MetaDAMA - Data Management in the Nordics

"Det er vanskelig å komme seg ut av det jeg kaller: et excel-helvete. / It is hard to escape, what I call: Excel-hell."

Are you wondering how medium-sized companies can handle data strategy and data governance effectively? Join us as we talk to May Lisbeth Øversveen, who has over 23 years of experience in the industry, and shares her expertise from Eidsiva Bredbånd. She provides us with insight into how to work with data maturity and the implementation of data strategy.

How can mid-sized companies balance resources and create effective data governance strategies? May Lisbeth and I explore this topic in depth. We talk about the importance of involving the business units early in the process in order to create ownership and commitment around the improvement measures.

Here are my key takeaways:

• The way we talk about data as a profession has changed, the lingo has changed and we adopt to trends.
• To display and evaluate data from different sources that are not connected, excel becomes the tool of choice.
• There is a very calculated amount of resources, that limit your ability to set up substantial teams to work exclusively on eg. Data Governance.
• Data Governance in SME (Small Medium sized enterprises) can be modeled as a repeatable process that incrementally enhances your data governance maturity.
• Identify sizable initiatives, ensure that they can be handled with a set amount of resources, and create metrics that enable you to track your progress.
• You need to find ways to ensure observability and monitoring over time.
• Don’t create something that you have no resources to maintain and improve going forward.
• To identify the right initiatives at the right time, you need to ensure a close collaborating with your business users.
• Ensure transparent and traceable ownership of the initiatives from the business side.
• To create a movement and engagement in data requires continuous and structured communication.

Data Maturity Assessment

• There is a need for speed and agility in SME, to ensure compatibility.
• Data Maturity Assessments are a welcome introduction to ensure that you create a baseline when working with data.
• There are advantages to both an internal view and to get some external perspective on your data maturity.
• Results from a maturity assessment can be a reality check that is not always easy to convey, yet you need to be realistic.
• Maturity assessments should ideally be both
   
  • Modeled/tailored to the needs of the organizations in question.
  • Repeatable and comparable over time and across organizations.
  • Good assessments cover both.
• To initially increase your maturity you can pick different tasks:
  • Low hanging fruits
  • «Duct-taped» operations that you can finally rectify
  • Find known problems that are visible
  • Find pinpoints for your business users
• It is good to start with cases that are understandable for business users, create interest, and can easy show value to leadership - this is what creates buy-in.
• You need to ensure that you keep a clear communication towards bigger, more substantial tasks, so your resources are not limited to quick win actions.

Data Strategy

• Data Strategy needs to be closely aligned with business strategy.
• Have a clear vision of where you want to go.
• To have a structure approach run our data strategy on how to handle both people, process, and technology is important for any work with data.
• Technology is not the staring point, but rather a consequence of your strategic choices, your organizational setup and your available resources.
• You need to include well-defined metrics to track progress.
• Find metrics that are closely connected to business outcome and value creation.

«The notion of having clean data models will be less and less important going forward.»

Unlock the secrets of the evolving data landscape with our special guest, Pedram Birounvand, a veteran in data who has worked with notable companies like Spotify and in private equity. Pedram is CEO and Founder at UnionAll. 
Together, we dissect the  impact of AI and GenAI on data structuring, governance, and architecture, shedding light on the  importance of foundational data skills amidst these advancements.

Peek into the future of data management as we explore Large Language Models (LLMs), vector databases, and the revolutionary RAG architecture that is set to redefine how we interact with data. Pedram shares his vision for high-quality data management and the evolving role of data modeling in an AI-driven world. We also discuss the importance of consolidating company knowledge and integrating internal data with third-party datasets to foster growth and innovation, ultimately bringing data to life in unprecedented ways.

Here are my key takeaways:

• Always when a new technology arrives, you need to adopt and figure out how to apply the new technology - often by using the new tools for the wrong problem.
• There is substantial investment in AI, yet the use cases for applying AI are still not clear enough in many companies.
• There is a gap I how we understand problems between technical and business people. Part of this problem is how we present and visualizer the problem.
• You need to create space for innovation - if your team is bugged down with operational tasks, you are canibalizing on innovative potential.
• Incubators in organizations are valuable, if you can keep them close to the problem to solve without limiting their freedom to explore.
• The goal of incubators is not to live forever, but top become ingrained in the business.
• CEOs need a combination of internal and external council.
• Find someone in the operational setting to take ownership from the start.
• The more data you have to handle the better and clear should your Data Governance strategy be.
• Small companies have it easier to set clear standards for data handling, due to direct communication.
• You want to make sure that you solve one problem really well, before moving on.
• Before intending to change, find out what the culture and the string incentives in your organization are.

LLMs as the solution for Data Management?

• ChatGP already today very good at classifying information.
• It can create required documentation automatically, by feeding the right parameters.
• It can supersede key value search in finding information.
• This can help to scale Data Governance and Data Management work.
• Data Management will become more automated, but also much more important going forward.
• RAG architecture - first build up your own knowledge database, with the help of vectorizing the data into a Vector-database.
• The results from querying this database are used by the LLM for interpretation.
• Find a way to consolidate all your input information into a single pipeline to build your knowledge database.
• Building strong controls on naming conventions will be less important going forward.
• Vectorized semantic search will be much faster.
• Entity matching will become very important.
• Fact tables and dimensional tables become less important.

Data to value

• Be able to benchmark your internal performance to the market
• undertand trends and how they affect you.
• How to use and aggregate third party data is even harder than internal data.
• You need to find ways to combine internal and third party data to get better insights.

«Don’t go over to the cloud without truly understanding what you are getting into.»

Unlock the secrets of cloud migration with industry expert Jonah Andersson, a senior Azure consultant and Microsoft MVP from Sweden. Learn how to seamlessly transition your data systems to the cloud. Jonah shares her knowledge on cloud infrastructure, AI integration, and the balance between Edge AI and Cloud AI, providing a comprehensive guide to building resilient cloud systems.

Explore the intersection of IT consulting, Data Governance, and AI in cloud computing, with a specific focus on security and agile workflows. Understand the critical impact of GDPR on data management and the essential collaboration between IT consultants and data governance experts. Jonah and I delve into the growing trend of edge AI, driven by security and latency concerns, and discuss responsible AI usage, emphasizing security and privacy. Learn how to navigate the complexities of multi-cloud strategies and manage technical debt effectively within your organization.

Jonah offers tips on avoiding common migration mistakes and highlights the significance of using tools like Azure's Cloud Adoption Framework. Whether you're modernizing outdated systems, merging companies, or transitioning to a new cloud provider, this episode equips you with the essential knowledge and resources to ensure a successful and strategic cloud migration journey. Join us for a deep dive into the future of cloud computing with an industry leader.

Here are my key takeaways:

• Azure services can be tailored to use cases and service needs. But you need to understand your requirements and needs.
• Once you understand what you need to do, you need to gain perspective in the how - what methods and processes are supported?
• Think security at every step.
• Security with integrations is an important part, we need to focus more on.
• Bringing different competencies together is a vital ingredient in building resilient applications.
• Cloud is about where your data resides, how you protect it and how you handle big data.
• Cloud should support the entire data lifecycle.

Cloud and AI

• «Cloud computing is the backbone of AI.»
• AI pushed for Edge AI, in addition to cloud. Reasons for Edge AI are latency, but mainly security.
• Cloud can provide an attack surface for eg. data poisoning, lack of control for training data, etc.
• AI tools can pose concerns on what and how you are exposing data.
• Awareness and education are important, when building something with AI.
• You need to at least understand your input to track your output - explainability starts with understanding of your data sources.
• There is a risk to Model Governance by on-perm due to the level of competancy needed.

Multi-Cloud vs. Single Cloud

• This is one of the questions to consider at the beginning of a cloud migration.
• Drivers for multi cloud strategy are:
   
  • Avoiding proprietary vendor lock-in,
  • Existing applications or infrastructure in another platform,
  • Choosing according to the quality of services offered by cloud vendors.
• If you choose multi cloud for automated resource management, you need to consider support platforms.

Cloud Migration

• Reason for cloud migration boil often down to gaining resiliency in the cloud, due to redundancy.
• You need to uphold Data Quality not just after the migration but also during the transit.
• Cloud migration requires strategy.
• There are great resources to help with your cloud migration, like the Cloud Adaption framework or the Well-Architected framework.
• Use observability and orchestration tools for your migration process.
• Ensure you understand your cost, and can optimize it to fit with your needs.

"For me, it really goes back to basic human needs, almost."

How can the sense of community support Data Professionals? We dive deep into this question with Tiankai Feng, a prominent figure in data governance and the Data Strategy and Data Governance lead at ThoughtWorks Europe. In this season four premiere of MetaDAMA, Tiankai shares his unique journey and how his passion for music plays a pivotal role in his professional and personal life. His story underscores the multidimensional nature of data professionals and the importance of a supportive community.

Building and nurturing internal communities is crucial. Tiankai and Winfried discuss how data governance conferences serve as therapeutic spaces, offering more than just professional development—they provide emotional and communal support. We explore various community models like grassroots movements and rotational leadership, highlighting the indispensable role of leadership in fostering these spaces. Recognizing and valuing community leaders is essential for sustaining these supportive networks within organizations.

Lastly, we delve into practical strategies for building strong data management communities. From integrating community introductions into onboarding processes to using these groups as recruitment tools, we cover it all. We also examine how company culture shapes the type of communities that flourish and the support provided by external organizations like DAMA. Joining communities helps alleviate isolation, share solutions, and foster a connected environment. Tune in to learn how to make community engagement a cornerstone of data governance and elevate both personal and professional growth.

Here are my key takeaways:
Communities in organizations

• Community is needed as a counterpart to the transactional behavior in a workplace.
• Communities of Practice is an established model, that comes from a technical side, methodology focuses.
• Communities can create new lines of communication, that can help spread a sense of belonging in an organization, beyond a specific department or team.
• Leadership needs to accept that being in a Community is also part of the job.
• Community leaders need recognition and to be valued for their work.
• The «smartest person in the room» should not be the leader of a Community - this can turn a community into a lecture setting.
• Ensure that organizational hierarchies are «flattened» in a Community, to support physiological safety and freedom to speak.
• Ensure you have some rules of engagement or code of conduct in place.
• Breakout groups can be a way to get everyone to participate actively in the Community.
• Leadership plays an important role to promote Communities in an organization.
• Well functioning Communities of Practice can become a selling point for recruitment.

DAMA as a Community

• A Community for Data professionals outside their organizations.
• The most outstanding impact DAMA can have is networking in a broad community, both local/national, but also internationally across sectors.
• There is an element of mentioning and coaching that a community of this size can offer.
• Another factor can be talent-sourcing: both for organizations, but also for job-seekers.
• Upscaling and learning are a great part of the DAMA Community, also including the CDMP certification.
• You need to find your balance between domain or sector specific communities and large data communities like DAMA.

SOME Community

• You need to be conscious about what you are reading on SOME.
• It can be a great place to provoke some new thoughts and get perspective on your work.
• There is certainly an entertainment factor to using SOME. Humor can heal a lot, and laughing about challenges we face as Data folks is like therapy.

«We can get lost in politics, when what we should be discussing is policy.»

In this seasons final episode, we’re thrilled to have Ingrid Aukrust Rones, a policy expert with a rich background in the European Commission and Nordheim Digital, shed light on the role of the global geopolitical landscape in shaping digital policies.

Explore with us the dominant influence of big tech from the US to China, and how the EU's regulatory approach aims to harmonize its single market while safeguarding privacy and democracy. Ingrid breaks down the contrasting digital policies of these regions and discusses how the EU's legislative actions are often driven by member states' initiatives to ensure market cohesion. We also chart the historical shifts in digital policy and market regulations from the 1980s to the present, highlighting key moments like China's WTO entry and the introduction of GDPR.

Lastly, we delve into the future landscape of digital societies and the challenges nation-states face within the context of Web3. Ingrid emphasizes the concentration of power in big tech and its potential threat to democracy, while also lauding the EU’s robust regulatory measures like the Digital Markets Act and the Digital Services Act.

Here are my key takeaways:
Geopolitics

• our security, economy, the national and international system relies on data.
• How data is collected, stored, protected, used, transferred, retained.. happens as much across boarders as within.
• Data Strategy on this geopolitical level is about creating a digital autonomy, not being reliant on big international enterprises, but for our political system to stay sovereign
• US is based on a liberal, free market model that is very innovation friendly.
• China is based on a very controlled environment, with limited access to their domestic market. Incubation of local companies, shield from global competition.
• The EU is setting the regulatory standard. Freedom is balanced with other values, like fairness or democracy.
• We need to talk about the role that big tech has on the global scene.
• Geopolitical impact on digital policies.
• Ingrid has a role between policy and business, coordinating and finding opportunities between both.
• EU has set the global standard in how we could deal with data and AI from a regulatory perspective.
• Politics are the decisions we make to set the direction for society.
• «Policy is the plan and implementation of what is decided through politics.»
• Cultural differences influence how we perceive, utilize and establish global policies, but also how we work with data in a global market.
• We have an issue if we only think in 4-5 year election cycles for tackling long term issues.

The EU

• Regulation is the biggest tool the EU has.
• «We are always in competition with technology, because technology develops so fast, and legislation develops so slowly.»
• You can see a change in responsibility for enforcement of EU rules and regulations, where implementation is moved from national responsibility to EU responsibility.
• The EU system is not any easy system to understand from the outside.

The rise of Big Tech

• We can go back to the anti-trust laws from the 1980s that opened for much more monopolistic behavior.
• The rise of the internet had a large influence on big tech.
• The liability shield was a prerequisite for social media platforms to gain traction.
• Big tech has created dependency for other organizations due to eg. their infrastructure offerings.
• We need to be aware of that concentration of power in the market.
• Big Tech is not just leading but also regulating the development of the market.
• Bigger companies that are competing with Big Tech, feel their influence and size the most.

«Hva er mulig å gjøre med disse teknologiene når de blir 10 ganger så bra som de er idag? / What might be possible to do with these technologies when they become 10 times as good as they are today?»

Can moonshot innovation really be the key to solving challenges that traditional methods fail to address? Today, we're thrilled to welcome Yngvar Ugland from DNB's New Tech Lab, who will unravel the complexities of digital transformation and share his unique insights from both corporate and startup ecosystems. From breaking the mold of the classic "people, process, technology" framework to stressing the importance of customer-centric approaches, Yngvar’s perspective offers a refreshing and profound look into fostering genuine innovation within established enterprises.

Technological innovation isn't always smooth sailing, and Yngvar helps us understand the friction between traditional mindsets and innovative approaches. Balancing high-trust societies against the urgency-driven dynamics of capitalism, we discuss the complex landscape of AI hype and explore technologies like GPT-3 and GPT-4. With an optimistic outlook, Yngvar encourages us to embrace the transformative potential of generative AI, highlighting the unprecedented opportunities that lie ahead. Tune in to gain a deeper understanding of the ever-evolving world of technology and digital transformation.

Here are my key takeaways:

• Yngvar has build and is leading the as he calls it «Moon-shoot unit at DNB».
• What do we need to do to actually implement and adopt to new technology and ways of working?
• How do we think tech for people in tech?
• We can identify three needed dimensions for change: 
  • a data / tech component, 
  • a business component and 
  • a change component.
• There is a difference between necessary and sufficient - just because a change is necessary, doesn’t mean that the proposed solution is sufficient.
• You need to find ways to navigate uncertainty, be active beyond concrete hypothesis testing, or tech-evaluation.
• For organizations to be successful, you need to coordinate both maintenance, improvement and innovation - it’s not one of those, but all there in concert that can ensure success over time.

• Innovation and digital transformation is not a streamlined process.
• Uncertainty offers a space for opportunity.
• We use the term agile without grasping its true meaning - an inspect-and-adapt mindset is key to agile.
• The development from GPT-1 through GPT-2 to GPT-3 is an example for the exponential development of technology.
• The digital infrastructure in Norway, that can utilize data and technology for value creation across public and private sectors is a reason for our success.
• The difference to the US market is that there are large cooperations that take on societal challenges.
• How our society is structure has an influence on how we perceive the need for innovation.
• It is natural to meet resistance in change and innovation.
• To iterate effectively you really need to live a mindset build around FAIL - First Attempt in Learning.
• We overestimate the effect of technology in the short term and significantly underestimate the long term.

"Det var jo veldig urealistisk å tenke kanskje at en haug med folk som har matematisk eller Computer Science bakgrunn, skal komme inn og skjønne forretningen. / It was very unrealistic to think that maybe a bunch of people with a mathematical or computer science background would come in and understand the business."

Join us on Metadama as we welcome Erlend Aune, an accomplished data science expert with a rich background in both academia and industry.  Through real-world examples from the Norwegian industry, we illustrate how successful research collaborations and technology transfers can stimulate innovation and create value. Despite the promising advances, we also candidly address the cultural and operational challenges businesses encounter when integrating AI research into their workflows.

What practical steps can bridge the gap between theoretical education and real-world application? Our conversation further explores the intersection of business development and the practical application of machine learning and data science. We emphasize the need for environments that foster hands-on experience for students, such as hackathons and industry-linked thesis projects. Additionally, we discuss the importance of tailored training development within organizations, focusing on understanding trainee characteristics to achieve meaningful training outcomes. Tune in to gain valuable insights and actionable advice on nurturing the next generation of data scientists and enhancing organizational capabilities.

Here are my key takeaways:

Data Science and Business Development

• Data science needs a strong connection to business development 
• You need to embed Data Science in a cross-functional environment
• Business acumen needs to be ingrained in the work with data
• Data Science needs to start from a Business side - ensure that you work on the problems that generate value for your organization.
• Data Science works with probability, not certainty - this notion is not yet understood by everyone in business.
• Data organizations are often build on an engineering mindset, that can be contradictive to an exploratory mindset.
• Even when designing Data Warehouse, you need to understand the business impact, have a business development mindset.

Norway & AI

• Norway has a great AI and ML research community.
• The public discourse on AI portraits a quite narrow view, that doesn’t reflect the broad application and research done in the field.

Research & Business

• Responsible AI is not a one-size fits all. Different organizations have different needs, for either certainty, security, reliability of outcome, etc. So a rAI approach needs ton be tailored to the business need.
• Startups and companies that have products related to the AI research environment, have the advantage that products are improved in tact with research development.
• In addition to in-house R&D, organizations can collaborate directly with research environments at universities.
• You cannot do R&D just as a pocket of excellence, if you want to operationalize results in your organization.
• We need to shorten the distance between R&D and operations.

For the Data Science Student

• If you apply knowledge on different challenges, you will get an intuition on how to solve a broad variety of challenges.
• When selecting a task within an organization as a Master thesis, make sure the task is delimited.
• Traits to succeed as a student working in industry:
  • Interest in your discipline
  • Interest in the organization and its sector
  • Problemsolving
  • Creativity

"We don’t need Data Governance where we don’t have anything to fix."

How can Data Diplomacy transform an organization into a data-driven organization? This episode brings Håkan Edvinsson, a visionary in data management and governance, into the conversation, revealing the intricacies and impacts of Data Diplomacy in Nordic organizations. Håkan's journey from business data modeling in the 90s to robust governance practices today offers a treasure trove of insights. Together, we dissect the evolution of enterprise architecture and its role in business innovation.

Discover how data governance is not just about maintaining quality but is a dynamic force that propels organizations forward with each structural change. We discuss the concept of data design and how this approach is shaping the future of responsible data usage in companies like Volvo Penta and Gothenburg Energy. Our dialogue uncovers the importance of integrating governance into decision-making and planning, ensuring data is not just managed but used as a strategic asset for innovation.

The finale of our discussion broadens the horizon, touching upon artificial intelligence and its relationship with traditional data practices. We challenge the status quo, urging businesses to embrace a leaner governance model that aligns with Lean and Agile methodologies. Alongside this, we unravel the subtle yet crucial distinction between data and information, arguing for a proactive business ownership in data design and governance.

Here are my key takeaways:

• If you want an organization to last, someone has to define key terms.
• Data Governance and Data Quality should not be done reactively, but rather by design.

Enterprise Architecture

• Connecting the work of EA to certain project gates, is underpinning a reactiveness in EA.
• EA claims to be the master interpreter of business needs, yet EA artifacts are based on second hand knowledge.
• Architecture as well as Governance are supporting a development, not dictating it.
• EA is NOT the business designer, just an interpreter, a facilitator, that enables those with 1st hand knowledge.
• Don’t generalize away from business reality.

Data Diplomacy

• As long as you are working with operational data, you need to embrace business data design.
• You need to bridge Business with IT.
• The «gravity for change», mainly through external factors provide management attention.
• Use these external triggers to create more with less.
• Dont talk solutions and technology - too many opinions. Stick to the data.
• Focus on what data should look like. Base your work on the facts.
• Enable people to understand data, requires Data Governance to take a facilitator role, not an excellence role.
• «Being a hero once doesn’t mean you are lasting.» - you need to find a sustainable way of doing data work, beyond task based, checklist compliance.
• Establish a Data Governance network that represents the entire organization.
• A common language and established tacit knowledge can speed up processes.
• You need to be ready, prepared, and on the edge to ensure you are resilient to change.
• Integrate your data decisions into the management structure.
• Firefighting gets more credit then fire prevention.
• Traditional Data Governance is too focused on operational upkeep, laking a future outlook.
• Data Governance don’t rely have the means to state: What should it look like in tomorrows world?
• Entity Manager: taking charge of definition, label and structure of a certain data entity, of the data that we should have.
• A Facilitator works with these entity mangers in their respective area.
• Advice against top-down, classical Data Governance implementation.

«AI will be so important in transforming health care as we know it today."

Join us as we sit down with Elisabeth M.J. Klaussen from DoMore Diagnostics, who are on a mission to transform cancer diagnostics with artificial intelligence to improve patient care and make drug development more effective. With a rich background in quality assurance and R&D within Pharma, Biotech, and MedTech, Elisabeth shares how AI is revolutionizing patient care and the pathway to personalized medicine.

Navigating the complexities of starting a healthcare venture can be as intricate as the regulations that govern it. In this episode, we discuss the maze of regulations across continents, the implications of the European AI Act for innovators, and the non-negotiable necessity of protecting patient data.

Wrapping up our dialogue, we emphasize the importance of a Quality Management System (QMS), especially when developing AI models. As we delve into the EU's AI Act and its potential to harmonize standards, Elisabeth offers invaluable advice to health startups: the development of a robust QMS is not just a regulatory tick box but a foundational pillar for market readiness.

Here are my key takeaways:
AI in Health Care:

• Personalized medicine requires to analyze a lot of data and set it in a personalized context.
• To create value with AI in health care is challenging, due to the high density of regulations, yet benefits can be huge.
• AI can enable us to use investments in pharmaceuticals, biotech as well as patient care more effectively.
• You need to ensure you can constrain AI models, not only on the data input, but also through use of parameters or model-architecture.
• The product from DoMore Diagnostics is i.e. a static model, not generative, that gives an output on leanings only.
• There is a need to apply for a new CE marking, if model would change.

Regulations in Health Care:

• You need to understand both your product and its intended purpose to understand what regulation will apply to you.
• You need to set up a team with the right people and competency.
• Try to find generalists - People that have a core competency, but are really good at adopting and learning new surrounding competencies at a more generalist level to complement each other.
• Laws and regulations in the industry are getting more and more globally standardized.
• If you adhere to the area with the most stringent rules, you can basically introduce your product to any market you like.
• If you set up your organization for regulatory compliance, you have two perspectives to keep in mind:
   
  • Internally - how do you set up your principles, polices and processes internally?
  • How do you act towards your sector and market?
• The regulation on EU level provides a framework, within you can find national regulations and laws that go beyond. One example is product labeling that can vary between EU countries.

The EU AI Act:

• The EU AI Act introduces requirements that the heavily regulated industry is following already. (E.g. quality systems, documented design and development of your product, validations, performance studies)
• EU regulations are political documents, that are build on compromise.
• There is a huge constraint within the EU commission as well as on the authority side to take on the workload that results from the AI Act and other new regulations.
• The more cumbersome regulations are and the more regulations you build in, the more expensive will products get.
• Standards and regulations can help to structure your ways of working, ensuring efficiency, not wasting time and money in doing things over and over again.
• «You can be more creative, if you have a structured way of working.»

«Don’t make it hard to understand for the business. Make it simple and clear.»

Get new perspectives on Data Governance with Valentina Niklasson from Volvo Penta as she talks about certain patterns, stages in the acceptance of Quality Management or Lean, that Data has to go through. Her rich experience in making Data Governance business-centric emerges, showcasing how you can get an organization engaged in Data.

Gain insights on the synergy between lean methodology and effective Data Management. We explore the application of the PDCA Deming circle in Data and discuss how common languages and methodologies bridge the gap between Data, IT and business. This convergence is not just theoretical; it's a practical pathway to tapping into customer insights, translating needs into strategies, and fostering a culture where continuous improvement reigns.

Finally, we delve into the human aspect of Data and Data Stewardship, emphasizing the importance of people over technology in cultivating a data-driven culture. By engaging the curious early and involving them in the development of business information models, we build ambassadors within the business, ready to champion change. Valentina and I talk about the dynamic role of Data Stewards and the approach to involving business personnel, ensuring the smooth adoption of new processes and strategies.

Here are my key takeaways:
Quality management as inspiration

• Data is still treated as an IT problem, but should really be treated as a business problem.
• We need to find a better way to communicate across data, IT and business.
• Use the same methodology wherever possible and try to reduce complexity in processes.
• Try to adapt to the ways of working in the business. Not creating own ways on digital, data or IT.
• You need to understand customer relations, end customers and the entire value chain to define needs correctly.
• Standardized ways of working can help to do right from start.
• Deming Cycle, PDCA, can be directly adopted to data. Think of data as the product you are building, that should have a certain quality standard.
• Don’t make it hard to understand for the business:
  • Using the same forms and approaches.
  • Business data driven process.
  • Let the business take part in the entire process.
• Lean Methodology should take a bigger place in data.
• A product management mindset makes data quality work easier.

Data Stewardship

•  You need to ensure owning the problem as well as the solution.
• High data quality is vital for data-driven organization. Someone needs to ensure this.
• Stewardship can have a negative connotation. 
• The technical demands on Data Stewards are really big today.
• Data Stewardship works if the Data Steward is part of a broader team.
• The role of Steward needs to be adjusted to the fast-speed reality.
• Data Stewards need to be able to solve problems, not only report to a central organization.
• Data Stewards should be approached in the business. You need that domain knowledge, yet they cannot perform the entire stewardship role.
• Most important to empower Data Stewards to start working and analyzing the challenges ahead.
• Don’t force Data Stewards to be technical data experts. That should be a supportive role in the Digital / data organization.
• If you build something new, engage Data Stewards from the beginning. 
• You cannot take responsibility for something you don’t understand.
• If you want to be sustainable in Data, you need to help the people in your organization to be part of the journey.
• It’s not only about hiring new competency, but engaging with the knowledge you have in your organization.