«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

• 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.

• 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.

• 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.

«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.»

Embark on an exploration of what a data-driven Police Force can be, with Claes Lyth Walsø 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:

• 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.

«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.

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. Decentralized

• 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.

The 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 what’s 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
• «It’s 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?
• Don’t 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.
• Don’t try to build communication streams through strict processes - that’s 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 won’t break. But in the end your customers are going to complain.»
• «If you automate it, you don’t 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 you’re 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.