Avsnitt
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«Data Management is an interesting one: If it fails, whatâs the feedback loop?»
For the Holiday Special of Season 4, weâve 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 modelingâa once-vital skillâhas fallen by the wayside and why it must be revived to support todayâs 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: Joeâs "Mixed Model Arts", a guide to modernizing data modeling practices, and our host Winfried Etzelâs 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 tangible
Data 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 Contracts
Shift Left in Data needs to be understood more clearly, towards a closer understanding and collaboration with source systems.Data Contracts are necessary, but itâs no different from interface files in software. Itâs about understanding behavior and expectations.Data Contracts are not only about controlling, but also about making issues visible.Data Governance
Think 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 doesnât work. It needs a new look.Enforcing rules, that people donât 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 Modeling
Business 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. -
«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.
Itâs 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.
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:The Data
You 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 cases
Visibility 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. -
Saknas det avsnitt?
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«I think we are just seeing the beginning of what we can achieve in that field.»
Step into the world of data science and AI as we welcome Victor Undli, a leading data scientist from Norway, who shares his insights into how this field has evolved from mere hype to a vital driver of innovation in Norwegian organizations. Discover how Victor's work with Ung.no, a Norwegian platform for teenagers, illustrates the profound social impact and value creation potential of data science, especially when it comes to directing young inquiring minds to the right experts using natural language processing. We'll discuss the challenges that organizations face in adopting data science, particularly the tendency to seek out pre-conceived solutions instead of targeting real issues with the right tools. This episode promises to illuminate how AI can enhance rather than replace human roles by balancing automation with human oversight.
Expectations from working with cutting edge tech, and chasing the last percentage of precision.Reality is much more messy.Time management and choosing ideas carefully is important.«I end up with creating a lot of benchmark models with the time given, and then try to improve them in a later iteration.»Data Science studies is very much about deep diving into models and their performance, almost unconcerned with technical limitations.A lot of tasks when working with Data Science are in fact Data Engineering tasks.Closing the gap between academia and industry is going to be hard.Data Science is a team sport - you want someone to exchange with and work together with.
Join us as we explore the challenges of bridging the gap between academia and industry, with a spotlight on Norway's public sector as a cautious yet progressive player in tech advancement. Victor also shares his thoughts on developing a Norwegian language model that aligns with local values and culture, which could be pivotal as the AI Act comes into play. Learn about the unique role Norway can adopt in the AI landscape by becoming a model for small countries in utilizing large language models ethically and effectively. We highlight the components of successful machine learning projects: quality data, a strong use case, and effective execution, and encourage the power of imagination in idea development, calling on people from all backgrounds to engage.
Here are my key takeaways:
Get started as Data ScientistPublic vs. Privat
There is a difference between public and privat sector in Norway.Public sector in Norway is quite advanced in technological development.Public sector acts more carefully.Stakeholder Management and Data Quality
It is important to communicate clearly and consistently with your stakeholders.You have to compromise between stakeholder expectation and your restrains.If you donât curate your data correctly, it will loose some of its potential over time.Data Quality is central, especially when used for AI models.Data Curation is also a lot about Data Enrichments - filling in the gaps.AI and the need for a Norwegian LLM
AI can be categorized into the brain and the imagination.The brain is to understand, the imagination is to create.We should invest time into creating open source, Norwegian LLM, as a competitive choice.Language encapsulates culture. You need to embrace language to understand culture.Norways role is a sa strong consumer of AI. That also means to lead by example.Norway and the Nordic countries can bring a strong ethical focus to the table. -
«Focusing on the end-result you want, that is where the journey starts.»
You need to understand the cost of error of a ML/AI applicationCost of error limits the usability of AIDecision Science is a broader take on Data Science, combining Data Science with Behavioral Science.Decision Science covers cognitive choices that lead to decisions.Decision Science can just work in close proximity to the end user and the product, something that has been a challenge for many.
Curious about how Decision Science can revolutionize your business? Join us as our guest Rasmus Thornberg from Tetra Pak guides us through his journey of transforming complex ideas into tangible, innovative products.
Aligning AI with business strategies can be a daunting task, especially in conservative industries, but itâs crucial for modern organizations. This episode sheds light on how strategic alignment and adaptability can be game-changers. We dissect the common build-versus-buy dilemma, emphasizing that solutions should focus on value and specific organizational needs. Rasmus's insights bring to life the role of effective communication in bridging the divide between data science and executive decision-making, a vital component in driving meaningful change from the top down.
Learn how to overcome analysis paralysis and foster a learning culture. By focusing on the genuine value added to users, you can ensure that technological barriers don't stall progress. Rasmus shares how to ensure the products you build align perfectly with user needs, creating a winning formula for business transformation.
Here are my key takeaways:
Decision ScienceFrom Use Case to product
Lots of genAI use cases are about personal efficiency, not to improve any specific organizational target.Differentiating between genAI and analytical AI can help ton understand what the target is.genAI hype has created interest from many. You can use it as a vessel to talk about other things related to AI or even to push Data Governance.When selecting use cases, think about adoption and how it will affect the organization at large.When planning with a use case, find where uncertainties are and ability for outcomes.Itâs easy to jump to the HOW, by solving business use cases, but you really need to identify the WHY and WHAT first.Analysis-paralysis is a really problem, when it comes to move from ideation to action, or from PoC to operations.«Assess your impact all the time.»You need to have a feedback loop and concentrate on the decision making, not the outcome.A good decision is based on the information you had available before you made a decision, not the outcome of the decision.A learning culture is a precondition for better decision making.If you correct your actions just one or two steps at a time, you can still go in the wrong direction. Sometimes you need to go back to start and see your entire progress.The need for speed can lead to directional constrains in your development of solutions. Be aware of measurements and metrics becoming the target.When you build a product, you need to set a treshold for when to decommission it.Strategic connection
The more abstract you get the higher value you can create, but the risk also gets bigger.The biggest value we can gain as companies is to adopt pur business model to new opportunities.The more organizations go into a plug-n-play mode, the less risk, but also less value opportunities.Industrial organizations live in outdated constrains, especially when it comes to cost for decision making.Dont view strategy as a constrain, but rather a direction that can provide flexibility. -
«We made a transition from being a company that produces a lot of data, to a company which has control over the data we are producing.»
Mathematics is a combination of beauty, art and structure.Find your way in data and digitalization before jumping on the AI-train.Ensure that people can excel at what they are best at - this is what Tine tries to do for the farmers.Data only has a value, when it can be used - find ways to use data from analytics to prediction to more advanced algorithms.Create a baseline through a maturity assessment to see how you can tailor your work to the different business units.Follow up and monitor the usage of your data tools in the different areas of your businessCreate a gateway into data for your business users: Once that gateway is established it is also easier to introduce new tools.Data Literacy has a limit - not everyone in the business needs to be a data expert.Yet you need someone you can trust to enable and provide guidance - the Data team.Business users need to understand the difference between concrete answers and probability.How do you transform a complex organization without breaking the culture?Your data/digital/AI transformation team is key in ensuring good transformative action without breaking culture.Ensure you have good ambassadors for your data work in the Business Units, that what to transfer their knowledge in their respective units.Create a network of data-interested people, that help to drive adoption.Engage people by showing an initial value.Offer courses and classes for people to learn and understand more, but also to spread the word about your focus points.Inhouse courses provided by your own staff can increase the confidence in your data team.AI can mean different things to different people. It is important to define AI in your setting.Donât replace existing work process with AI-driven solutions, just for the sake of it. Find ways to focus on where improvement actually provides business value.When you think of a new AI project, you have several options:Develop in houseBuy off the shelfDo nothing Option two should be your preferred solutionAI strategy is part of a larger ecosystem, with conditions to adhere to.Data and algorithms should become interconnected, also visually represented.«Always remember your core business.»
Unlock the secrets of optimizing supply chains with data and AI through the lens of TINE, Norway's largest milk producer. Our guest, Olga Sergeeva, head of the analytics department at Tine, takes us on her journey from a passion for mathematics to spearheading digital transformation in the fast-moving consumer goods industry.
Ever wondered how organizations can successfully integrate AI tools into their business processes? This episode dives into the uneven digital maturity across departments and the strategies used to overcome these challenges. We discuss how data visualization tools act as a gateway to AI, making advanced algorithms accessible without needing to grasp the technical nitty-gritty. Olga shares how TINEâs data department empowers users by providing crucial expertise while ensuring they understand the probabilistic nature of AI-generated data.
Finally, discover how teamwork and a systematic approach can drive data adoption to new heights. From improving milk quality with predictive algorithms to optimizing logistics and production planning, we explore practical AI use cases within Tine's supply chain.
Here are my takeaways: -
"Det er vanskelig Ă„ komme seg ut av det jeg kaller: et excel-helvete. / It is hard to escape, what I call: Excel-hell."
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.Donât 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.
Are you wondering how medium-sized companies can handle data strategy and data governance effectively? Join us as we talk to May Lisbeth Ăversveen, who has over 23 years of experience in the industry, and shares her expertise from Eidsiva BredbĂ„nd. 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: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 rectifyFind known problems that are visibleFind pinpoints for your business usersIt 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.»
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.
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: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 marketundertand 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. -
«Donât go over to the cloud without truly understanding what you are getting into.»
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.
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: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.
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.
Building and nurturing internal communities is crucial. Tiankai and Winfried discuss how data governance conferences serve as therapeutic spaces, offering more than just professional developmentâthey 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 organizationsDAMA 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.»
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 sovereignUS 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.
In this seasons final episode, weâre 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 EUâs robust regulatory measures like the Digital Markets Act and the Digital Services Act.
Here are my key takeaways:
GeopoliticsThe 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 Ä gjÞre med disse teknologiene nÄr de blir 10 ganger sÄ 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?»
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, doesnât 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 - itâs 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.
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, Yngvarâs 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: -
"Det var jo veldig urealistisk Ă„ tenke kanskje at en haug med folk som har matematisk eller Computer Science bakgrunn, skal komme inn og skjĂžnne 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."
Data science needs a strong connection to business development You need to embed Data Science in a cross-functional environmentBusiness acumen needs to be ingrained in the work with dataData 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.
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 DevelopmentNorway & AI
Norway has a great AI and ML research community.The public discourse on AI portraits a quite narrow view, that doesnât 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 disciplineInterest in the organization and its sectorProblemsolvingCreativity -
"We donât need Data Governance where we donât have anything to fix."
How can Data Diplomacy transform an organization into a data-driven organization? This episode brings HĂ„kan Edvinsson, a visionary in data management and governance, into the conversation, revealing the intricacies and impacts of Data Diplomacy in Nordic organizations. HĂ„kan'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.
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.
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: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.Donât 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 doesnât 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 donât 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.
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.
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: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.» -
«Donât 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.
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.Donât 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.
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 inspirationData 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.Donât 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 donât understand.If you want to be sustainable in Data, you need to help the people in your organization to be part of the journey.Itâs not only about hiring new competency, but engaging with the knowledge you have in your organization. -
«Dataen i seg selv gir ikke verdi. Hvordan vi bruker den, som er der vi kan hente ut gevinster.» / «Data has no inherent value. How we use it is where we can extract profits.»
Norwegian police is working actively to analyse risks and opportunities within new technology and methodology, including how to utilize the potential of AI.But any analysis has to happen in the right context, compliant within the boundaries of Norwegian and international law.Data Scientists are grouped with Police Officers to ensure domain knowledge is included in the work at any stage.Build technological competency, but also ensure the interplay with domain knowledge, police work, and law.Juridical and ethical aspects are constantly reviewed and any new solution has to be validated against these boundaries.The Norwegian Police is looking for smart and simple solutions with great effect.The Norwegian Police is at an exploratory state, intending to understand risk profiles with new technology before utilizing it in service.There is a need to stay on top of technological development of the Norwegian Police to ensure law enforcement and the security of the citizens. This cannot be reliant on proprietary technology and services.Prioritization and strategic alignment is dependent on top-management involvement.Some relevant use cases:Picture recognition (not necessarily face-recognition) - how can we effectively use picture material from e.g. crime scenes or large seizure.Language to text services to e.g. transcribe interrogations and investigations. Human errors are way harder to quantify and predict then machine errors.This is changing towards more cross-functional involvement.The IT services is also moving away from project based work, to product based.They are also building up a «tech-legal staff», to ensure that legal issues can be discussed as early as possible, consisting of jurists that have technology experience and understanding.Data-driven police is much more than just AI:Self-service analysis, even own the line of duty.Providing data ready for consumption.Business intelligence and data insights.Tackling legacy technology, and handling data that is proprietary bound to outdated systems.
Embark on an exploration of what a data-driven Police Force can be, with Claes Lyth WalsĂž from Politiets IT enhet (The Norwegian Police Forces IT unit).
We explore the profound impact of 'Algo-cracy', where algorithmic governance is no longer a far-off speculation but a tangible reality. Claes, with his wealth of experience transitioning from the private sector to public service, offers unique insights into technology and law enforcement, with the advent of artificial intelligence.
In this episode, we look at the necessity of integrating tech-savvy legal staff into IT organizations, ensuring that the wave of digital transformation respects legal and ethical boundaries and fosters legislative evolution. Our discussion continuous towards siloed data systems and the journey towards improved data sharing. We spotlight the critical role of self-reliant analysis for police officers, probing the tension between technological advancement and the empowerment of individuals on the front lines of law enforcement.
We steer into the transformation that a data-driven culture brings to product development and operational efficiency. The focus is clear: it's not just about crafting cutting-edge solutions but also about fostering their effective utilization and the actionable wisdom they yield. Join us as we recognize the Norwegian Police's place in the technological journey, and the importance of open dialogue in comprehending the transformations reshaping public service and law enforcement.
Here are my key takeaways: -
«If you want to run an efficient company by using data, you need to understand what your processes look like, you need to understand your data, you need to understand how this is all tied together.»
Join us as we unravel the complexities of data management with Olof Granberg, an expert in the realm of data with a rich experience spanning nearly two decades. Throughout our conversation, Olaf offers insights that shed light on the relationship between data and the business processes and customer behaviors it mirrors. We discussed how to foster efficient use of data within organizations, by looking at the balance between centralized and decentralized data management strategies.
Decentralization alone might not be able to solve challenges in large organizations. Synergies with central departments can have a great effect in the horizontal.You have to set certain standards centrally, especially while an organization is maturing.Decentralization will almost certainly prioritize business problems over alignment problems, that can create greater value in the long run.Without central coordination, short-term needs will take the stage.Central units are there to enable the business.
We discuss the "butterfly effect" of data alterations and the necessity for a matrix perspective that fosters communication across departments. The key to mastering data handling lies in understanding its lifecycle and the impact of governance on data quality. Listeners will also gain insight into the importance of documentation, metadata, and the nuanced approach required to define data quality that aligns with business needs.
Wrapping up our session, we tackle the challenges and promising rewards of data automation, discussing the delicate interplay between data quality and process understanding.
Here are my key takeaways
Centralized vs. DecentralizedThe Data Value Chain
The butterfly effect in data - small changes can create huge impacts.We need to look at value chains from different perspectives - transversal vs. vertical, as much as source systems - platform - executing systems.Value chains can become very long.We should rather focus on the data platform / analytics layer, and not on the data layer itself.Manage whatâs important! Find your most valuable data sources (the once that are used widely), and start there.Gain an understanding of intention of sourcing data vs. use of data down stream«Itâs very important to paint the big picture.»You have to keep two thoughts in mind: how to work a use-case while building up that reusable layer?Donât try to find tooling that can solve a problem, but rather loo for where tooling can help and support your processes.Combine people that understand and know the data with the right tooling.Data folks need to see the bigger picture to understand business needs better.Donât try to build communication streams through strict processes - thatâs where we get too specialized.Data is not a production line. We need to keep an understanding over the entire value chain.The proof is in the pudding. The pudding being automation of processes.«Worst case something looks right and wonât break. But in the end your customers are going to complain.»«If you automate it, you donât have anyone that raises their hand and says: «This looks a bit funny. Are we sure this is correct?»»You have to combine good-enough data quality with understanding of the process that youâre building.Build in ways to correct an automated process on the fly.You need to know, when to sidetrack in an automated process.Schema changes are inevitable, but detecting those can be challenging without a human in the loop. -
«A lawyer has to be compliant. An advice from a lawyer should be fault free. Therefore it is so difficult to just do something. It is not in their DNA."
Unlock the secrets to the legal sector's digital transformation with our latest guest, Peter van Dam, Chief Digital Officer at Simonsen Vogt and Wiig. We promise you a journey into the innovative realm where data management and artificial intelligence redefine the traditional practices of law. Peter offers us a glimpse into his professional trajectory from legal tech provider to digital pioneer, emphasizing how data and application integration are revolutionizing legal services.
Discover the unique challenges and opportunities that come in a new era of digital sophistication in the law profession. Our conversation dives into the significance of roles like Chief Digital Officer in shaping a progressive future for a historically conservative field. We share stories of how to catalyze excitement for technology among legal eagles and clients alike, and we explore the strategic vision needed to navigate the balance between innovation, confidentiality, and compliance.
The episode examines the expanding potential for automation within legal services. Here, the focus shifts to how digital tools enhance, rather than replace, the human expertise of lawyers. Rounding off the discussion, we shine a light on how law firms are upgrading their data access protocols, ensuring that sensitive information remains under lock and key.
My key takeaways:LegalTech
Legal might seem as a conservative section, but on the insight everyone, from lawyer, to staff to paralegal is working on continuous improvement and growing more and more efficient.Low code, citizen development, hackathons, etc. are ways to quickly iterate on ideas and applying them.Internal and external marketing of the importance of technology in law is important.You have to lift those first step barriers, an get first hand knowledge of using AI and tech, to really embrace it.Document & Content Management
Optimizing interoperability and data exchange between different document management tools is an interesting journey.There is huge, untapped potential in unstructured data.The biggest challenge for document management is to find ways of cutting through the noise of redundant, obsolete, and trivial data.You need a certain quality of data sources to utilize LLMs and genAI.Methods of AI Governance need to work in concert with classical methods of data and Information Management.Data volumes are growing exponentially, and so does the cost. Records Management is important to structure data, create retention schedules and ensure that datahis available according to need and regulatory requirements.AI and trends in Technology
Find a way to balance need and investment in a way that you have the relevant tools available when needed but are not exclusively reliant on those tools.Development in technology, data, AI, sustainability, etc. creates more demand for legal services - technological development accelerates legal demand.For the practice of law, human interaction is vital. There might be a more differentiated service offering going forward, but human interaction with a lawyer will still be at the core of the practice.The role of CDO
The role of CDO is challenged, because it can mean so many different things in different environments.A Chief Digital Officer is important to get enthusiasm about new technology and to actually get it implemented and used.Communication is the most important skill and tool.As a CDO or Digitalization department you need to think 6 month ahead, elicit trends and find out what can become relevant for your firm. -
«We are going to treat our data at the highest level, making sure that we can use it as a competitive advantage. Then itâs a strategic choice.»
«When we talk about product mindset its all about how we work as a team.»It is important to ensure aligned autonomy, when working in a compartmentalized organization with product management. You are delivering a piece to the totality.«Now, we need to have an adaptive Strategy everywhere.»Digital is the totality, the ecosystem that you are creating. Data has to flow in that ecosystem.There is no digital without data, but there is data without digital.People are coming and going within your company, and are bringing data along.
Unlock the strategic potential that lies at the heart of Data and AI with our latest discussion featuring Anna Carolina Wiklund from IKEA. Embark on a thought-provoking journey with us as we dissect the significance of robust strategies in shaping digital landscapes. From the role of data as the lifeblood of digital commerce to the ways it can radically alter customer behavior, this episode promises insights that redefine the boundaries of e-commerce and digital merchandising.
We explore the complex interplay between business, digital and data, revealing how the alignment of strategies across various organizational levels can forge a path to business impact. Learn how a coherent vision can transform not just marketing strategies, but also those of HR and other departments, and the critical importance of shifting from output to outcome-focused objectives to measure success.
Finally, we navigate through the evolution of strategy in the face of AI's relentless march, examining the essential need for agility and visionary thinking to keep pace with a rapidly transforming arena. This episode is a masterclass in instilling a culture of excellence, accountability, and collaboration that can propel companies forward. With real-world examples and actionable insights, we offer a clarion call for businesses to reassess and adapt, ensuring that their strategies are not just surviving, but thriving, in the AI era. Join us and fortify your strategic acumen for an increasingly digital future.
My key takeaways:One Strategy
The goal of strategy is to create one clear direction for the company.If you have multiple strategies, you will pull people in different directions.Break down strategies in where you deliver the value.Organizational models and actual value creation do not always overlap.There are transversal strategies that stretch throughout the entire organization (eg. HR, product), whilst there are specific strategies that strive towards one goal (eg. marketing).You can no longer afford to have business and digital separated.Digital tools do not deliver any value unless they are part of a process and used by the business.Ensure that you measure that matters, what is the value that you are creating.You need to work on a mindset for the totality of the organization, not a digital vs business mindset.OKRs can help to get that forward leaning mindset and to become more process oriented.The strategic part is really the choices you have, while plan is the actions you take towards these choices.A plan is about creating transparency in the company, so everyone understands what they are delivering and how it fits together.You need to have a goal to work towards. Your Strategy is laying out the logic to get there.«Culture eats strategy for breakfast» -
"We believe that by making data more accessible, the city will become more transparent and accountable to the people that we serve."
Think about how you make data available - design thinking, finding new was to visualize data is important for inclusion.Its the responsibility of public sector to make as much of their data openly accessible.The role of CDO is important, because you need someone to see the bigger picture and how data effects everyone.Managing data, especially for public services, comes with a social responsibility.The difference between a CDataO and a CDigitalO - data requires a different skill set than digital transformation.Data professionals need to ask the correct questions in a service design process.Data access and ownership should be discussed already at the design phase.People have expectations towards digitalization in public sector: you want to access the data you need at the time you need it, from where you are.«Data is a valuable societal asset, where we all have the shared responsibility to ensure data quality.»Data quality is a precondition for using data to its purpose and its potential.You need to think digital universal accessibility, when it comes to data and visualization.With data stories the city of Reykjavik uses visual, verbal and sound effects to convey messages through data.There is a focus on using accessible language, and to not over-complicate texts.Data, especially in the public sector, has not been collected and curated with trains AI language models in mind.There is a great risk that historical biases and previous lack of awareness is transmitted into our models.
In our latest MetaDAMA episode, we're joined by Inga Ros Gunnarsdottir, the Chief Data Officer (CDO) of the City of Reykjavik, who's at the forefront of a transformation towards data-driven innovation of inclusion and accessibility. She walks us through her fascinating journey from engineering at L'Oreal to shaping the future of data use in municipal services. Her insights reveal how simple text, visuals, and a focus on digital accessibility are revamping the way citizens interact with their city's data.
As we navigate the terrain of digital transformation, Inga Ros delineates the distinct roles of a Chief Data Officer versus a Chief Digital Officer, highlighting the intricacies of their contributions to a city's digital ecosystem. Reykjavik's Data Buffet serves as a prime example of how open data visualization platforms can enhance not just transparency and accountability but also literacy in a society hungry for knowledge. She also shares compelling stories of data's impact in classrooms, planting the seeds for a future where every citizen is data-literate.
We wrap up our conversation with a deep dive into the nuances of creating data visualization tools that adhere to digital accessibility standards, ensuring that everyone, regardless of ability, can partake in the wealth of information available. The discussion traverses the significance of maintaining the Icelandic language in data communication and the imperative of ethical data collection practices, especially concerning marginalized groups. By the episode's end, it's clear that the key to unlocking the full potential of data lies in the simplicity and clarity of its presentation, an ethos that Inga Ros champions and we wholeheartedly endorse. Join us on this journey to discover how Reykjavik is rewriting the narrative on data inclusivity and the profound societal transformations that follow.
My key takeaways:Data Buffet:
Open data visualization platform and an open data portal.Make as much of the cityâs data easily accessible.Access to a wide variety of correct and reliable data is an enabler for innovation in societal services. - Visa fler
