution for solving the growing demand for data expertise. Our operations combine a strong customer and data understanding and a way to solve customer needs with the help of our network of top data experts. The merger with Finitec is a logical step, with which we can now together continue to serve Finnish companies by bringing the entrepreneurial know-how to wider use, while offering our entrepreneurs more interesting project opportunities."</em> rejoices Teemu Birkstedt, who will continue working as the AI Roots board member. </div><h1>Finitec's data expertise is getting stronger<strong> </strong></h1><div>Finitec is Finland's leading agency for freelancers in the technology industry. At the center of its operations are committed top experts who solve the challenges of customers' business and technology development resourcing. The goal is to offer independent software development, design and management professionals the best project opportunities in the field.<br> <br> With the merger of Finitec and AI Roots, the range of IT experts in the joint network will increase and the special expertise related to data will be strengthened. Both companies still belong to the Bravedo community, and through Finitec, to the Fusion Ecosystem. Finitec and AI Roots have already experience of working together through Bravedo community.<br> <br> <em>"Our business operations are very similar, but there are also differences in our operating models, both towards customers and the entrepreneur network. We believe that we will get a lot of new perspectives from each other for the development of our combined operations and thus make us even stronger players in the field."</em> says Finitec CEO Pekka Hackspik.<br><br><action-text-attachment content-type="image" url="https://hs-6795090.f.hubspotstarter.net/hub/6795090/hubfs/Finitec_3_kropattu.jpg?width=1120&amp;upscale=true&amp;name=Finitec_3_kropattu.jpg" width="1120" height="608" caption="Markku Seraste joins the AI Roots partner team"></action-text-attachment></div><h1> AI Roots core team expands</h1><div>With the merger, Markku Seraste became part of AI Roots' core team transitioning to the team from Finitec. He brings to AI Roots expertise in developing high quality service towards freelancers and our customers. In addition, Markku has extensive experience in managing IT projects, which is a valued area of expertise in our customer base. The current team of AI Roots, Ari Rantanen, Iiris Lahti, Max Rantakallio will continue in the core team as before and Teemu Birkstedt as the member of the board. Some effect to for example monthly invoicing practices and contact details can be expected, but we will inform separately the ones impacted. <br> </div>

AI Roots becomes a subsidiary of Finitec

<div>Today, more and more companies know the value of data and want to harness the possibilities of data to speed up and develop their own business. Succeeding in this, however, requires solid knowledge and expertise in the data industry. AI Roots helps companies by connecting the best entrepreneurs operating in the field and companies that need their expertise. Our network includes a wide range of experts, such as data architects, analysts, data scientists, BI developers, and engineers. The entrepreneurs who belong to our network are experienced professionals who have the necessary skills to act as independent entrepreneurs in the rapidly developing data industry. Entrepreneurship is a growing trend in many industries and can also be seen in the fields of data, analytics, and artificial intelligence. More and more top experts want to work on their own terms and work with various clients as independent entrepreneurs, who are also able to function seamlessly as part of the company's internal teams. <br><br></div><div><strong>Frequently asked questions:</strong></div><ul><li>Why would it make sense for a company to choose an entrepreneur from the AI Roots network?</li><li>How long does an entrepreneur's assignment usually last?</li><li>Does AI Roots help companies in recruiting?</li><li>What is a typical assignment like?</li><li>For what kind of needs can I get a professional from the AI Roots network?</li><li>Will acquiring an entrepreneur be expensive?</li><li>How do I know that the skills of the entrepreneur match the needs of my company?</li><li>Who can I contact for more information?</li></ul><div><br><strong>Why would it make sense for a company to choose an entrepreneur from the AI Roots network?</strong></div><div>Those who have progressed to become entrepreneurs are usually the most experienced in the field, and the expertise they have accumulated is at a level where it can be offered through their own companies. Entrepreneurs always represent themselves and usually have an internal motivation to provide high-quality service to their customers. We also claim it is possible to develop a personal relationship with the entrepreneur, in which things can be agreed upon flexibly and the entrepreneur often brings an outside perspective and his or her own personality to the organization.</div><div><br><strong>How long does an entrepreneur's assignment usually last?</strong></div><div>You can choose an entrepreneur for a single project, as part of a longer assignment, or even help build the entire DataOps team from the beginning. A typical assignment takes about 3 to 12 months.</div><div> </div><div><strong>Does AI Roots help companies in recruiting?</strong></div><div>AI Roots is not a recruitment agency, but an agency for professionals working in small consulting firms or as independent freelancers. We have subcontracting agreements with these experts and handle all the needed contracts and invoicing with our customers. Typically companies resource these experts with specific allocation and invoicing is based on actual delivered working hours. In that sense, we operate in a similar way as any other consulting company.</div><div> </div><div><strong>What is a typical assignment like?</strong></div><ul><li>The entrepreneur is selected to fill a position for which it has been difficult to find an internal person through recruitment or the departure of an internal employee comes unexpectedly.</li><li>The entrepreneur is selected to bring expertise to the team that is difficult to find in the market and that may not be found in the internal team.</li><li>Having an entrepreneur as a part of the team even for six months increases the competence level of the internal team, as an example ML Ops coaching roles.</li><li>A typical assignment takes about 3–12 months. Often the entrepreneur is employed with 80–100 % allocation and sometimes also with 50–60 % allocation.</li></ul><div><br><strong>For what kind of needs can I get a professional from the AI Roots network?</strong></div><div>Our entrepreneurs have versatile expertise in data, analytics, and artificial intelligence tasks. With the help of external expertise you can, for example, scale your team with the entrepreneur’s specific expertise, clarify your data strategy, renew your data architecture, develop your analytics tools and<br>artificial intelligence solutions, improve the quality of your data, and deepen your company's understanding of data and its possibilities. The turnover rate of our experts is very high, so when you find the right vacant worker, you should be quick.</div><div> </div><div><strong>Will acquiring an entrepreneur be expensive?</strong></div><div>A full-time, experienced entrepreneur is a little more expensive in terms of costs compared to the salary costs of a similar expert working as an internal employee. However, it is possible to reduce costs, for example, by lowering the allocation to 60–80 percent. The service of an experienced entrepreneur is an investment in the company's data capability and pays for itself.</div><div> </div><div><strong>How do I know that the skills of the entrepreneur match the needs of my company?</strong></div><div>The AI Roots network includes only solid professionals in the field, who are selected through a careful process, in which the entrepreneur's professionalism and suitability are assessed before the assignment starts. We make sure that our entrepreneurs are ready to commit to every customer project, can work flexibly if needed, and help raise your organization's data capabilities to a new level.<br><br><br><strong>Who can I contact for more information?</strong></div><div>We will be happy to answer any questions if you need more information about our network and data. We at AI Roots support companies in various industries and phases of the data journey. We can help to scale up a more mature data team or help in establishing one. If you would like to explore our talent base and different ways how we can support you, book a 30 - 60 minute introduction meeting from any of our patners.</div>

Frequently asked questions (FAQ)

<div>Design Thinking methods can be used to find the underlying motivations, feelings and core needs of the user, innovating new solutions and optimizing the overall user experience. Design Thinking methods can be applied also for data solutions, as they are designed most often with an employer, team or customer in mind. Focusing more time on building user understanding in the first phases of the data development project will save a lot of development effort and iterations in the later phases. It will also help to improve the collaboration and communication of the data solutions between developers, analysts and business users resulting in higher user satisfaction and ability to create value from data.<br><br></div><div>However, most often data solutions can't be designed only based on what the end user wants. The data might not be available or fit for the purpose. There might not be enough development capacity as there might be other solutions that have higher priority. The solution might increase the user satisfaction, but the wider business value creation potential might not be that high. There might be a data security or privacy risk  that the organization is not willing to take. Do these challenges sound familiar?<br><br></div><div>I have experience from utilizing these methods successfully in designing data solutions for media services while working for <a href="https://sanoma.fi/tiedote/data-on-yksi-tulevaisuuden-menestystekijoista/">Sanoma Media Finland.</a> In this article I interview <strong>Minna Kärhä</strong>, Head of Data at Finnair and <strong>Clemens Westrup</strong>, Head of Data Science at Sanoma Media Finland. They are both strong evangelists of this method and can share some benefits and concrete examples of using these methods in practice.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMzJlOGVhMWItNTA5MC00NDg3LWEzYmMtZGFiMjUxNjZiZmU5P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--c97a761c8e93f2ae58f3540e3a27eaa60dd2c159" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt6TW1VNFpXRXhZaTAxTURrd0xUUTBPRGN0WVROaVl5MWtZV0l5TlRFMk5tSm1aVGtHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--d69d45b82a4acc1ff4fdf568cbd85e27655d90ef/AIRoots-Iiris-Lahti.jpg" filename="AIRoots-Iiris-Lahti.jpg" filesize="115377" width="1050" height="791" previewable="true" presentation="gallery"></action-text-attachment></div><div><br>You can also check our recorded presentation of the learnings and experiences from Sanoma Media Finland and data design. The video was originally presented at the <a href="https://datainnovationsummit.com/"><strong>Data Innovation Summit 2020</strong></a>.</div><div> <br><br><a href="https://www.youtube.com/watch?v=8egOgW0oTlI&amp;embeds_euri=https%3A%2F%2Fwww.rootsof.ai%2Fblog%2Fdata-design&amp;embeds_origin=https%3A%2F%2Fwww.rootsof.ai&amp;source_ve_path=MjM4NTE&amp;feature=emb_title">https://www.youtube.com/watch?v=8egOgW0oTlI&amp;embeds_euri=https%3A%2F%2Fwww.rootsof.ai%2Fblog%2Fdata-design&amp;embeds_origin=https%3A%2F%2Fwww.rootsof.ai&amp;source_ve_path=MjM4NTE&amp;feature=emb_title</a></div><div> </div><h1>Applying data design to designing any data solution</h1><div>When developing the methodology, we often found us wondering what do we actually mean with a data solution or where this method can be applied. These are a few examples:<br><br></div><ul><li>Dashboards and reports</li><li>Presentations with storytelling through graphs and insights</li><li>Data catalogs and dictionaries</li><li>Data and information architecture and tools</li><li>Algorithmic features in operational systems or digital services</li></ul><div><br>We have split these into three categories:<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvOTE1ZWI5YmMtMWQ3MS00YzEyLTkwYzAtMmQ5ZTgyOTBmMDk0P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--f70dedc120e78129f4512a496fa7916a714178a7" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs1TVRWbFlqbGlZeTB4WkRjeExUUmpNVEl0T1RCak1DMHlaRGxsT0RJNU1HWXdPVFFHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--f01565271073076c494f2618f92e8ed8651c43ab/Data%20design%20application%20areas.png" filename="Data design application areas.png" filesize="761079" width="1050" height="593" previewable="true" presentation="gallery"></action-text-attachment></div><div><br><strong>Designing data visualizations and user interfaces</strong> can be compared to designing web sites or applications.  In modern data visualization tools you can improve the data usability a lot by focusing on layout, navigation, buttons, use of colors, fonts and graphics and careful selection of data. The most important part in designing a data visualization solution is to understand the core user needs for the data and in which kind of situations the data is used. <br><br></div><div>The data design method can be also applied to <strong>designing data tools and systems</strong>, by referring to data catalogs, application interfaces, data warehousing solutions and the overall architecture. The target of these kind of data solutions is to improve data accessibility, quality and usability in an organization.<br><br></div><div>Finally, data design can be also applied to <strong>designing and innovating new data services, features or products</strong>. This refers to for example recommendation engines, personalization features, chat bots, robotics and process automation and basically every feature in an operative system or process that uses processed data to perform better. This requires both innovation capability and understanding of what data is available and how it can be used for creating new features.</div><div> </div><h1>Data design method in practice</h1><div>It is hard to define a simple toolkit of methods that would work for every organization or project. We promote flexibility in selecting the methods, based on organization culture, team competencies and working models and solution complexity. The main principle in user-centric development of a data solution is to continuously improve your understanding of the core user needs and challeng your ideas about the potential solutions. Sometimes a coffee break or Teams discussion with a few potential users  might help you get on the right track. Sometimes data-based analysis on the current solution's user statistics is needed to understand the background a bit better. If the solution has a wide audience, you might want to try a survey to the users. There are also cases that are so difficult to get into that organizing a bit more structured design sprint or a innovation workshop is needed. If you know that it requires a lot of work to develop the solution, you might want to build a prototype first to check that you are going to the right direction with your plan. <br><br></div><div>There is not a single set of methods, tools, interview questions or project plans that will work in every case. Some cases are very simple and straightforward, some need more investigation, analysis and design work. In general, typical design projects contain these type of phases: <br><br></div><ul><li>Discovering  and understanding the user needs</li><li>Evaluating ideas based on business value creation potential, technical feasibility and risks</li><li>Innovating potential solutions for solving the user needs</li><li>Experimenting alternative solutions and validating value creation potential</li><li>Productizing, testing, optimizing  and monitoring the final solution after it has been taken into use</li></ul><div><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvYjdiM2NjMmEtY2YzMi00Y2I3LWJkOTktOTJlMmI5ZDcxYjEzP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--7bf572498ff293449e50928f8bd7e1987176e6c8" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxpTjJJelkyTXlZUzFqWmpNeUxUUmpZamN0WW1RNU9TMDVNbVV5WWpsa056RmlNVE1HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--60d4f2f658310eef5b77b6267f289b0fd47225fa/ezgif.com-webp-to-jpg.jpg" filename="ezgif.com-webp-to-jpg.jpg" filesize="406231" width="1050" height="595" previewable="true" presentation="gallery"></action-text-attachment><br><br><br></div><h1>Why design thinking methods are not enough</h1><div>When designing data solutions, it is quite unlikely that you can deliver exactly what the users need. Usually, there are a few things to consider, such as<br><br></div><ul><li>What benefits would the solution create for the business, customers, employees or operations?</li><li>What is the technical readiness to start developing the solution, by meaning data architecture, accessibility, quality and fit to the purpose?</li><li>What is the operational readiness to take it into use, by meaning processes, competencies and willingness to adopt new tools?</li><li>Are there any risks of failing or delaying in the product development or does the data solution contain otherwise privacy or security risks?</li><li>Do you have enough development and maintenance capacity and skills available?</li><li><br></li></ul><div>Since there are a lot of aspects to investigate and take into consideration, we decided to develop a canvas to help the design work. The canvas is applicable to any data solution design project. You can use it in a very light way to evaluate your existing idea or have a bit more extensive project by gathering information more widely across the organization. You can use it to evaluate a single idea, or generate multiple ideas for comparison. If you are interested on using the canvas in your work, <a href="mailto:iiris@rootsof.ai"><strong>drop us a note</strong></a> and we will guide you through it and provide you with a template. The canvas 1.0. version below is an example but when training the method to our customers, we usually make modifications to it and make sure that it fits to the overall design and demand practices of the organization. The canvas can be also embedded to other project planning and documentation tools, such as Confluence.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvOTgwNGQ4ZjItYjUxNC00MDA5LWJiMGYtMjY1YmZlNTE0ZGVjP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--3b824116a188da1b2c0e7dab201a7d8fd46a26fe" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs1T0RBMFpEaG1NaTFpTlRFMExUUXdNRGt0WW1Jd1ppMHlOalZpWm1VMU1UUmtaV01HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--1828ffd5ace8df5d0cbdee95602ba01617d20880/Data%20Design%201.0.%20Summary%20canvas%20guide.png" filename="Data Design 1.0. Summary canvas guide.png" filesize="307357" width="1050" height="742" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><h1>Benefits in using data design</h1><div><a href="https://www.linkedin.com/in/minna-k%C3%A4rh%C3%A4-926779/"><strong>Minna Kärhä</strong></a>, Head of Data at Finnair, has been actively involved in developing the data design method. Minna shares her experiences on the importance of user-centric design in developing data solutions:<br><br></div><ul><li>Crystallizing the right problem to be solved</li><li>Helping the team to make decisions and prioritize their work for the most value adding and best time-to-market solution</li><li>Ensuring the team will consider the critical aspects before finalizing the solution design</li><li>Creating great team spirit for success by empowering all the experts to formulate the final solution together</li><li>Increasing the overall understanding of data ownership and responsibilities and how data in created and used in the business</li></ul><div><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMWIwOTUwOWEtOGNjNS00MWYwLTg0ZGItMjk0MzU5MDA4YTE5P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--4aeccda7564084719e35d88b40bed8824b2c6e14" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt4WWpBNU5UQTVZUzA0WTJNMUxUUXhaakF0T0RSa1lpMHlPVFF6TlRrd01EaGhNVGtHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--c106898b5c6c61a2068915a3d3911eab7dd9497c/Minna%20Ka%CC%88rha%CC%88.jpg" filename="Minna Kärhä.jpg" filesize="130359" width="1050" height="826" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><h1>Experiences in utilizing data design</h1><div><a href="https://www.linkedin.com/in/clemenswestrup/"><strong>Clemens Westrup</strong></a>, Head of Data Science at Sanoma Media Finland, shares Minna's experiences. Along with his study background in Cognitive Computer Science and Machine Learning, Clemens has extensive experience as a Service Design practitioner. An alumni of the Stanford University based global Design Thinking program ME310, he spent two years teaching design methods and processes at Aalto Design Factory, as well as running trainings for companies. He is one of the originators of the methods in this post – tailoring user-centric design to data product development – and we spent a few years together iterating and improving the approaches in Sanoma Media Finland’s Data teams.<br><br>“It was always obvious to me that designing products, which rely on data as their main driver for value creation, would hugely benefit from user-centric methods, just as any other product or service does. But it is not exactly straightforward to apply for example classic Design Thinking, which has its roots in building physical products, in this domain. A seasoned service designer can easily adapt the process and methods, but what if you came out of your first workshop on user-centric design, ready to throw your new skills onto real world problems, but don’t know where to start? Therefore an opinionated but specifically tailored minimal framework can really help, for newcomers to design methods or experienced designers that are new to the challenges of data products."</div><div><br>Clemens continues explaining the most recent experiences on applying Design Thinking to Data Science: "At Sanoma we are taking an iterative grass-roots approach to bringing about the gradual adaption of user-centricity in our data teams and beyond. We organized a workshop together with our friends at Aalto Design Factory for the initial spark and to give familiarity. We then ran so-called Service Design Essentials projects on personalization in a 6-workshop format, in collaboration with Ruutu and Oikotie Työpaikat, to reiterate the process and tie it into the context of real product delivery with cross-functional teams. We started injecting specific tools, such as design briefs, into our planning processes and started encouraging experimentation with the tools in hackathons and ongoing projects. And most importantly, we are adapting it all to Sanoma’s culture and language. Because if you look closely, many people are already using these methods and processes, they might just not call them Service Design – and who cares really what you call it. It is our task to support these bottom-up drivers to foster wider adaption and exchange towards a more data-driven culture of innovation that we make our own.”<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMzI1MTczOTgtMjAzOC00MmIyLWFjY2QtZmEyNDQzMTNlZWUyP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--d6dfd5a2304e229240e857d5e52c55b00ef23352" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt6TWpVeE56TTVPQzB5TURNNExUUXlZakl0WVdOalpDMW1ZVEkwTkRNeE0yVmxaVElHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--ff11e88d59dc57656e63b40f4e7dfd0d079fd6be/Clemens-westrup.jpg" filename="Clemens-westrup.jpg" filesize="37750" width="778" height="698" previewable="true" presentation="gallery"></action-text-attachment><br><br></div><h1>What is unique in data design method</h1><div>As Clemens mentions, it is not about what specific methods are used but more about how to adapt and tailor the methods to fit to the unique organization culture and environment. We also believe that data development projects have a few unique features that encouraged us to start developing the data design method. Taking into account the data availablility and quality, compliancy and usage restrictions and fit to the purpose are a few examples. We are also focusing on ensuring that the method stays practical and can be carried out  as a natural and easy step in the data development process. <br><br></div><h1>How the data design method differs from a traditional design thinking:</h1><ul><li>Specifically tailored and hand-picked methods for data and analytics development work instead of standard tools and templates</li><li>Practical examples and simple tips to be taken into use immediately without too many theoretical frameworks</li><li>Focus on doing and practicing with real-life cases </li><li>Possibility for peer-to-peer coaching with other companies</li></ul><div> </div><h1>Contact us for help</h1><div>We can offer wide variety of trainings to help you get started. One very effective way is also picking up a tricky user need or data solution idea and starting to practice the method with a real-life case. Please contact us for more information and help.</div><div>Contact details:<br><br></div><div>Iiris Lahti | Email: <a href="mailto:iiris@rootsof.ai"><strong>iiris@rootsof.ai </strong></a>| Phone: +358405188207<br><br><br></div><div><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNDA1YjRkOTEtMDdhNS00YmE2LThjMTMtZTczOTFmZmQ1MTc3P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--75c4c6c4758d720c6c566f66ffa6742b81b375f0" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWswTURWaU5HUTVNUzB3TjJFMUxUUmlZVFl0T0dNeE15MWxOek01TVdabVpEVXhOemNHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--6760d043ecd2211fc48e2533956fa629843e519e/AIRoots-68.jpg" filename="AIRoots-68.jpg" filesize="377051" width="1050" height="701" previewable="true" presentation="gallery"></action-text-attachment><strong><br></strong><br></div>

Introducing data design

<div>Employment as we know it is in the middle of a great disruption. It is expected that by 2030, 50% of US employees will be self-employed, and the same direction is foreseeable in Europe.  Employees everywhere are looking for new, more flexible and independent ways to work. At the same time, both start-ups &amp; corporates are building their resourcing strategies  around partnerships and networks. Covid pandemic has greatly relaxed the location and time constrains related to the work and further enabled the use of partnerships in resourcing. But while people are looking for more independent ways of employment, they still want to belong to professional networks and professional communities. This is the premise that AI Roots is built on. </div><div> </div><h1>Transforming data consulting</h1><div>Our core mission is to transform the data and AI consulting and resourcing. We want to provide an effective and flexible way for hiring data professionals. We offer high-quality AI and data development services to our customers, taking into account the maturity, unique business needs and the competence building strategy of the customer. At the same time, we want to support freelancers and small consulting companies to get employed, succeed, and grow.<br><br></div><div>AI Roots was not founded only to find right talent to match the identified customer need. We are here to solve our customers’ business problems in efficient and sustainable way. AI Roots has built a strong and committed partner team that will dive into the customer’s business to discover and asses the opportunities to create true business impact with data and AI. The close collaboration between the customer, AI Roots partners and data professionals from our network ensures that we will not only “do things right” but also “do the right things” to solve customer business problems and to realize previously untapped opportunities.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMzc4ZmM3MzEtYWQ1Ni00MTcxLWJmM2MtYWQwOWQyN2RiYTYzP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--379fec379952edac2e9016450bda4197340962de" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt6TnpobVl6Y3pNUzFoWkRVMkxUUXhOekV0WW1Zell5MWhaREE1WkRJM1pHSmhOak1HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--69b8c9d32fb28d5fbd130338851d48860ec00ae6/AIRoots-68-2.jpg" filename="AIRoots-68-2.jpg" filesize="88495" width="1050" height="538" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><h1>Partnering with top talent</h1><div>Even for an experienced independent data professional it can be hard to find inspiring  clients or projects. There are also other challenges, such as not being able to accept interesting larger scale projects that require variety of competences for a successful delivery. Taking care of the sales, marketing, personal development and brand building can be overwhelming for an entrepreneur. And sometimes you will just need a sparring partner to discuss about professional – or extraprofessional – topics.<br><br></div><div> AI Roots can help both independent data freelancers and small businesses to overcome these challenges by working actively with customers to identify project opportunities and by collaborating with the network to find the optimal means to respond to customer needs by combining the expertise in the network to a high performing team. We also help entrepreneurs to get more visibility with joint marketing efforts, organize networking events for data professionals to exchange ideas and more.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvYjBmMGEzZmItMzQ2NC00NWIyLWJlYjItY2U3NTU4ZWZhMGIwP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--ca4667013c612f8c55dad6d78a8a2808a569787e" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxpTUdZd1lUTm1ZaTB6TkRZMExUUTFZakl0WW1WaU1pMWpaVGMxTlRobFptRXdZakFHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--02da5f83dc53f42cfeaa668b637d70f03bec4418/AiRoots-95.jpg" filename="AiRoots-95.jpg" filesize="338358" width="1050" height="701" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><h1>Collaboration with Veracell</h1><div>One of the top AI growth companies we have had a privilege to collaborate with is <a href="https://www.veracell.com/"><strong>Veracell</strong></a>. They deliver cutting-edge, production grade data and AI driven solutions and no-nonsense consulting to customers who strive to empower themselves with the improved use of data. Veracell seniors  are multitalents with a great variety of AI, software development and business advisory expertise. Project experience of Veracell people stretches over a multitude of business and technology domains, including IoT, healthcare, eCommerce, manufacturing, eCommerce, distributed computing platforms, and forensics.<br> <br><br></div><div>Timo Erkkilä, CEO of Veracell, shares our positive experiences on the collaboration:<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvODNjZTFmMmItNGQyZi00YjM4LWEwZjItNTk5NzkxMWRiMDlhP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--a77d347b3908a82bc8003d8f5d489e8ae2a2ef48" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs0TTJObE1XWXlZaTAwWkRKbUxUUmlNemd0WVRCbU1pMDFPVGszT1RFeFpHSXdPV0VHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--8cb85b964ca4d947d9784bf1c7801b7250f90e2a/P6180174.jpg" filename="P6180174.jpg" filesize="76654" width="1050" height="601" previewable="true" presentation="gallery"></action-text-attachment></div><blockquote><em>"Veracell operates with a minimal sales team and we see us as an ideal partner to AI Roots in extending their own business capabilities and providing scalable solutions for their customers. We rely on AI Roots expertise and leadership in both finding the right type of clients and to managing project expectations. In addition, AI Roots supports us in managing the planning and execution phases. The two companies compliment each other well in joint workshops and client cases."<br></em><br></blockquote><div>By having companies like Veracell in our network we feel very confident in attacking even the hardest of challenges to provide sustainable business value by innovative use of data and AI, from AI strategy and roadmaping to the implementation of cutting-edge AI services.<br><br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvM2MxNzJhMDctNWZkMy00ZGJiLTlkNjQtYWY2NzI3NzkzY2I3P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--fdd82be14d183f831d32fb5a6e2a373ee7daa64a" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt6WXpFM01tRXdOeTAxWm1RekxUUmtZbUl0T1dRMk5DMWhaalkzTWpjM09UTmpZamNHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--0f4ef47074c895be8529d34cc84e4e62573824be/P6180193.jpg" filename="P6180193.jpg" filesize="138415" width="1050" height="779" previewable="true" presentation="gallery"></action-text-attachment></div><div>Do you have a business problem that can be solved by utilizing data and AI? Or are you a data entrepeneur interested in joining AI Roots network? Please let us know!</div>

Supporting data talent

<div>By<strong> </strong><a href="https://www.rootsof.ai/blog/author/minna-k%C3%A4rh%C3%A4"><strong>Minna Kärhä</strong></a><strong><br><br></strong>When someone points that “data is a valuable business asset”, usually everyone gives a nod of approval. But not many have the tools to prove and show the actual value of data. What is the value of our customer data base? What about the product data we have in our company?<br><br></div><div>As data itself is “just” a collection of values: numbers, characters etc. it is not even possible, I dare to say, to define value for it alone. There is usually a cost linked directly to the data: cost of collecting or acquiring it, cost of processing it and cost of storing it, even a cost of sharing it. But linking data to revenue or other type of monetary value is challenging, because it is not a product that is being sold on it’s own (for most).<br><br></div><div>It is common that the terms “data” and “information” are sometimes mixed. Information means processed data that has a meaning to human. Data is the ingredient for information. When thinking about information it starts to be a bit easier to think of the value as well. “<em>If I have this information, I am able to do xxx</em>”. And doing the xxx eventually creates business value, i.e. cost savings or revenue.</div><div>In this article I will share  some learnings and a few practical examples from Finnair in calculating the value of data. Although COVID19 has changed the environment for air traffic significantly, and at the moment the challenge is not to manage the high volumes, it is still important to understand the 360 operational effect when flights are cancelled or changed – so this information is still useful. </div><div> <action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNjM2YjIwNGUtN2I1MS00ZTE3LWJhMmEtMjZmNjY0ZWM1ZWZkP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--afbfa7427dcb284b05c9930e573a2dffa99cf65f" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWsyTXpaaU1qQTBaUzAzWWpVeExUUmxNVGN0WW1FeVlTMHlObVkyTmpSbFl6VmxabVFHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--42ef4f1fbaef7332d076af5bfefbbd30d1de7f4d/IMG_9540.jpg" filename="IMG_9540.jpg" filesize="177788" width="1050" height="1575" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><div>Practical examples from Finnair in how to generate value from data:<br><br></div><div><strong>1. Improving the marketing effectiveness through better targeting<br></strong><br></div><div>Being able to target marketing campaign to right audience we used data of our customers earlier interactions with us. We modelled this data and created recommendations: “<em>this person would be most likely to be interested in this type of product</em>”. This recommendation was then the information we got. By using that information in our marketing campaign, we got great results. The results were measured with A/B testing: one target group was formed using the traditional method and another one using this new information. The second group was significantly smaller, so the email campaign costs were also lower. However, the conversion rate (people buying the product) was significantly higher for the second group. The value of the data used and information gained from it was the cost saving of marketing campaign + better revenue gained from the product sales<em>.</em></div><div><br><strong>2. Growing sales through high quality customer care<br></strong><br></div><div>After the successful campaign, we did not stop there but shared the same information also to our customer contact center. Now they are also able to use the same information when in contact with the customers. The value is not directly just upselling products for customers to make their travel experience even nicer, it is also<strong> increasing the overall customer satisfaction which brings back not only the same customers again, but also new customers when recommended</strong>. And customers like to spend their money on service providers that add value for them.</div><div> </div><div><strong>3. Optimizing ground operations to improve customer experience<br></strong><br></div><div>Talking about customer satisfaction: Weather is one major component what comes to flight delays, especially in Helsinki airport, which is the home hub of Finnair. Flights are tightly connected in many ways: transferring passengers, crew, cargo and aircrafts all have connections that cause extra hassle for customers and work for ground crew and operations if missed. By bringing together weather data (past and forecast), runway capacity data and historical data of flights, and modelling that we were able to create information about the likelihood of a flight being delayed. By using that information in the Operations Control Centre daily work, it is now possible to take actions earlier to manage the disruption situations with more flexibility and secure customer satisfaction by reduce the hassle. Value in this case comes from f.e. <strong>being able to focus ground operations to the correct place and being prepared before f.e. the customers arrive – saved time.<br><br></strong><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNzg2NjFjMWEtNDIzYi00NDkzLWE3M2YtYjAzNjE2NjQ5ZWRhP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--4bd275fb71321c5c20dae206715c1a883c6eb32f" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWszT0RZMk1XTXhZUzAwTWpOaUxUUTBPVE10WVRjelppMWlNRE0yTVRZMk5EbGxaR0VHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--2f5917f1f25b00eefdee6adbb0fb466f78ca304e/hans-eiskonen-Lojbz27NQ1w-unsplash.jpg" filename="hans-eiskonen-Lojbz27NQ1w-unsplash.jpg" filesize="216441" width="1050" height="678" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><div>Based on these few examples I would define the formula to calculate value of data being as simple as:</div><div><strong>(RoC + SC + CV) - (CoRoDAM + CoAS2D)<br></strong><br></div><ul><li><strong>CoRoDAM</strong> = Cost of resources and effort put to Data Asset management</li><li><strong>CoAS2D</strong> = Cost of acquiring, storing and distributing the data</li><li><strong>RoS</strong> = Revenue gained</li><li><strong>SC</strong> = Cost Saved (also time saved if relevant)</li><li><strong>CV</strong> = Customer Value</li></ul><div><br>Like investing your money, it is also important to carefully consider where you invest your data to get the best ROI. Therefore starting every data &amp; analytics initiative with the business value potential is critical as that is the only way to validate if it makes sense to use the time &amp; effort to build the solution. In addition, continuous monitoring of the value generation is important, as is managing your investment portfolio, sometimes you need to make the decision to move your money to a new portfolio that creates more value. <br><br></div><div>We have developed in collaboration with AI Roots new tools for making sure that the business benefits are analyzed and estimated when designing new data solutions. Data Design method and the tailored canvas template helps us to integrate that with the understanding of the core customer needs and the evaluation of solution feasibility, i.e. how much effort and resources are needed to maintain, develop and use it. Check out our article explaining the roots of the <a href="https://www.rootsof.ai/blog/data-design">Data Design</a> method.</div><div><br>Feel free to comment or contact us to discuss more about the topic. It would be interesting to find out what is your formula for calculating the value of your data.</div>

How to measure the value of data?

<div>By<strong> </strong><a href="https://www.rootsof.ai/blog/author/jukka-reitmaa-ari-rantanen-juha-karvanen-santtu-tikka"><strong>Jukka Reitmaa, Ari Rantanen, Juha Karvanen, Santtu Tikka</strong></a><strong><br><br><br></strong>Tekoäly on edennyt viime vuosina valtavin harppauksin. Suurimmat edistysaskeleet on otettu koneoppimisen alueella, jossa suuria tietomassoja analysoivat syvät neuroverkot ovat oppineet tunnistamaan tekstiä, puhetta ja kuvia tasolla, joka monissa tapauksissa ylittää ihmisen kyvyt. Tämä kehitys on mahdollistanut esim. luonnollisen kielen ymmärtämiseen ja robottien autonomiseen toimintaan liittyviä sovelluksia, joita aiemmin pidettiin mahdottomina.<br><br></div><div>Syväoppimisen hienosta edistyksestä huolimatta datatieteilijän saa edelleen hiljennettyä kysymällä häneltä päätöksenteon tueksi näkemystä datasta tunnistettujen yhteyksien, esim. kohonneen myynnin ja erilaisten markkinointitoimenpiteiden, tai potilaiden hoitopolkujen ja terveystilanteen kehittymisen välisistä syy-seuraussuhteista. Ensimmäisenä datatieteilijä muistuttaa, että tilastollisen päättelyn keinoin tuotetut yleistykset perustuvat datassa havaittuihin korrelaatiosuhteisiin, joita ei pidä tulkita kausaalisiksi syy-seuraus -suhteiksi: Pelkkä havainto myynnin ja markkinointibudjetin koon kasvamisesta yhtä aikaa ei välttämättä tarkoita, että tehokas markkinointi on lisännyt myyntiä. Voi myös olla, että kasvanut myynti on mahdollistanut suuremmat panostukset markkinointiin.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMDE1OTdmYmQtYTEwNi00Y2QxLTlkODAtMDZmNmViYTdkZDRiP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--f05570c2b295852a37001d00521e9064a35acc51" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt3TVRVNU4yWmlaQzFoTVRBMkxUUmpaREV0T1dRNE1DMHdObVkyWldKaE4yUmtOR0lHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--ecf4a0ed0d437a902c9b0e203a073aae74276bb8/10%20Data%20quality.jpg" filename="10 Data quality.jpg" filesize="102556" width="1050" height="760" previewable="true" presentation="gallery"></action-text-attachment><br><br></div><div>Toisena vastauksena syy-seuraussuhteista esitettyyn kysymykseen datatieteilijä tarjoaa kontrolloitua koetta, datatieteilijän kielellä A/B-testiä. A/B-testissä toimenpide, esim. markkinoinnin sähköposti, kohdistetaan toiseen kahdesta mahdollisimman identtisestä kohderyhmästä, toisen ryhmän jäädessä ilman sähköpostia. Sitten tutkitaan, kuinka kohderyhmien välinen ostokäyttäytyminen eroaa sähköpostin lähettämisen jälkeen, ja tulkitaan erojen johtuvan sähköpostista – tai sattumasta.<br><br></div><div>A/B-testin järjestäminen onkin voimakas työkalu kausaalisuhteiden testaamiseksi ja havaitsemiseksi. Niin voimakas, ettei data- ja tilastotieteilijöiden yhteisö pitkään aikaan muita menetelmiä tunnustanutkaan. Aina A/B-testin järjestäminen ei kuitenkaan ole helppoa, tai edes mahdollista. Ihmisiä ei voi ehdoin tahdoin alistaa myrkyllisille pienhiukkasille, ilmaston puolikasta ei ole mahdollista vapauttaa ylimääräisestä hiilidioksidista, eikä yrityksen asiakkaista ole helppoa rajata kahta samankaltaista kohderyhmää, joista puolet näkevät yleisesti esitettävän TV-mainoksen ja toinen puoli ei.<br><br></div><div>Eikä tarvitsekaan: tekoälyn seuraava suuri hyppäys, kausaalimallinnus, on valmis siirtymään tutkimuslaboratorioista käytännön sovelluksiin.</div><div> </div><h1>Kausaalimallinnus kypsä siirtymään laboratorioista käytäntöön</h1><div>Kausaalimallinnuksen suuri lupaus soveltajille ja asiakkaille on päätöksenteon tuki, joka ei pysähdy mallintamaan menneisyyttä vaan antaa luotettavia vastauksia toimenpiteiden vaikutuksista. Professori Juha Karvanen, yksi alan suomalaisista pioneereista kuvailee kausaalimallinnuksen hyötyjä verrattuna perinteiseen tilastolliseen päättelyyn seuraavasti: “Päätöksenteossa on kyse toimenpiteiden valinnasta. Jotta voisimme tehdä hyviä päätöksiä, meidän tulee arvioida erilaisten toimintavaihtoehtojen odotettuja seurauksia. Tähän tarvitsemme kausaalimallinnusta. Mustina laatikoina näyttäytyvät ennustemallit eivät herätä luottamusta päätöksenteossa -- eivätkä välttämättä anna oikeita vastauksia. Kausaalipäättelyssä oletukset tehdään näkyviksi ja päättelyn kulku oletuksista tuloksiin on toistettavissa ja dokumentoitavissa.”<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvYzYwOTE0MTItMTFkMS00MTYyLTk1NDYtNDg2MTkzMmZhMDZjP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--7136a8ca13393bc14f250179a333c18ca35992ed" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxqTmpBNU1UUXhNaTB4TVdReExUUXhOakl0T1RVME5pMDBPRFl4T1RNeVptRXdObU1HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--bec7eed669f5ae5bbfda4d84ca83e35a7d31c4e0/prosessinoptimointi2-unsplash.jpg" filename="prosessinoptimointi2-unsplash.jpg" filesize="190373" width="1050" height="722" previewable="true" presentation="gallery"></action-text-attachment></div><div><br>Tutkijatohtori Santtu Tikka väitteli kausaalimallinnuksesta vuonna 2018. Santtu on kehittänyt ja implementoinut monia kausaalimallinnuksessa tarvittavia algoritmeja. Santun algoritmitoteutuksiin viitataan myös kausaalimallinnuksen suuren yleisön tietoisuuteen tuoneessa Judea Pearlin kirjassa ”The Book of Why” (ilmestynyt myös suomeksi nimellä "Miksi - syyn ja seurauksen tiede"). “Book of Why” onkin hyvä, melko yleistajuinen johdanto kausaalimalleihin.   <br><br></div><div>Käytännön esimerkkinä kausaalimallinnuksen voimasta Juha ja Santtu nostavat esiin tuoreen tutkimusartikkelinsa, jossa on selvitetty suolan käyttötottumusten kausaalivaikutusta verenpaineeseen. Ainutlaatuista tässä tutkimuksessa on se, että syy-seurausketjun palaset rakennetaan yhdistämällä tietoa kyselytutkimuksesta ja 34 kliinisen kokeen meta-analyysista. Oikeaa tapaa tietojen yhdistämiseen ei tarvitse yrittää keksiä itse, vaan se saadaan suoraan kausaalimallinnuksen tuloksena. Tulosten mukaan korkeasta verenpaineesta kärsivä henkilö voi laskea verenpainettaan keskimäärin kolme yksikköä lopettamalla pöytäsuolan käytön ja suolan lisäämisen ruoanvalmistuksessa. <br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvZTA3YjBjZWYtMTJiMC00MjE1LTgwYjItMTdkZmFhY2E2ZGU1P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--7e4262e0eec017b82512960e562e8b01e3ba50a9" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxsTURkaU1HTmxaaTB4TW1Jd0xUUXlNVFV0T0RCaU1pMHhOMlJtWVdGallUWmtaVFVHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--6395be44eb38f18d1162aada7f58ae83809a48d4/Picture%20by%20National-Cancer-Institute%20from%20Unsplash.jpg" filename="Picture by National-Cancer-Institute from Unsplash.jpg" filesize="86975" width="1050" height="700" previewable="true" presentation="gallery"></action-text-attachment></div><div><br>Kausaalimallinnuksen nykytilanteen Juha ja Santtu kiteyttävät seuraavasti: “Kausaalimallien teoria on nyt edennyt niin pitkälle, että kehitetyt menetelmät voidaan ottaa käyttöön käytännön sovelluksissa. Tätä varten datatieteilijät tarvitsevat uudenlaista ajattelutapaa ja sopivia työvälineitä. Päätöksentekijälle kausaalimallinnus tarkoittaa mahdollisuutta entistä parempiin päätöksiin, jotka perustuvat enemmän tietoon ja vähemmän arvauksiin.” <br><br></div><div>Millaisia käytännön ongelmia kausaalimallinnuksen avulla voidaan sitten nykyisin ratkaista? Jukka Reitmaa, kausaalimallinnuksen tuotteistamiseen keskittyvän Explicans-yrityksen perustaja jatkaa: "Kausaalimallinnuksessa ymmärretään, ettei tietämys asu vain datassa, vaan datan ja asiantuntijatiedon tulee toimia yhdessä. Tämä on eduksi monimutkaisissa päätöksentekotilanteissa joissa tarvitaan useita eriluonteisia tietolähteitä: kokemuksellista tietoa, parhaita asiantuntija-arvioita, kerättyä dataa. Runsaimmin kausaalimallinnusta on hyödynnetty lääketieteellisessä tutkimuksessa, koska siellä vaikuttavuus on keskiössä. Myös investointipäätökset, liiketoimintaprosessien suunnittelu tai vaikkapa markkinointiviestinnän optimointi ovat pohjimmiltaan syy-seurausongelmia ja ratkeavat toden teolla vasta kausaalimallinnuksen avulla. <br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMWEyZmU2YTUtNjU1OS00ZGU5LTk0YjgtN2Q0NjVlNDU0M2E0P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--a9af186fb4bfcdcf6464fe3132196fbda82506b7" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt4WVRKbVpUWmhOUzAyTlRVNUxUUmtaVGt0T1RSaU9DMDNaRFEyTldVME5UUXpZVFFHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--4bd7e53ff04c6d4cdcc2f1be5bcf1f26b72fa90b/kausaalimallinnus-1.jpg" filename="kausaalimallinnus-1.jpg" filesize="433616" width="1518" height="797" previewable="true" presentation="gallery"></action-text-attachment><br><br></div><div>“Ilman kausaalimallinnusta tämän tyyppisten ongelmien ratkaisu jää väistämättä mutupohjaiseksi ja voi pahimmassa tapauksessa johtaa vääriin toimenpiteisiin”, Jukka Reitmaa toteaa. ”Explicansin kehittämä teknologia mahdollistaa käytännön syy-seurausongelmien analysoinnin ja luotettavien vastausten löytämisen.”</div><div> </div><h1>Pilotit käynnistyvät</h1><div>AI Roots ja Explicans kartoittavat parhaillaan sopivia pilottikohteita Explicansin kausaalimallinnusmenetelmien ja -prosessin validoimiseksi liiketoimintakumppaneiden kanssa. Asiakkailta saatu vastaanotto on tähän mennessä ollut erittäin innostunutta. “Datalähtöisen päätöksenteon tuen tyypillinen haaste on asiantuntijoiden ja päättäjien hiljaisen tiedon systemaattinen yhdistäminen yrityksen keräämien tietomassojen sisältämään informaatioon. Mahdollisuus validoida datalla päivittäistä päätöksentekoa ohjaavan hiljaisen tiedon sisältäviä oletuksia kriittisten prosessien syy-seuraussuhteista ymmärrettävästi kiinnostaa yrityksiä ja organisaatioita yli toimialarajojen. Aidot vastaukset kysymyksiin ‘Miksi havaitsemamme ilmiö tapahtui?’ sekä ‘Mitä tapahtuu, jos menettelemme jatkossa toisin’ ovat datalähtöisen päätöksenteon tavoitteita, joiden saavuttamiseksi kausaalimallinnus tarjoaa työkaluja, jotka tulevat uudistamaan datalähtöisen päätöksenteon kentän”, toteaa Ari Rantanen, yksi AI Rootsin perustajista. <br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvOWZhZTI1MjYtNDllOS00NjhlLTlhNGYtODRjNjQzNzkwMThiP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--a54d3f92b96f2f6cb85d69117cec7564d9164b5a" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs1Wm1GbE1qVXlOaTAwT1dVNUxUUTJPR1V0T1dFMFppMDROR00yTkRNM09UQXhPR0lHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--6fd81886dfb82f157eddd8263079e146e09aed0d/kausaalimallinnus-2.jpg" filename="kausaalimallinnus-2.jpg" filesize="127984" width="1157" height="649" previewable="true" presentation="gallery"></action-text-attachment><br><br></div><div>“Esim. monikanavaisen markkinoinnin kenttään kausaalimallinnus tarjoaa kauan kaivattuja työkaluja yleiseen tilanteeseen, jossa kampanja sisältää eri kanavissa tapahtuvia kohdennettuja ja kohdentamattomia toimenpiteitä, jotka tähtäävät mahdollisesti viivellä tapahtuvaan konversioon. Asiakaskokemuksen johtamiseen kausaalimallinnus tarjoaa samoin työkaluja, esim. tuotteen ominaisuuksien ja hinnan muutosten vaikutuksen aiempaa selkeästi realistisempaan arviointiin. Myös tuoteportfolion optimointi, valmistavan teollisuuden prosessien harvinaisten tapahtumien mallinnus tai vaikkapa työntekijöiden urapolun ymmärtäminen ovat käytännön ongelmia, jotka hyötyvät kausaalimallinnuksesta.”<br><br></div><div>”Hyötyäkseen kausaalimallinnuksesta yritys tarvitsee vain tunnistetun, syy-seuraussuhteiden ymmärtämistä vaativan liiketoimintaongelman, näkemyksen liiketoimintaprosessin kausaalisuhteista sekä dataa prosessin eri vaiheista”, Jukka Reitmaa sanoo ja jatkaa: ”Kannattaa ottaa rohkeasti yhteyttä, kykenemme nopeasti arvioimaan kuinka kausaalimallinnus kykenee auttamaan liiketoimintaongelmasi ratkaisemisessa.” Epäreilua kilpailuetua uuden teknologian varhaisille soveltajille jaetaan nyt.<br><br></div><h1>Kiinnostuitko kuulemaan lisää? </h1><div>Ole yhteydessä:<br><a href="mailto:ari@rootsof.ai">ari.rantanen@rootsof.ai</a><br><a href="mailto:jukka.reitmaa@explicans.fi">jukka.reitmaa@explicans.fi</a><br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvZDI4ZDExYTctYTBiYi00OGZkLTg3NzYtYjY1ODRlZWY5OTdiP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--465c75aa7772312257e298d806574930fa6ae515" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxrTWpoa01URmhOeTFoTUdKaUxUUTRabVF0T0RjM05pMWlOalU0TkdWbFpqazVOMklHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--d3bab1f0e73c6940c84ee82184b3e49e1c949e16/kausaalimallinnus-3.jpg" filename="kausaalimallinnus-3.jpg" filesize="217217" width="1471" height="738" previewable="true" presentation="gallery"></action-text-attachment></div>

Syyt ja seuraukset esiin kausaalimallinnuksella

<div>By <a href="https://www.rootsof.ai/blog/author/teemu-birkstedt">Teemu Birkstedt</a></div><div><br>Suomi on poikkeuksellinen ja hieno maa monella tapaa. Myös teknologian kehittämisen alueella. Meillä on paljon teknistä huippuosaamista dataan, analytiikkaan ja koneoppimiseen. Edelläkävijäyrityksemme Nokiasta Supercelliin ja vaikka Woltiin ovat varmistaneet että osaamisemme on ajan hermolla, myös yliopistomme tuottavat koko ajan lisää ja paremmin soveltuvaa osaamista data-markkinaan.<br><br>Toisaalta Suomi on tosi pieni maa ja valitettavasti jopa takapajula datan hyödyntämisessä oikeasti ja skaalassa. Yhden ruotsalaisen ison retailerin käyttämä pilvipalveluiden kulutus on yhtä suurta kuin koko Suomen markkinan kokonaiskäyttö. Ruotsikaan ei ole edelläkävijä digitaalisten ja algoritmisten palveluiden kanssa.<br><br>Oma tavoitteeni on tehdä merkityksellistä duunia paikasta ja ajasta riippumatta. En ole ainoa vaan tämä on suurempi virtaus työmarkkinassa, perinteiset työurat ovat vähenemään päin. Työelämän tulevaisuuden ymmärtäjä, hyvä kaverini Iikka (ja yksi AI Rootsin perustajavoimista) sanoi pari vuotta sitten tosi fiksusti: ”isäni oli koko työuransa yhden firman palveluksessa, minulle tulee varmaan kuutisen yritystä jolle työskentelen työurallani ja lapseni tulee todennäköisesti tekemään töitä kuudessa eri työpaikassa yhtä aikaa”.<br><br>Edellä olevat ajatukset ovat johtaneet yrityksen nimeltä AI Roots Oy kehittymiseen. Saatan kuulosta jopa idealistilta mutta otan sen riskin 45+ iässä.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvODM3ZTM1ZWYtNjMyNi00ZmFmLWI0OWUtZjdkOTJhZjgyOTZmP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--3753aa4698711c6c2c55fdc6adb0b5bea5d006af" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs0TXpkbE16VmxaaTAyTXpJMkxUUm1ZV1l0WWpRNVpTMW1OMlE1TW1GbU9ESTVObVlHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--5d797dbe923385af273e6e472e3c5997bdd4c349/AiRoots-92-3.jpg" filename="AiRoots-92-3.jpg" filesize="110444" width="1050" height="814" previewable="true" presentation="gallery"></action-text-attachment><br> <br><strong>Tehtävämme data-alan osaajamarkkinan uudistajana</strong><br> <br>Uskomme, että data-alan huippuosaajien verkostona, yhteisönä ja osaajia yrityksille välittävänä toimijana meillä on mahdollisuus myös uudistaa ja viedä alaa eteenpäin. Olemme tunnistaneet aktiivisen vuoropuhelun kautta kolme tärkeintä tehtävää:<br><br></div><ol><li><strong>Luomme vuoropuhelun data-alan pienyrittäjien, yritysten ja tutkimusyhteisön välille.</strong> Uskomme, että tulevaisuuden menestykset takaa kyky uudistua ja oppia alan parhailta osaajilta ristiin. Meidän tehtävänämme on toimia pienyrittäjiä, yrityksiä ja tutkimusyhteisöä yhdistävänä linkkinä. Tässä on yhteen hiileen puhaltavalla Suomella aivan uniikki mahdollisuus erottua.<br><br></li><li><strong>Mahdollistamme osaamisen liikkuvuuden ja luomme osaamiselle parhaita paikkoja toimia.</strong> Meidän tehtävänämme on poistaa kitkaa työmarkkinasta ja luoda uusia tapoja kiihdyttää alan kehitystä Suomessa. <br><br></li><li><strong>Edustamme data-alan huippuosaajien yhteisönä alan korkeinta tekemisen laadun standardia.</strong> Tehtävänämme on tarjota parasta tarpeisiin soveltuvaa osaamista kumppaniasiakkaallemme ja varmistaa, että yhteistyömme tuottaa odotetun tuloksen. Huippukokeneet partnerit kuratoivat tekemisen laadun.</li></ol><div><br>Yksittäiset esimerkit kertovat parhaiten mitä teemme ja mihin tähtäämme paljon isommassa skaalassa tulevaisuudessa:</div><div><br></div><ul><li>Autamme data muutosta aloittelevaa yritystä ensimmäisissä datan hyödyntämisen askeleissa tarjoamalla konsultoivaa full stack-osaajaa joka osaa toteuttaa dataputkia, visualisoida niitä ja viedä päätöksenteon tueksi<br><br></li><li>Tuotamme AI-tuotteita kehittäville huipputaloille, kuten Groke Technologies teknologiaosaamista. Se voi olla osaavia algoritmikehittäjiä tuomaan suuntaa ja vauhtia tai arkkitehtejä (”rakennusmestareita”) valvomaan tekemisen laatua.<br><br></li><li>Muotoilemme asiakkaillemme, kuten teimme Baronalle, konkreettisen datastrategian, joka ei jää paperille, vaan joka jalkautuu aidoksi ja käytännönläheiseksi yrityksen kilpailukykyä kehittäväksi tekemiseksi ja tuomme osaajia sen toimeenpanoon.<br><br></li><li>Kehitämme asiakasarvon hallitsemiseen ja <a href="https://www.rootsof.ai/blog/kausaalimallinnus">kausaalimallinnukseen</a> helposti käytettäviä ratkaisuja yhteistyössä akatemian kanssa joita kehittyneemmät asiakkaamme jo käyttävät<br><br></li><li>Koulutamme ja kehitämme asiakkaillemme uusia dataratkaisujen suunnittelun ja kehittämisen työvälineitä ja käytäntöjä, kuten <a href="https://www.rootsof.ai/blog/data-design">datamuotoilun perusteita</a></li></ul><div><br>Konseptimme erottuu ja poistamme jo nyt kitkaa markkinasta, tämä saa innostumaan:<br><br></div><ul><li>Finnairin data- analytiikkayksikön vetäjä on ollut AI Rootsin mukana (osa-aikaisesti). Hän on tuomassa huippuosaamisensa muun muassa datan hallintamallien (Data Governance) ja datan muotoilumenetelmien (Data Design) kehittämiseen. On eri asia kun merkittävää muutosta itse johtava henkilö auttaa kumppania välttämään sudenkuopat ja löytämään ne parhaat käytännöt<br><br></li><li>Saamme jatkuvasti innostavaa palautetta kyvystämme tukea ja auttaa yrittäjiä työllistymään mielenkiintoisiin hankkeisiin ja rauhoittaa työaikaa asiakashankinnan sijaan itse tekemiseen. Erityisesti korona-aikana tämä on tuonut myös monelle turvaa ja mahdollistanut yrittäjäpolulla pysymistä. Verkostomme yrittäjien kokemuksista lisää <a href="https://www.rootsof.ai/fi/verkosto"><strong>täältä</strong></a>.<br><br></li><li>Amazon-verkkokaupan kysynnän ennustamisen alueen entinen lead data scientist on ryhmässämme tuomassa osaamisensa suomalaisten yritysten käyttöön. Samoin johtavan pelifirman entinen AI arkkitehti.</li></ul><div><br>AI Roots on koronavuodesta huolimatta käynnistänyt toimensa ripeästi ja  starttivuotensa aikana tehnyt yhteistyötä yli 20 yrityksen kanssa. AI Rootsilla ja konseptilla on suuri potentiaali myös ulkomailla. Asiakaskumppaneita on löytynyt jo tässä vaiheessa mm. Japanista, Hollannista ja Ruotsista.<br><br></div><div>Kiinnostavia keskustelunavauksia ja uusia toimenkuvia tulee aukeamaan lähiviikkoina, kannattaa seurata.</div>

Työn tulevaisuutta tekemässä

<div>By <a href="https://www.rootsof.ai/blog/author/akseli-sepp%C3%A4l%C3%A4">Akseli Seppälä</a></div><div><br></div><blockquote><em>This blog post is based on a AI governance and AI ethics </em><a href="http://urn.fi/URN:NBN:fi-fe202104099940"><em>study</em></a><em> conducted by the author. We will share here the most recent AI governance related research and the most interesting findings of the study. We are also committed helping your organization build data governance practices.</em> </blockquote><div><br><strong>How to govern your AI</strong><br><br></div><div>AI governance is a topic that is still abstract for many organisations and business stakeholders. Some might have heard of it when reading about biased algorithms, ML development practices, or data governance. Indeed, AI governance can be seen as an extension of data governance. Many of the data governance practices can be extended to include AI/ML related processes, such as review of use cases, as well as data collection and processing and information security and data protection practices.</div><div> </div><div><strong>Why is AI governance relevant?</strong><br><br></div><div>Few of the main reasons why organisations should consider implementing AI governance are brand and reputational risks, corporate responsibility, and future regulation. The media is filled with grim predictions of out-of-control robots, biased decision-making, unfair treatment of minority groups, privacy violations, adversarial attacks, and challenges to human rights caused by ungoverned algorithms. Moreover, there have been instances of biased algorithmic decision-making and unfairness, mostly related to discrimination of<strong> </strong><a href="https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm"><strong>minority groups</strong></a> or <a href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G"><strong>gender</strong></a>. These may cause unrepairable damage to organisations brand image and reputation. Moreover, these days it is common for organisations to have responsibility on their agenda or have responsibility objectives incorporated in their overall strategy and values. Thus, it makes sense to have control over their AI systems to avoid the costly mistakes of uncontrollable AI. Lastly, future regulation, such as the European Commission’s recent proposal for <a href="https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai"><strong>AI regulation</strong></a>, will eventually make AI governance from a “nice-to-have” subject to “must-have” operations for many organisations using AI.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNzcwYzExNTItYzY0Ni00NTQ4LWIwODQtNjMxYTMwYjNlMzUyP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--b587a0e75e470fa9fa34ab4496f1485e485b97e2" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWszTnpCak1URTFNaTFqTmpRMkxUUTFORGd0WWpBNE5DMDJNekZoTXpCaU0yVXpOVElHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--c20b99891a4b9a7eb43fc58506d3a5ec161d6974/marvin-meyer-SYTO3xs06fU-unsplash-1.jpg" filename="marvin-meyer-SYTO3xs06fU-unsplash-1.jpg" filesize="199333" width="1050" height="700" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><div><strong>What is AI governance?</strong><br><br></div><div>AI governance is a topic closely related to AI ethics. A common way to express the challenges of AI is to consolidate them into a more understandable form of ethical principles, such as fairness, privacy, accountability, transparency, and explainability. Governmental bodies and international organisations, as well as private companies have published their own guidelines of ethical AI. These include the IEEE “Ethically Aligned Design” document and the European Commission’s AI HLEG “Ethics Guidelines for Trustworthy AI”. The guidelines generally address the ethical challenges by proposing various ways to increase fairness, transparency, or accountability of AI systems. The European Commission’s AI HLEG suggests that the methods used to implement ethical principles should encompass the AI’s entire life cycle and include both non-technical and technical methods. The non-technical methods include certificates, standardization (such as ISO Standards and IEEE P7000), education and awareness, multi-stakeholder inclusion, team diversity, and governance frameworks, whereas the technical methods consist of rigorous model validation and testing as well as continuous monitoring.<br><br></div><div><strong>Fairness</strong> is closely related to the prevention of bias and non-discrimination. AI systems are subject to various distortions, which may lead to unfair decisions. Every ML model is trained and evaluated using data, which can reflect existing biases in religion, gender, or race. The dataset’s characteristics will therefore influence a model’s behaviour and reinforce existing discriminations. Thus, the use of high-quality and representative datasets is recommended to address the issue of “garbage in, garbage out”. Furthermore, the development and design phase of AI systems may also suffer from bias, and thus, it is suggested to employ diverse development teams.<br><br></div><div><strong>Accountability </strong>refers to being responsible for the AI system, its potential impacts and behaviour. AI’s complex nature can place further distance between the results of an action and the actor who caused it, raising the question about who should be held accountable and under what circumstances. This is also referred to as the “responsibility gap”, whereby it is unclear who is ultimately responsible. The accountability and responsibility should be allocated between those who deploy, develop, design, and use the AI systems, as we cannot hold the technology itself responsible for its actions. Organisations can create internal review boards to oversee the use and development of AI. The review boards can use impact assessments to document, assess, identify, and minimize the AI system’s potential negative impacts, and use it to decide whether an AI system should be deployed or developed in the first place. It also is suggested that AI systems should be able to be independently audited by internal or external auditors. Moreover, accountability is strongly connected to human control. AI systems must be designed and implemented so that humans can retain control of the system or intervene in its actions. Furthermore, organisations must provide an option for individuals to opt out of automated decisions related to them.<br><br></div><div><strong>Transparency </strong>can be understood as the transparency of the organisations using and developing the AI systems, or as the transparency of the system itself. The former refers to the understanding of by whom, why and what decisions were made during the AI design and development, whereas the latter refers to the understanding of how the system reaches a decision and how it is designed. The greatest challenge of AI governance is the complexity and opacity of the technology itself. It is possible to increase transparency by releasing the algorithm’s source code, or providing information about the variables affecting the decision-making. Moreover, organisations can also consider minimizing the use of black boxes or abandon them altogether. Transparency can also be achieved by providing explanations of the processes that lead to the decisions. Explanations can be described as either global or local ones. Global explanations refer to the general rules and overall behaviour of the model (i.e., how the system generally reaches a decision, what drives the system’s decisions, which factors are the most important ones, etc.), whereas local explanations look at the specific decision (i.e., how it reached a particular outcome).<br><br></div><div>In addition, it is suggested to implement ethical principles into the AI system’s design, generally referred to as “X by Design”, such as Privacy by Design and Security by Design.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNWI2MDYxNDQtMjMyMi00OWIyLWExNjQtODk1ODk5MmIwY2Q5P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--4dbb89fc4eb14ce57baab6ccf232fa877f6b4c94" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWsxWWpZd05qRTBOQzB5TXpJeUxUUTVZakl0WVRFMk5DMDRPVFU0T1RreVlqQmpaRGtHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--0891832db281d2c9825d349b092512dc46b227ec/analysis-business-businesswoman-chart-colleagues-communication-1571535-pxhere.com.jpg" filename="analysis-business-businesswoman-chart-colleagues-communication-1571535-pxhere.com.jpg" filesize="104208" width="1050" height="629" previewable="true" presentation="gallery"></action-text-attachment></div><div> </div><div><strong>How are organisations conducting AI governance today?</strong><br><br></div><div>According to a <a href="http://urn.fi/URN:NBN:fi-fe202104099940"><strong>study</strong></a> conducted by the author of this blog post, organisations are using various AI and data governance practices to govern their AI, such as review boards and impact assessments for AI use cases. In addition, organisations have in place many AI design and development practices as well as education and stakeholder communication activities to support AI governance.<br><br></div><div>The study was conducted as a part of a research project called Artificial Intelligence Governance &amp; Auditing (<a href="https://ai-governance.eu/"><strong>AIGA</strong></a>), which explores how to execute responsible AI in practice. The AIGA project is coordinated by the University of Turku and funded by Business Finland. Overall, 13 expert interviews were conducted with 12 Finnish organisations. The organisations either created AI systems for clients, used AI systems as a sales product or in organisation’s own business activities, or provided AI-related services. Eight interviewed organisations were operating in the private sector and four in public. Moreover, the participants were from a range of managerial positions, from lead data scientists to CEOs.<br><br></div><div>In the figure below, you can find the key themes of the study.<br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvM2YzODI5YjQtZmI2My00YzY5LWJkMmYtZTFiZTBmNmIyOWYzP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--0f0b4f27d5ae9f8851d647c5e1fb3b9800bd14e0" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt6WmpNNE1qbGlOQzFtWWpZekxUUmpOamt0WW1ReVppMWxNV0psTUdZMllqSTVaak1HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--198eaf7c370633d9f54b4ddc0cd77ec7f4d47dc9/data_structure_text.jpg" filename="data_structure_text.jpg" filesize="42802" width="603" height="603" presentation="gallery"></action-text-attachment><br>Organisations built their AI governance practices on top of data governance. Indeed, data governance functioned as a foundation for AI governance, and some of the practices covered both data and AI related use cases. Therefore, organisations should have a solid data governance base, which can be extended to include AI systems. </div><div> </div><div><strong>Our services in Data Governance</strong><br><br></div><div>AI Roots has successfully helped organisations to develop and implement data governance practices suitable for the company’s unique needs and business environment. We can support our customers for example by:<br><br></div><ul><li>developing data strategy and setting target state for data governance</li><li>creating guidelines for responsible and ethical use of data and AI</li><li>defining and onboarding data stewards and other data related new roles (i.e., people who are responsible for data flow and quality, data usage and understanding, and managing access to data)</li><li>designing data architecture to cover governance needs</li><li>aligning data governance with privacy policies</li><li>designing data management tools</li><li>supporting data catalogue requirement gathering and building a business case, evaluating tool options, and planning and supporting successful deployment</li><li>developing data quality management practices and tools</li><li>organizing data governance related trainings</li></ul><div><br>We at AI Roots are committed to assist companies to develop data governance practices all the way from technical data quality management to helping the business take ownership and govern the responsible use of data and AI in the organisation. Please, feel free to contact us in case you need help in this area.<br><br></div><div>Iiris | <a href="mailto:iiris@rootsof.ai">iiris@rootsof.ai</a> | +358 40 5188 207</div>

AI Governance

<div>Dataosaajien markkina on tällä hetkellä ylikuumentuneessa tilassa. Tilanne pakottaa yritykset miettimään uusia tapoja osaamisen katvealueiden  täyttämiseen. Datakehitykseltä haetaan joustavuutta eli kykyä skaalata tekemistä ylös ja alas tarpeiden muuttuessa. Osaajamarkkinan ylikuumentuminen näkyy monen yrityksen datatiimeissä lisääntyneenä paineena työntekijöiden pitoon ja sitouttamiseen. <br><br>Rekrytoinnin rinnalle tarvitaan kumppani, joka pystyy paikkaamaan yritykselle kriittisiä osaamisen aukkoja ja kehittämään siten myös yrityksen oman tiimin osaamista. Geneerisen konsultin sijaan haetaan persoonaa, täsmäosaamista ja yrittäjähenkistä vastuunkantajaa. Kymmenien toimeksiantojen kokemuksella ja yrittäjäverkostomme huippuosaajien äänellä voimme todeta, että tähän osaamisen skaalaamisen haasteeseen osaava ja motivoitunut data-alan yrittäjä on yksi parhaimmista ratkaisuista. Pienyrittäjän työllistäminen oman kehitystiimin tueksi on myös yrityksiltä vastuullinen teko. Me toimimme tässä osaamisen paikallistajana, yrittäjyyden mahdollistajana ja välittäjäaineena, yritysten ja verkostomme yrittäjien tukena.<br> </div><h1>Yrittäjyys data-alalla</h1><div>Koska kysyntä osaajista on kovaa, yrittäjyys on monelle data-alan huippuosaajalle yhä houkuttelevampi ja saavutettavampi vaihtoehto. Oman yrityksen kautta tulee vapaus valita asiakkuudet ja kiinnostavimmat ja omaa osaamista kehittävimmät hankkeet. Yrittäjyyden yleistyminen on haaste myös isoille konsulttitaloille, joiden palkkamalli jää auttamatta jälkeen siitä mitä yrittäjä voi omasta työstään suoraan tai meidän kaltaisen toimijan kautta laskuttaa. Saammekin tällä hetkellä yllättävän paljon yhteydenottoja osaajilta, jotka ovat joko kyllästyneet työhönsä sisäisessä roolissa tai ison konsulttitalon leivissä ja harkitsevat oman yrityksen perustamista.<br><br>Me olemme AI Rootsilla mahdollistamassa näitä yrittäjyystarinoita. Meidän kauttamme yrittäjät saavat tuekseen muista huippuosaajista yhteisön ja pystymme rakentamaan asiakkaille myös isompia DataOps/MLOps-tiimejä. Emme sido osaajiamme vaan vetovoima perustuu siihen, että kauttamme yrittäjät saavat kiinnostavimmat toimeksiannot ja asiakkuudet. Monelle myyntityö on ollut este yrittäjäksi ryhtymiselle ja yrittäjät saavat sen tuen ikään kuin ulkoistettuna palveluna meidän kauttamme ja voivat keskittyä itse tekemiseen.<br>Samalla autamme asiakkaitamme tunnistamaan datakehityksen ja datan hyödyntämisen kipupisteet ja varmistamme, että asiakkaamme saavat käyttöönsä tarpeisiinsa nähden juuri oikeat osaajat. Diili on reilu myös yrittäjälle, jonka nettoansio yrittäjänä tyypillisesti jopa kaksinkertaistuu perinteiseen konsultointityöhön verrattuna.<br> </div><h1>Kysyntää erilaisille osaajille</h1><div>Näemme asiakkaidemme keskuudessa selkeää muutosta siinä minkälaista dataosaamista yritykset tarvitsevat tuottaakseen tulosta ja missä tilanteessa data-osaajien markkina ylipäätään on.<br><br>Datatyön tekeminen monipuolistuu ja demokratisoituu. Yhä useampi, myös liiketoiminnassa, tekee datatyötä oman toimensa ohella, osallistuu datakehityksen hankkeisiin ja tekee yhteistyötä dataosaajien kanssa. Samanaikaisesti huippuosaamisen merkitys, esimerkiksi data-alustojen rakentamiseen tai uusimpien koneoppivien ratkaisujen tuotannollistamiseen korostuu entisestään.<br><br>Markkinassa näkyy nyt vahvasti kysynnän laajuntuminen hyvin erilaisiin osaajiin. Data scientist ei ole enää yksinäinen ihmeiden tekijä, vaan hän tarvitsee tuekseen liiketoiminnan omistajan, joka tuntee teknologiaa ja koneoppimisen menetelmiä sen verran, että tietää mikä on mahdollista ja pystyy ideoimaan sille uusia käyttökohteita. Tekoälykokeilut ja PoC:t pitää pystyä viemään tuotantoon, niitä pitää ylläpitää, monitoroida ja niiden suorituskyky  vaatii jatkuvaa optimointia. Tähän data scientist tarvitsee tuekseen arkkitehdin, datakehittäjän ja jonkinlaisen ML/dataOps-tiimin ja -käytännöt, jotta kehitystä voidaan edistää ketterästi ja laadukkaasti. Vastaavasti Power BI:n kantaiset itsepalveluun tähtäävät raportointiratkaisut parantavat datan saatavuutta ja demokratisoivat raporttien kehitystyötä, mutta tuovat uudenlaisia paineita datamallien ja -virtojen kehitykseen. Tarvitaan laadukas ja uudelleenkäytettävä datapohja, johon avataan näkyvyys esimerkiksi datakatalogin kautta ja jonka päälle voi turvallisin mielin lähteä toteuttamaan omia analyysinäkymiä. Kaiken tämän rakentamiseen tarvitaan hyvin laaja-alainen, skaalautuva ja ketterästi toimiva datatiimi. Kysyntä data-alalla yrittäjinä työskenteleville on siis hyvin laajaan osaamiseen keskittyvää. <br> </div><h1>Ole rohkeasti yhteyksissä!</h1><div>Suosittelemme olemaan rohkeasti yhteyksissä mikäli sinua kiinnostaa yrittäjän hyödyntäminen dataosaamisen skaalaamiseen, olet jo data-alan yrittäjä ja kaipaat toimeksiantoja tai verkoston tukea tai olet vasta haaveilemassa yrittäjäpolulle ryhtymisestä:<br><br><a href="mailto:ari@rootsof.ai">ari@rootsof.ai</a><br><a href="mailto:iiris@rootsof.ai">iiris@rootsof.ai</a><br> <br>Keskustelimme näistä aiheista myös edellisessä AI &amp; Business strategies-tapahtumassa:<br><br><a href="https://vimeo.com/625589029?embedded=true&amp;source=video_title&amp;owner=46609820">https://vimeo.com/625589029?embedded=true&amp;source=video_title&amp;owner=46609820</a></div>

Yrittäjyys data-alalla

<div>The ways of working are going through a shift, and freelancing has become an appealing alternative for increasingly many people. The benefits of self-employment are numerous and undisputed: According to an <a href="https://s29814.pcdn.co/wp-content/uploads/2019/06/MBO-SOI-2019.pdf"><strong>MBO study</strong></a>, 82 % of freelancers are happier working independently. The advantages of freelancing involve factors like an increased sense of freedom, earning potential, and decreased dependency on one company. Working for yourself can be described as the generation’s way of working. <a href="https://www.washingtonpost.com/world/2021/10/18/labor-great-resignation-global/"><strong>The Great Resignation</strong></a> refers to the phenomenon in which masses of employees left their jobs during Covid-19 pandemic. Now many are working as freelancers, and it seems like this development is here to stay.<br><br></div><div>In the data and software industries freelancing is a particularly interesting option for both employees and organizations. Companies constantly struggle to find the best talents to develop their data and machine learning solutions, and for employees this introduces new working possibilities. Freelancer networks help to match the talents and the companies. Change <a href="https://www.forbes.com/sites/jonyounger/2019/08/23/master-class-in-the-freelance-revolution-with-mbo-partners-ceo-miles-everson/?sh=347e3a247358"><strong>acceleration</strong></a> further boosts the freelancer revolution. Companies must resource projects at a fast pace and need access to skills outside of the organization.<br><br></div><div>In part due to the labor market situation in the tech field <strong>Juha-Matti Santero</strong>, who works as a Senior Data Engineer in the AI Roots network, decided to pursue the path of entrepreneurship. “It wasn’t something I initially considered for myself. However, I was already somewhere between being a consultant and a freelancer and wanted to go all in.”<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNzVkMzVhOTMtYzA5Mi00NWU4LWI1OWUtNGEzMTA5OGY1ZmZmP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--f1d0859e58c619e6b521a5809a1734ed3e0637a3" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWszTldRek5XRTVNeTFqTURreUxUUTFaVGd0WWpVNVpTMDBZVE14TURrNFpqVm1abVlHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--3f946f862aabd7d715daddcc357ad8468a057e91/_DSC0527%20crop%20small.jpg" filename="_DSC0527 crop small.jpg" filesize="90015" width="1050" height="591" previewable="true" presentation="gallery"></action-text-attachment><br><br>Santero (in the middle of the picture) has gained experience and expertise from working in large IT consulting companies. Before AI Roots he worked in a network of data consultants, which functioned well for him as the more independent way of working allowed him to see the value of his work more concretely. Eventually, Santero wanted to experience the full range of entrepreneurship and joined the AI Roots network in 2022. “The current labor situation in the data and software industries is so good now, so if you want to give freelancing a shot, the risk is rather low”, Santero describes.</div><div><strong> </strong></div><h1>Plan before giving it a go</h1><div>The shift to entrepreneurship can came across as an overwhelming process in the early steps. To dispel worries, Santero recommends taking time for planning and doing research. He read blogs of other freelancers and looked for other helpful content online. After deciding to become self-employed, he needed to choose the most suitable company form. Eventually, Ltd (Oy) was the best option, as it allows to separate personal and company assets. The majority of AI Roots freelancers work via an Ltd. Santero found establishing an Ltd a straightforward process that required registrations to the Patent and Registration Office and the Finnish Trade Register. Once the business is established, Santero recommends creating a functional work schedule and investing in an accountant. “You might think you save money when doing everything yourself, but eventually you will have more time to do the actual customer work.”</div><div> </div><div><strong>Tips for starting as a freelancer</strong><br><br></div><ul><li><strong>Take time for planning:</strong> before starting your business, take time to do research and find out about your mandatory responsibilities, such as the pension insurance for entrepreneurs (YEL)</li><li><strong>Outsource:</strong> don’t be afraid to delegate administrative tasks and invest in an accountant. You will have more time to concentrate on the core tasks and customer work.</li><li><strong>Join a network:</strong> a network connects you to customers, helps to tackle bureaucracy, and enables you to network with colleagues</li><li><strong>Schedule your work:</strong> optimize your use of time and leave a slot for the administrative tasks. If needed, synchronize your working hours with the customer</li></ul><div> </div><h1>Expert networks as a starting point</h1><div><a href="https://www.forbes.com/sites/jonyounger/2022/03/08/expert-networks-help-freelancers-in-many-fields-grow-their-income-and-impact/?sh=74042e6d35d3"><strong>Expert networks </strong></a>are becoming more and more common throughout various industries including technology. As the networks model consolidates its position, its potential no longer lies only in short-term projects, but allows the formation of more established customer relationships. For freelancers, professional networks make it easier to tackle some of the main concerns self-employment may involve; networking with customers and tackling paperwork and bureaucracy.<br><br>Due to these reasons, Santero highly recommends joining a network. For him, AI Roots has been a working option. ”Customer acquisition and contract-related issues are perhaps the biggest things to consider. When you become part of a network such as AI Roots, you no longer need to worry about these issues, which is great. The contract bases are already there, and all legal issues are taken care of. For me AI Roots was the stepping stone in becoming a freelancer.”<br><br></div><div>There is a wide range of different kind of data entrepreneurs with longer background working as a freelancer in the<strong> </strong><a href="https://www.rootsof.ai/services-for-data-professionals"><strong>AI Roots network</strong></a>. "Pete Tahvanainen has been a valued expert to our customers who need a seasoned full-stack data engineer and BI analyst. Working in the industry for more that 20 years, he also is showing example and giving encouraging support to new entrepreneurs in our network. It can be hard to be alone in this industry as a freelancer but working with others makes it easier and you can learn a lot.", says Iiris Lahti from AI Roots. <br><br></div><div>"I've been an entrepreneur for quite some while, since 2007. AI Roots is one of the best networks I've participated. There are many renown professionals in the network that I've worked during the years and new acquittances who have just started their entrepreneurial path. People in the network are highly professional and amiable. Working in projects with professionals in the network is thus very enjoyable. AI Roots network value is high, bringing new exiting and interesting data business opportunities which do realise to actual projects." says Pete Tahvanainen.</div><div> </div><h1>How to become a member of the network</h1><div><strong>1. Contact us:</strong> Book a 30min introduction meeting with Iiris or Max or send a contact request using the <a href="https://www.rootsof.ai/contact-us"><strong>contact form</strong></a>. Send your CV in advance to our partner.</div><ul><li><a href="https://meetings.hubspot.com/iiris-lahti">Book a meeting with Iiris</a></li><li><a href="https://meetings.hubspot.com/max-rantakallio"><span style="background-color: highlight;">Book a meeting with Max</span></a></li></ul><div><strong>2. Introduction:</strong> in the first meeting with AI Roots we will get to know you and you will learn more about what kind of services and assignments we can offer you</div><div><strong>3. Create profile and CV:</strong> AI Roots is using Cinode for creating a profile and CV for all new and existing freelancers. Creating a profile and CV to our portal doesn’t obligate anything but it helps AI Roots to find potential customers and assignment that match your skills and availability. </div><div><strong>4. Case found:</strong> Once a suitable assignment is found that interests the freelancer, AI Roots will help you to meet the customer. If everything goes well, we will help you with the contract negotiations and make your move to freelancing as smooth as possible. We have all the needed contract forms ready and optimized for freelancer projects.</div><div><strong>5. Welcome to AI Roots:</strong> You can join our network at any time, also before signing the contract. We have a joint Slack channel, networking events and active knowledge sharing with other entrepreneurs. By joining our network you can make sure to be the first one hearing about interesting new projects when needing one.</div><div><br>Learn more about AI Roots and how we support entrepreneurs in data industry:<br><br><a href="https://vimeo.com/625589029?embedded=true&amp;source=video_title&amp;owner=46609820">https://vimeo.com/625589029?embedded=true&amp;source=video_title&amp;owner=46609820</a></div>

Becoming an entrepreneur in the data industry

<div>Barona, the leading private employment agency in Finland, and AI Roots first started their cooperation 1,5 years ago. The Barona data journey kicked off with refreshing Barona data vision and opportunities. Since then, a lot has happened in Barona’s data journey. Leveraging work-life data has become a central factor in executing their mission, creating a better, fairer, and more successful work life. According to Barona’s Data Development Manager, Kristiina Juurmaa, Barona has taken big leaps to better make use of their data assets and to become more data-driven.</div><blockquote><em>“Our data maturity is growing rapidly and AI Roots has supported us in every step of this process. During the past year, we’ve had many projects that enable us to do more complex and advanced use cases in the future.”</em></blockquote><div>In addition to investing in data competencies, Juurmaa sees that the organization’s whole relationship with data has shifted:<em> “We have come a long way in the way we relate to data as an organization. For example, the vocabulary in which our people talk about data has changed majorly.”<br></em><br></div><h1>Partner in strategy and practice</h1><div>As companies’ data journeys are not always easy, AI Roots has walked beside Barona and helped them to remember that the process often contains similar steps regardless of the industry, and the execution requires a sense of patience. However, with carefully selected professionals, a lot can be accomplished in a few months or a year.<br><br></div><div>Jussi Ropo, operating as Barona’s CFO, has been happy with AI Root’s approach and sees the company holds a special capability to understand both the big picture and the practical issues. <em>”AI Roots’s process comprehends both the strategy work and the execution. They not only draw us a fine data vision and leave us alone with that but help us to carry things through. That is something we highly appreciate.”<br></em><br></div><div>Ropo also sees that their cooperation has been seamless since it is always easy to ask for help regardless of the situation: <em>“We have an exceptionally low threshold to ask for help, whether it’s about high-level things or basic daily stuff.”</em> Ropo adds that certain data projects have taken more time than initially planned but believes that careful execution will benefit them in the future.<br><br><br> <action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvOTc5YTYzZDEtMzlmMS00ZjIzLTg2ZmYtN2YyYzdkNTY5ZWU0P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--0961d101bfd1aaee207d8e07bdba1702c4529529" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs1TnpsaE5qTmtNUzB6T1dZeExUUm1Nak10T0RabVppMDNaakpqTjJRMU5qbGxaVFFHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--b632f555703bcb5413b8b49907dc72609b1ba2da/AI%20Roots%20case%20Barona%20Jussi%20Ropo.png" filename="AI Roots case Barona Jussi Ropo.png" filesize="410369" width="1050" height="525" previewable="true" presentation="gallery"></action-text-attachment></div><h1>Seamless teamwork as a premise for success</h1><div>The AI Roots data entrepreneurs working for Barona have been happy with how Barona has welcomed them as part of the team. The seamless teamwork has been a driver of successful cooperation and recognized also at Barona. <em>“We have been extremely happy with the AI Roots professionals, and they are now a central part of our team”</em>, says Kristiina Juurmaa.</div><div>Barona is utilizing several data engineers with various backgrounds to build up their data platform, integrations, data models and data visualization solutions. Barona has also started to explore possibilities for Machine Learning with the help of a data scientist for the AI Roots network.<br>Barona tends to interview all partners with technical skill set requirements, and they have always found the appropriate people from the AI Roots network.<em> ” You can sleep well at night when you know you’re working with AI Roots professionals”</em>, Jussi Ropo adds. He has not noticed obstacles regarding the entrepreneurship model but sees it as an advantage:<em> “As an entrepreneur, you must maintain your reputation and have a natural incentive to deliver.”<br></em><br></div><div><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvY2EzNjY1ZWYtMDM4My00NWJkLWJiOTEtOTJhYjdmOGFmNzA5P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--fac9514ac4c177c33501485f707a32c9f027ed82" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxqWVRNMk5qVmxaaTB3TXpnekxUUTFZbVF0WW1JNU1TMDVNbUZpTjJZNFlXWTNNRGtHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--2ecf09000c344179f247dddf3c18c99816a6921f/AI%20Roots%20case%20Barona%20Kristiina%20Juurmaa.png" filename="AI Roots case Barona Kristiina Juurmaa.png" filesize="247204" width="1050" height="525" previewable="true" presentation="gallery"></action-text-attachment></div><h1>People related data as a tool for creating better work life</h1><div>Iiris Lahti, the founding partner of AI Roots, has been impressed by Barona’s ambition to leverage work-life data and develop capabilities in HR-analytics: <em>“It is great to see how Barona is becoming one of the HR analytics forerunners in Finland. After all, we don’t see that many companies developing competencies in HR analytics, as many are focused on sales, production, or logistics”</em>, Lahti describes.</div><div>Juurmaa agrees that Barona has high ambitions in terms of data and reaching their strategic objectives:</div><blockquote><em>”We want to be the forerunners of developing people-related data responsibly. In a way all our data is human data, and in that sense, everything we do with the data can have a big impact on people’s work-life experience.”</em></blockquote><div><br></div><h1>Partner with us to boost your data journey</h1><div>We at AI Roots support companies in various industries and phases of the data journey. We can help to scale up a more mature data team or help in establishing one. If you would like to explore our talent base and different ways how we can support you, book a 30 - 60 minute introduction meeting with Iiris or Max or send a contact request using this <a href="https://www.rootsof.ai/fi/dataosaamisen-tilanne">form</a>.<br><br><br></div>

AI Roots supporting Barona data journey

<div>Groke Technologies is a company focused on multi-sensor-based and intelligent voyage advisory systems, supporting all stakeholders involved in vessel operation. Groke has a highly data-intensive product and data is a central factor of their system. As an example, they have collected millions of images from the oceans around the world. From the beginning, it was clear that Groke’s demands for data collection, quality, and partners were going to be high. According to Juha Rokka, the Co-Founder and CEO of Groke Technologies, data is their greatest asset. “We have a real-time system, and we must process all data on a millisecond level. Our system is continuously learning, so collecting data and developing the system based on that is a cornerstone of our operation.” Rokka describes that their system is based on three main building blocks, artificial intelligence, sensor fusion based on machine learning, and the user interface. Utilizing data to develop the system has been a continuous learning journey, of which AI Roots has been part.</div><h1>A partnership of like-minded companies</h1><div>Groke and AI Roots are companies of the same age, both founded in 2019. Around this time, the companies also started their cooperation. From the beginning, Rokka appreciated AI Roots’ experience in the start-up world and the capability to move things forward fast.</div><blockquote><em>“Our cooperation started with the right tone right away. I liked AI Roots’ concept and the way they do things. An agile operation model is extremely important for us since our needs are always acute. I also value that people at AI Roots understand the start-up world and have done things on a big scale, but also very practically”,</em> Rokka describes.</blockquote><div>As a result of their cooperation, Groke has been able to develop a strong internal data team and competence, which according to Rokka, is particularly important for them. AI Roots also delivered Groke the role of Data Science Lead, whose input receives praise from Rokka.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvYjZkYmUxODItMzM3YS00NWVmLWEyMGEtNjFmZmQwMzdhODNmP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--0368349d1e7804f31b8a59a63136e9254aad458b" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxpTm1SaVpURTRNaTB6TXpkaExUUTFaV1l0WVRJd1lTMDJNV1ptWkRBek4yRTRNMllHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--441c89c2b736e8c9351e7432061484f736a5bd44/AI%20Roots%20Groke%20Technologies.png" filename="AI Roots Groke Technologies.png" filesize="558251" width="1024" height="512" previewable="true" presentation="gallery"></action-text-attachment><br><br></div><h1> Exploring the possibilities of ML</h1><div>In the future Groke aims to invest in exploring the possibilities of machine learning and how they can further expand ML capabilities in data analytics and on the vessels. In this, the expertise of AI Roots is valued. </div><blockquote><em>”Whenever there are new ideas regarding ML or AI, I usually discuss with AI Roots first. Then you know the level of expertise is there from the beginning”</em>, Rokka states.</blockquote><div>Iiris Lahti, the Founding Partner of AI Roots, is also looking forward to the future of their cooperation, with which Lahti has been very pleased. ”Our partnership represents an ideal partnership model in which we can grow together and develop cooperation along with the changing situations, needs, and products”, Lahti says.</div><div>“We have people with Ph.D. background, but also very practical people who have been working in manufacturing long before us. Also, we have people straight from the school bench and others that are the age of 50+. What is in common is that our people want to see the results of their work very concretely”, Juha Rokka describes. Another layer of having a diverse team is utilizing people outside the organization.</div><blockquote><em>“It is good to have external people who can give us a third-party opinion. With AI Roots we can always have an open discussion when new ideas emerge”, Rokka concludes.</em></blockquote><div>In case you have similar needs and challenges finding data experts, please reach out to us and let's discuss how we can help.</div>

AI Roots supporting Groke Technologies

<div>This November we conducted a small survey for women working in the data industry to better understand how the industry is perceived by women professionals, how issues of diversity come across, and in what ways we could support more women to enter the industry, which benefits of talented people coming from different backgrounds. The study was conducted in collaboration with <a href="https://www.linkedin.com/showcase/women-in-ai-finland/">Women in AI Finland</a> and the results of the study released as a part of the Women in AI mentoring program event.<br>Many paths to the industry</div><div>The survey highlighted that it is possible to enter the data industry via various routes. Many of the respondents had worked in a different position/unit or a completely different industry before becoming data professionals. Ending up in the field was often described as a coincidence. It was emphasized that it is possible to find work within the field with different skill profiles and educational backgrounds. In sum, the industry needs new talents and there is no one right way to become a data professional.</div><blockquote>“I studied to become a math teacher but ended up hopping into the IT industry and first working on architectures and infrastructures. Then discovered that on the data side of things I could leverage my math skills as well. So ended up doing things that included everything that I had been doing so far in math and IT.”</blockquote><div>However, it was not always the case that women ended up in the industry by chance. In fact, many women were interested in data and had aimed for a career in data via technology/IT studies or, for example, a training program.</div><blockquote>(Entered the field)<em> </em>”Through my interest, I applied to the Big Data Specialist recruiting training program. From this, I applied for a job from data consulting company and got to consult on a wide variety of data projects.”</blockquote><div><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMmVjNjExOGQtNTVhYi00ZWUwLWI2ZGEtOWNlZTU3NjQ0ODU3P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--7cc3d30e451a1bf710c9d082709e0923b9d798aa" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt5WldNMk1URTRaQzAxTldGaUxUUmxaVEF0WWpaa1lTMDVZMlZsTlRjMk5EUTROVGNHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--c2f00c0873e130a61e495d0954f1d5c26d4bfef3/_DSC4311.jpg" filename="_DSC4311.jpg" filesize="492632" width="1050" height="701" previewable="true" presentation="gallery"></action-text-attachment></div><h1>Excellent employment opportunities</h1><div>As also this inquiry underlined, there is a high demand for data professionals, and with that the employment opportunities in the field are superb: Whereas new positions emerge, the data-related roles also become more diverse, and traditional business roles go to a more data-oriented direction. Due to the demand of the workforce and different skill requirements, it was seen that the door is now open for women to enter the field.</div><blockquote>“At the moment, the situation for employment is excellent. There are many open jobs. I believe that the industry will continue to grow strongly because there are still industries and companies where decisions are not made using data. In addition, data teams will grow in data-driven industries.”</blockquote><h1>Diverse perspectives are needed</h1><div>The data industry was seen as a male-dominated field, which, however, shows signs of opening and where women should strive to get into: The industry is well paid and possible career paths are numerous. Diversity was considered an important factor not only in terms of equality but as an enabler of better company performance and creating accurate, unbiased technology products and applications. Having diverse perspectives was also seen as central to problem-solving and innovation. Diversity was perceived to include not only different genders but other minorities and people with different character traits and neurotypes.<br><br></div><blockquote>“The result of various data processes (whether it's assessment science, A/B testing, an ML model running in production, or something completely different) in some way shapes our environment and our opportunities to work in it. There have been many troubling stories from Silicon Valley about what happens if the developer pool of data-intensive products is too homogenous. For example, it comes to mind that the health meter does not know about the existence of menstruation and face recognition that only works on Western faces. In the best case, even a homogeneous team can adapt and know how to develop for diverse users, but this is more likely to succeed if the team itself is closer in composition to the population.”“(Diversity is important) so that we do not waste talent because of irrelevant stereotypes, and everyone has a safe and supportive work environment. I've been thinking about this mostly from the neurodivergent perspective. We need to support different working styles. It's not easy for anyone.”</blockquote><div><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNTg5NTMwNzctMTM1OC00NGNlLWI5NTYtZmRkNzU1MGYxMDM1P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--6c81f3bb524ef8422c188e984ac8c4703397b197" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWsxT0RrMU16QTNOeTB4TXpVNExUUTBZMlV0WWprMU5pMW1aR1EzTlRVd1pqRXdNelVHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--9e27a66449b36077e1d36309f761b1346c6a90ed/_DSC4684.jpg" filename="_DSC4684.jpg" filesize="480451" width="1050" height="701" previewable="true" presentation="gallery"></action-text-attachment></div><h1>The importance of role models  and mentoring </h1><div>In increasing the attractiveness of the data industry for women, the importance of role models and mentors was highlighted. Other key factors were sharing information, training opportunities, and dismantling myths and stereotypes about the field. For example, coding was seen to be associated with unnecessary mysticism. The technical skills required to act as a data engineer, data analyst or even a data scientist can be acquired and learned, such as any other skills. Nowadays data &amp; analytics and related engineering skills can be learned in various training platforms or universities. <br><br></div><div>With that, it would be important to dismantle the perception that the industry is particularly challenging, or one should have specific characteristics to enter the field. In addition, data-related roles and tasks should be better defined to make the industry more approachable. The data engineering and data science is technical in nature but the industry offers also opportunities to more business oriented career, for example by working as a data product manager, business owner, data steward, data strategist or data designer.<br><br></div><div>To proceed in their careers, the women working in data put value in the support of colleagues, supervisors, and other women working in the field. They also emphasized the significance of mentoring (for example mentor programs), inspiring role models, and experiences of success achieved at work as useful for career advancement.<br><br></div><blockquote>“Inspiring women have encouraged with their example and made collaboration. Good predecessors, also men who have trusted and uplifted.”</blockquote><h1>Network, trust yourself and bravely go to out of your comfort zone</h1><div>Based on our small query, the path for women is now open to the data industry, where diverse perspectives and different skills profiles are needed more than ever. Luckily, there are already pioneers in the data industry who have paved the way for future female professionals. The essential message emerging from the study was to be brave, develop one's skills, and go outside of your comfort area.<br><br></div><blockquote>“Network, go to your discomfort zone, to ambitious projects and interesting projects. Be uncompromising in how you are treated. Set boundaries and vote with your feet if the alternative is to get stuck and waste your career in an environment that does not promote your growth. Also, find the right partners and do it together. Follow the biggest question or problem you have in mind and use your skills to solve it.”“You can do it! I could too. Not everyone has to be the same. You will surely find your suitable slot, where your strengths help to produce added value for.”</blockquote><div><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvNDA5YzQ3MTctZGEyOS00ZDYyLWFiYzUtNTUwYWMzM2U5MGQ1P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--4aa2ed32172afefa2c33b6afb071ffbc92c618e2" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWswTURsak5EY3hOeTFrWVRJNUxUUmtOakl0WVdKak5TMDFOVEJoWXpNelpUa3daRFVHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--5835c4e58a3d76cb919930caffab8f1e888d5290/WAI-1.png" filename="WAI-1.png" filesize="1111284" width="1050" height="701" previewable="true" presentation="gallery"></action-text-attachment></div><h1>Here are some tips that respondents gave for women seeking a career in data…</h1><ul><li>Get to know women working in the data field through LinkedIn or other social networks</li><li>Meet people live on data related events and conferences</li><li>Join a women's network, such as the local Women in AI or Women in Tech</li><li>Get a mentor to help setting a direction to your career development</li><li>Build your skills through (online) trainings</li><li>Begin your career as an intern in a company or through changing to a more data oriented role in your current employer</li><li>Educate yourself through courses in training platforms or by studying computer science, software engineering, data &amp; analytics based on your interest and targets</li><li>Boost your technical skills through completing certifications, joining hackathons or coding competitions</li><li>Be brave and believe in yourself and your ability to learn</li></ul><div><br>If you are considering a career as a freelancer data expert, you can also contact us for sparring and help: <a href="mailto:iiris@rootsof.ai">iiris@rootsof.ai</a> </div><div><br>---<br><br></div><div><strong>* Survey background</strong></div><div><em>The survey consisted of 11 questions, most of which were open questions. In total 18 female professionals answered the survey, and they represented a wide range of positions, such as Data Scientists, Data Engineers, Data Analysts, Project Managers, and developers. The women worked with various tasks, including data architecture, Machine Learning, DevOps, building digital identities, developing digital services, Data Archiving and Security. The majority of the respondents, 50 %, worked in a consulting company. 22 % worked in the companies’ internal data teams. The IT unit, entrepreneurs, and public institutions were also represented.</em></div>

How to support gender diversity in data and AI industry

<div>Meira Nova is one of the leading foodservice operators in Finland delivering food and restaurant commodities for S-group restaurants as well as external clients, including catering kitchens, workplace canteens, and the hotel and restaurant sector. The company’s turnover is 300 million euros and Meira Nova employs about 300 people, of which 200 are internal and 100 are external employees. Meira Nova is part of SOK (The Central Finnish Cooperative Society), which operates in the retail, travel and hospitality business in Finland and Estonia.<br><br></div><h1>Forming the business case</h1><div><br>The cooperation between Meira Nova and AI Roots started 2,5 years ago when Meira Nova was renewing its strategy, of which central focus areas were analytics and data-driven decision making. During that time AI Roots had contacted <strong>Jani Kaski</strong>, IT area lead at SOK, who recognized AI Roots’s potential to help Meira Nova in forming the analytics business case. At the time Kaski operated as the development manager at Meira Nova and carried forward the project of data-driven decision making.<br><br></div><blockquote><em>“AI Roots consolidated the analytics needs from Meira Nova's business stakeholders, and from there we jointly designed the development roadmap and related milestones”</em>, Kaski describes the early steps of the cooperation.</blockquote><div><br>Once the roadmap was ready, Meira Nova started to prepare the renewal of its analytics capabilities, based on the roadmap. At the same time, the SOK group was finishing a reference architecture project related to analytics services. The aim was to harmonize the analytics tools and the platforms used in the group. To carry out the renewal project, Meira Nova needed a partner. AI Roots was already in a good position as they had helped to form the analytics roadmap. According to Jani Kaski, also other factors influenced the choice of partner:<br><br></div><blockquote><em>“AI Roots performed well in the tender. The price tag of their offer was also very reasonable and competitive, so it was easy to choose AI Roots as a partner. AI Roots' strengths were also a more agile approach and the high experience level of consultants they were able to provide for this project.”</em></blockquote><div><br><strong>Phase 1: Engaging the business functions and setting new practices</strong><br><br>In addition to renewing the technological solutions, the first phase of the project included involving the business functions to define specifications for the analytics tools and services and training them to utilize the new tools. It was also central to adopt a new agile way of working, of which Meira Nova had little experience. Kaski sees that AI Roots’ understanding of how to involve the business units and teach the end-users to utilize the new technological tools were key prerequisites for succeeding in the first phase of the project:<br><br></div><blockquote><em>“It was a pleasure to see that the project progressed well and to notice how the end-user’s engagement and capabilities grew: Not only the ability to define the specifications but also learning how to use the system. The technical solution was well implemented but knowing how to involve people is not always self-evident for technical people. You can have good technical capabilities but finding developers with the adequate team and social skills can be more difficult.”</em></blockquote><div><br>In addition, the project stayed on budget.</div><div><br><strong>Phase 2: raising the stakes</strong><br><br></div><div>The successful implementation of the basic analytics capabilities in the first phase of the project laid a strong foundation for the continuation. Meira Nova had also formed a good working relationship with AI Roots, so it was easy to continue the journey together and keep on with the good practices. At the same time, the difficulty level of the project increased, and the aim now was to develop more advanced solutions. After Covid subsided, the intensity of the business also grew, which increased the workload of the business units and required from the project:<br><br></div><blockquote><em>“The social skills of the AI Roots developers were emphasized once again, as they had to take more part in defining the specifications and the facilitation. It helped that the developers were quite well versed in Meira Nova's business, and they understand, for example, certain financial requirements and can help in the decision making”</em>, Kaski describes.</blockquote><div><br>The second phase of the project is running smoothly, with tangible results.</div><div><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvMDAwZWU2NjEtMjFhZS00YTViLWIzMDQtY2QzZGQ3YzljMTJjP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--d7ba991d305cd429d33b81a00fec6a877d807bae" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWt3TURCbFpUWTJNUzB5TVdGbExUUmhOV0l0WWpNd05DMWpaRE5rWkRkak9XTXhNbU1HT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--7c2d03fc0be8e1d5c1cf475662afbf2cddc124bf/_DSC0776_pieni.jpg" filename="_DSC0776_pieni.jpg" filesize="34495" width="626" height="418" previewable="true" presentation="gallery"></action-text-attachment></div><div><strong>A joint learning journey</strong><br><br></div><div>Kaski's role has since moved from Meira Nova to operating as the area lead of SOK AID. Although there has been a slight shift of perspective, in practice little has changed in the cooperation with AI Roots. Kaski says he is also now in a better position to replicate the good practices of a successful project:<br><br></div><blockquote><em>“Culturally, the gains and well-working methods we have been able to achieve throughout the cooperation can be duplicated in other projects and activities. I can sincerely recommend AI Roots’ good developers and good organization and have already identified new tasks for AI Roots at SOK.”</em></blockquote><div><br>Kaski sees that Meira Nova, SOK IT, and AI Roots have grown together along the way and the joint journey has also involved good discussions, in which ideas and thoughts have been shared outside the project scope as well. <strong>Ari Rantanen</strong>, Partner at AI Roots, is on the same lines and has also been happy with the cooperation and looks forward to the future steps:<br><br></div><div><em>“Meira Nova has come a long way, and this one-team way of working has been quite unique.”<br></em><br></div><div>If you are interested to hear more about the case or need similar support in your organization, you may contact Ari <a href="mailto:ari.rantanen@rootsof.ai">via email</a> or <a href="https://meetings.hubspot.com/ari-rantanen">book a meeting </a>directly. Ari's email: <a href="mailto:ari.rantanen@rootsof.ai">ari.rantanen@rootsof.ai</a></div><div><br></div>

Meira Nova renewed its analytics with the support of AI Roots

<div>By <a href="https://www.rootsof.ai/blog/author/petri-mertanen"><strong>Petri Mertanen</strong></a><strong><br><br></strong>COVID-19 is speeding up digitalization and eCommerce, there’s no doubt about it. Because of the unexpected crisis, many organizations have been forced to stop and adjust their plans. The storm cloud might have a silver lining as this is a good time to analyze how you are acquiring new customers, which sales channels are effective and how do you deliver your products or services. Some companies are decreasing their advertising spend but at the same time marketing in digital channels becomes affordable even for the smaller businesses. For all, this creates an opportunity to evaluate the marketing channel strategy and improve marketing spend effectiveness.<br><br></div><div>In this article I explain how you can make sure that your data is valid and relevant for evaluating options and taking action. High quality data is also the foundation for more advanced analytics solutions.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvODFiNjc4ZjQtYjZlMS00Y2Y0LWIxZjAtMTExYmNhMmFhYTlkP2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--ff8116a4f8efdfef7f188e8b4877e3547c820488" content-type="image/jpeg" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWs0TVdJMk56aG1OQzFpTm1VeExUUmpaalF0WWpGbU1DMHhNVEZpWTJFeVlXRmhPV1FHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--8f70b555b1d8269e7f48f9a1180d1be7e533344e/Petri%20Mertanen%20.jpg" filename="Petri Mertanen .jpg" filesize="28674" width="700" height="394" previewable="true" presentation="gallery"></action-text-attachment></div><h1>Audit and validate data quality</h1><div>Whatever you decide or plan to do with your marketing spend during this crisis, data driven culture is the way to go. And when it comes to data, the quality of data is very important when making decisions and building the ability to use it in marketing. High quality data is the foundation of your house of analytics. If you want your house to last and have multiple floors, you need to have a very solid foundation. If you don’t, your house will collapse sooner or later. Every now and then, you should make <strong>data audits</strong> or have a more continuous way to check the quality of your data. Although we would like data to be 100% correct, there’s no such a thing as perfect data, especially when it comes to digital channels. By doing an audit to your data, you know how good your data is.<br><br></div><div>When eCommerce is blooming, it’s obvious that your web/mobile analytics system is playing a key role. According to my previous <a href="https://www.annalect.fi/tv-advertising-analytics-research/"><strong>research</strong></a> and current estimation, Google Analytics market share of (website) installations, is up to 80-90 % in Finland. I have been involved with hundreds of digital analytics audits and only few companies pass. Audits are must, but when you have collected data regarding your goals and possible online purchases, you should <strong>validate</strong> the data (against back-end) from time to time as well. And every time when you’re doing a test or launching a new campaign, you should debug and test the analytics setup and data collection. This way you’re able to maintain already achieved good data quality.</div><h1>Improve data relevancy</h1><div>If you have done the default implementation, you’re not going to get real business value out of your analytics system. Luckily, this is quite easy to check: Do you measure business outputs as goals? Likewise, if you have zero events or zero custom dimensions in place, you’re probably not tracking your website or mobile app close enough. Advanced setup usually means that there is a data layer in place, and you are tracking your website or app in very detailed level (with events).<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvZTU2NmU3NzQtMDdiNy00MTVjLTg0YmItNDY1NTc4ZTg5MDg4P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--56c1c2da030845e7fed7e9f6c37f21157175430c" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxsTlRZMlpUYzNOQzB3TjJJM0xUUXhOV010T0RSaVlpMDBOalUxTnpobE9Ea3dPRGdHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--ffdf5536afaa39c822e4bf10dcd5b10335d35534/Google%20Analytics%20Events.png" filename="Google Analytics Events.png" filesize="140346" width="1050" height="489" presentation="gallery"></action-text-attachment></div><div>Debugging and testing can be very time consuming to do.  Fortunately, there are systems to automate data quality checks: <a href="https://www.observepoint.com/products/"><strong>Observe Point</strong></a> has many products and one of the newest one, <a href="https://www.datail.io/"><strong>Datail</strong></a>, to mention few. Unfortunately, good data quality in one channel is not enough. In many organizations the data is spread in different kind of systems. Important data may be in CRM, advertising platforms, content management and web analytics systems.</div><h1>Define, test, integrate and visualize data</h1><div>In order to get and report real Key Performance Indicators (KPIs), you need to centralize data from silos to somewhere. Especially advertising spend can be in various ad management systems. You need that data in order to count Cost per Acquisition or Cost per Action (CPA) or Return on ad Spend (ROAS). These are examples of important KPIs, as you need to know how much certain business output costs or how much revenue you are getting back from each channel, you’re advertising in. If you need to test whether your metric is a KPI, there’s a nice <a href="https://www.kaushik.net/avinash/kill-useless-web-metrics-apply-so-what-test/"><strong>So what –test</strong></a> by Avinash Kaushik. With this test in mind, most of the people learn and ditch metrics like average time on site or average page views per session.<br><br></div><div>It is equally important that you don’t just report your KPIs – you need to set target levels for your goals. Your dashboard should show your key metrics in a timeline and compared to target level and previous year. You should see this data in a daily, weekly and monthly trend and year-to-date cumulative graph.<br><br><action-text-attachment sgid="BAh7CEkiCGdpZAY6BkVUSSJbZ2lkOi8vYmFja3N0YWdlLW5nL0FjdGl2ZVN0b3JhZ2U6OkJsb2IvYjE4ZDQ0MzItN2E5OS00YjAxLTk1YTgtZjA0YzkwMGQ3YjQ4P2V4cGlyZXNfaW4GOwBUSSIMcHVycG9zZQY7AFRJIg9hdHRhY2hhYmxlBjsAVEkiD2V4cGlyZXNfYXQGOwBUMA==--a4b0a75dca32c22f4b5530d80a7e3abf8564cf80" content-type="image/png" url="https://backstage-ng.herokuapp.com/rails/active_storage/blobs/redirect/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaEpJaWxpTVRoa05EUXpNaTAzWVRrNUxUUmlNREV0T1RWaE9DMW1NRFJqT1RBd1pEZGlORGdHT2daRlZBPT0iLCJleHAiOm51bGwsInB1ciI6ImJsb2JfaWQifX0=--5d34a007f03e5e409e69618e31d3cee63fc81748/Petri%20Mertanen%20analysis%20of%20goals%20and%20actuals.png" filename="Petri Mertanen analysis of goals and actuals.png" filesize="160960" width="1050" height="619" presentation="gallery"></action-text-attachment></div><div>In addition, you should have multiple data sources in your dashboard in order to succeed. If the need for integrating multiple data sources is permanent, there are many ways for building a Marketing Data Warehouse or a Data Lake solution. If you have a centralized Data Warehouse, then you don't need to do the integration work in the dashboard tool. Either way, there are also tools to help the integration work and keeping the metric metadata updated, such as <a href="https://supermetrics.com/"><strong>Supermetrics</strong></a>. </div><h1>Analyze data to take action</h1><div>Sharing data in your organization with a dashboard is not enough. Someone or something has to analyze the data - otherwise the numbers are useless. The most basic approach is heuristic analysis which is based on testing and observing website usability and comparing it to other similar sites or previous user experiences. This is not a very scientific as the output of analysis depends much on the person. Anyway, it’s better to do heuristic analysis than analysis at all. In this case we are usually describing what has happened and then why it happened? Good analysis includes clear recommendations or testing hypothesis to act on. When you are doing AB-testing and conversion rate optimization by testing, you have already taken a step or two forward in your data driven path.  The key is to let the testing results guide you also in those situations when they are not the same as your intuition would say.</div><h1>Grow roots for more advanced analytics</h1><div>When the organization’s maturity evolves, you are probably using algorithms and Machine Learning (ML) models to predict certain behavior. There are so many areas in marketing where advanced modelling can be used. Advertising systems have used algorithms for years for targeting, profiling and inventory optimization. There is also <a href="https://support.google.com/analytics/answer/7080259"><strong>predictive analysis and anomaly detection in Google Analytics</strong></a>. Marketing Mix Modelling (MMM) is definitely one of the most well-known advanced analytics solutions. With MMM you should get the big picture of your marketing actions, optimize the spend across marketing channels and analyze the effect of all the marketing Ps in use.<br><br>During the last couple of years, many start-ups have risen in the field of Machine Learning. These software vendors usually connect to several data sources like Google Ads, Analytics and Facebook. You can check for example <a href="https://diib.com/"><strong>Diib</strong></a> and <a href="https://uplifter.ai/"><strong>Uplifter</strong></a> in this scene. With these kind of vendors, and Google Analytics Intelligence, you have to know that you’re dealing with black-box solutions that have not been trained to fit to your business context. If you are statistician, mathematical person, programmer or Data Scientist, you may want to see and know what is under the hood. In this case you may want to test ML as a service type of platforms, like <a href="https://bigml.com/"><strong>BigML.com</strong></a>. And yes, some of you just want to use R or Python and that’s ok too. No matter if you’re struggling with the first solid implementation or fighting to find explaining algorithm, it’s important to know where you stand and what to develop next in order to get competitive edge from analytics. You can <a href="https://measurement.bain.com/"><strong>test your Measurement Maturity in here</strong></a>.</div><h1>Contact us for help</h1><div>If you are interested to improving the quality of your data, optimizing your marketing spend with data and growing your roots for more advanced analytics solutions, <a href="https://www.rootsof.ai/services-for-companies"><strong>contact us</strong></a> at AI Roots and our network of data professionals for help. AI Roots is also starting collaboration with <a href="https://www.mkollektiivi.fi/"><strong>MarkkinointiKollektiivi</strong></a>, a network of marketing professionals in Finland, to help marketing teams to utilize data and advanced analytics in their work.</div>

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