MetaDAMA - Data Management in the Nordics Winfried Adalbert Etzel - DAMA Norway

«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.

«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 it’s a strategic choice.»

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:
«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.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»