MetaDAMA - Data Management in the Nordics

• 4#11 - Kristiina Tiilas - The Role of Data Leadership in the Industrial Sector (Eng)

  «Leadership is about sowing the common vision and the common way forward, bringing the people with you.»How can a nuclear physicist transform into a data leader in the industrial sector? Kristiina Tiilas from Finland shares her fascinating journey from leading digitalization programs at Fortum to shaping data-driven organizations at companies like Outokumpu and Kemira. Kristiina provides unique insights into navigating complex data-related projects within traditional industrial environments. With a passion for skydiving and family activities, she balances a demanding career with an active lifestyle, making her an inspiring guest in this episode.We focus on the importance of data competence at the executive level and discuss how organizations can strengthen data understanding without a formal CDO role. Kristiina shares her experiences in developing innovative digitalization games that engage employees and promote a data-driven culture. Through concrete examples rather than technical jargon, she demonstrates how complex concepts can be made accessible and understandable. This approach not only provides a competitive advantage but also transforms data into an integral part of the company’s decision-making processes.Here are my key takeaways:The AI hype became a wake-up moment for Data professionals in Finland taking the international stage. As a leader in dat you need to balance data domain knowledge and leadership skills. Both are important.Leadership is important to provide an arena for your data people to deliver value.As a leader you are in a position that requires you to find ways of making tacit knowledge explicit. If not you are nit able too use that knowledge to train other people or a model.CDOThe Chief Data Officer is not really present in Nordic organizations.An executive role for data is discussed much, but in reality not that widespread.Without CDO present, you need to train somebody in the top leadership group to voice data.CDO is different in every organization.Is CDO an intermediate role, to emphasis Data Literacy, or a permanent focus?You can achieve a lot through data focus of other CxOs.Make data topics tangible, this is about lingo, narratives, but also about ways of communicating - Kristiina used gamification as a method.Creating a game to explain concepts in very basic terms with clear outcomes and structure can help with Data Literacy for the entire organization.Data in OT vs. ITPredictions and views on production should be able to be vision also in Operational Settings on all levels. There should not be any restriction in utilizing analytical data in operational settings.Security and timeliness are the big differentiators between OT and IT.These are two angles of the same. They need to be connected.IoT (Internet of Things) requires more interoperability.Extracting data has been a one way process. The influence of Reverse ETL on OT data is interesting to explore further.There are possibilities to create data driven feedback loops in operations.Data TeamsIf you start, start with a team of five: One who knows the data (Data Engineering) One who knows the businessOne who understands Analytics / AIOne who understands the users / UXOne to lead the teamYou can improve your capabilities one step at a time - build focus areas that are aligned with business need an overall strategy.If you expect innovation from your data team, you need to decouple them from the operational burden.Show your value in $$$.

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• 4#10 - Geir Myrind - The Revival of Data Modeling (Nor)

  "Vi modellerer for å forstå, organisere og strukturere dataene." / "We model to understand, organize, and structure the data."This episode with Geir Myrind, Chief Information Architect, offers a deep dive into the value of data modeling in organizations. We explore how unified models can enhance the value of data analysis across platforms and discuss the technological development trends that have shaped this field. Historical shifts toward more customized systems have also challenged the way we approach data modeling in public agencies such as the Norwegian Tax Administration.Here are my key takeaways:StandardizationStandardization is a starting point to build a foundation, but not something that let you advance beyond best practice.Use standards to agree on ground rules, that can frame our work, make it interoperable.Conceptual modeling is about understanding a domain, its semantics and key concepts, using standards to ensure consistency and support interoperability.Data ModelingModeling is an important method to bridge business and data.More and more these conceptual models gain relevance for people outside data and IT to understand how things relate.Models make it possible to be understood by both humans and machines.If you are too application focused, data will not reach its potential and you will not be able to utilize data models to their full benefits.This application focus which has been prominent in mainstream IT for many years now is probably the reason why data modeling has lost some of its popularity.Tool advancement and new technology can have an impact on Data Management practices.New tools need a certain data readiness, a foundation to create value, e.g. a good metadata foundation.Data Modeling has often been viewed as a bureaucratic process with little flexibility.Agility in Data Modeling is about modeling being an integrated part of the work - be present, involved, addressed.The information architect and data modeling cannot be a secretary to the development process but needs to be involved as an active part in the cross-functional teams.Information needs to be connected across domains and therefore information modeling should be connected to business architecture and process modeling.Modeling tools are too often connected only to the discipline you are modeling within (e.g. different tools for Data vs. Process Modeling).There is substantial value in understanding what information and data is used in which processes and in what way.The greatest potential is within reusability of data, its semantics and the knowledge it represents.The role of Information ArchitectInformation Architects have played a central role for decades.While the role itself is stable it has to face different challenges today.Information is fluctuant and its movement needs to be understood, be it through applications or processes.Whilst modeling is a vital part of the work, Information Architects need to keep a focus on the big picture and the overhauling architecture.Information architects are needed both in projects and within domains.There is a difference between Information and Data Architects. Data Architects focus on the data layer, within the information architecture, much closer to decisions made in IT.The biggest change in skills and competency needs for Information Architects is that they have to navigate a much more complex and interdisciplinary landscape.MetadataData Catalogs typically include components on Metadata Management.We need to define Metadata broader - it includes much more than data about data, but rather data about things.

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• 4#9 - Marte Kjelvik & Jørgen Brenne - Healthcare Data Management: Towards Standardization and Integration (Nor)

  "Den største utfordringen, det viktigste å ta tak i, det er å standardisere på nasjonalt nivå. / The biggest challenge, the most important thing to address, is standardizing at the national level."The healthcare industry is undergoing a significant transformation, driven by the need to modernize health registries and create a cohesive approach to data governance. At the heart of this transformation is the ambition to harness the power of data to improve decision-making, streamline processes, and enhance patient outcomes. Jørgen Brenne, as a technical project manager, and Marte Kjelvik’s team, have been instrumental in navigating the complexities of this change. Their insights shed light on the challenges and opportunities inherent in healthcare data modernization.Here are my key takeaways:Healthcare data and registryIts important to navigate different requirements from different sources of authority.To maintain comprehensive, secure, and well-managed data registries is a challenging task.We need a national standardized language to create a common understanding of health data, what services we offer within healthcare and how they align.Authorities need also to standardize requirements for code and systems.National healthcare data registry needs to be more connected to the healthcare services, to understand data availability and data needs.CompetencyData Governance and Data Management are the foundational needs the registry has recognized.Dimensional Modeling was one of the first classes, they trained their data team on, to ensure this foundational competency.If the technology you choose supports your methodology, your recruitment of new resources becomes easier, since you don’t need to get experts on that very methodology.ModelsUser stories are a focus point and prioritized. Data Lineage (How data changed through different systems) is not the same as Data Provenience (Where is the datas origin). You need both to understand business logic and intent of collection) - User stories can help establish that link.Understanding basic concepts and entities accounts for 80% of the work.Conceptual models ensured to not reflect technical elements.These models should be shareable to be a way to explain your services externally.Could first provides an open basis to work from that can be seen as an opportunity.There are many possibilities to ensure security, availability, and discoverability.Digitalization in Norwegian public services has brought forth a set of common components, that agencies are encouraged to use across public administration.Work based on experiences and exchange with others, while ensuring good documentation of processes.Find standardized ways of building logical models, based on Data Contracts.By using global business keys, you can ensure that you gain structured insight into the data that is transmitted.Low Code tools generate generic code, based on the model to ensure effective distribution and storage of that data in the registry.The logical model needs to capture the data needs of the users.Data Vault 2.0 as a modeling tool to process new dats sources and adhering to a logical structure.There is a discipline reference group established to ensure business alignment and verification of the models.Data should be catalogued as soon as it enters the system to capture the accompanying logic.Data VaultAdaptable to change and able to coordinated different sources and methods.It supports change of formats without the need to change code.It makes parallel data processing possible at scale.Yet due to the heterogeneity of data vault, you need some tool to mange.

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• Holiday Special: Joe Reis - A Journey around the World of Data (Eng)

  «Data Management is an interesting one: If it fails, what’s the feedback loop?»For the Holiday Special of Season 4, we’ve invited the author of «Fundamentals of Data Engineering», podcast host of the «Joe Reis Show», «Mixed Model Arts» sensei, and «recovering Data Scientist» Joe Reis. Joe has been a transformative voice in the field of data engineering and beyond. He is also the author of the upcoming book with the working title "Mixed Model Arts", which redefines data modeling for the modern era.  This episode covers the evolution of data science, its early promise, and its current challenges. Joe reflects on how the role of the data scientist has been misunderstood and diluted, emphasizing the importance of data engineering as a foundational discipline. We explore why data modeling—a once-vital skill—has fallen by the wayside and why it must be revived to support today’s complex data ecosystems. Joe offers insights into the nuances of real-time systems, the significance of data contracts, and the role of governance in creating accountability and fostering collaboration.  We also highlight two major book releases: Joe’s "Mixed Model Arts", a guide to modernizing data modeling practices, and our host Winfried Etzel’s book on federated Data Governance, which outlines practical approaches to governing data in fast-evolving decentralized organizations. Together, these works promise to provide actionable solutions to some of the most pressing challenges in data management today.  Join us for a forward-thinking conversation that challenges conventional wisdom and equips you with insights to start rethinking how data is managed, modeled, and governed in your organization.Some key takeaways:Make Data Management tangibleData management is not clear enough to be understood, to have feedback loops, to ensure responsibility to understand what good looks like.Because Data Management is not always clear enough, there is a pressure to make it more tangible.That pressure is also applied to Data Governance, through new roles like Data Governance Engineers, DataGovOps, etc.These roles mash enforcing policies with designing policies.Data ContractsShift Left in Data needs to be understood more clearly, towards a closer understanding and collaboration with source systems.Data Contracts are necessary, but it’s no different from interface files in software. It’s about understanding behavior and expectations.Data Contracts are not only about controlling, but also about making issues visible.Data GovernanceThink of Data Governance as political parties. Some might be liberal, some more conservative.We need to make Data Governance lean, integrated and collaborative, while at the same time ensuring oversight and accountability.People need a reason to care about governance rules and held accountable.If not Data Governance «(...) ends up being that committee of waste.»The current way Data Governance is done doesn’t work. It needs a new look.Enforcing rules, that people don’t se ant connection to or ownership within are deemed to fail.We need to view ownership from two perspectives - a legal and a business perspective. They are different.Data ModelingBusiness processes, domains and standards are some of the building blocks for data.Data Modeling should be an intentional act, not something you do on the side.The literature on Data Modeling is old, we are stuck in a table-centric view of the world.

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• 4#8 - Shuang Wu - Service Platform: From Analytics to AI-Driven Success (Eng)

  «We want to make data actionable.»Join us for an engaging conversation with Shuang Wu, Mesta's lead data engineer. We delve into the concept of platforms and explore how they empower autonomous delivery teams, making data-driven decisions a central part of their strategy.Shuang discusses the intricate process of evolving from a mere data platform to a comprehensive service platform, especially within organizations that aren't IT-centric. Her insights emphasize a lean, agile approach to prioritize use cases, focusing on quick iterations and prototypes that foster self-service and data democratization. We explore the potential shift towards a decentralized data structure where domain teams leverage data more effectively, driving operational changes and tangible business value in their pursuit of efficiency and impact.My key learnings:It’s not just about gaining insights, but also about harmonizing and understanding data in context.Find your SMEs and involve them closely - you need insight knowledge about the data and pair that with engineering capabilities.Over time the SMEs and the central data team share experiences and knowledge. This creates a productive ground for working together. The more understanding business users gain on data, the more they want to build themselves.Central team delivers core data assets in a robust and stable manner. Business teams can build on that.The DataYou can integrate and combine internal data with external sources (like weather data, or road network data) to create valuable insights.Utilizing external data can save you efforts, since it often is structured and API ready.Dont over-engineer solutions - find you what your user-requirements are and provide data that match the requirements, not more.Use an agile approach to prioritize use cases together with your business users.Ensure you have a clear picture of potential value, but also investment and cost.Work in short iterations, to provide value quickly and constantly.Understand your platform constrains and limitations, also related to quality.Find your WHY! Why am I doing the work and what does that mean when it comes to prioritization?What is the value, impact and effort needed?Service Platform:Is about offering self-service functionality.Due to the size of Mesta it made sense to take ownership for many data products centrally, closely aligned with the platform.Build it as a foundation, that can give rise to different digitalization initiatives.If you want to make data actionable they need to be discoverable first.The modular approach to data platform allows you to scale up required functionality when needed, but also to scale to zero if not.Verify requirements as early as you can.Working with business use casesVisibility and discoverability of data stays a top priority.Make data and AI Literacy use case based, hands-on programsYou need to understand constrains when selecting and working with a business use case.Start with a time-bound requirements analysis process, that also analyses constraints within the data.Once data is gathered and available on the platform, business case validity is much easier to verify.Gather the most relevant data first, and then see how you can utilize it further once it is structured accordingly.Quite often ideas originate in the business, and then the central data team is validating if the data can support the use case.

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