Afleveringen
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"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."
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 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 disciplineInterest in the organization and its sectorProblemsolvingCreativity -
"We don’t need Data Governance where we don’t have anything to fix."
How can Data Diplomacy transform an organization into a data-driven organization? This episode brings Håkan Edvinsson, a visionary in data management and governance, into the conversation, revealing the intricacies and impacts of Data Diplomacy in Nordic organizations. Håkan's journey from business data modeling in the 90s to robust governance practices today offers a treasure trove of insights. Together, we dissect the evolution of enterprise architecture and its role in business innovation.
Discover how data governance is not just about maintaining quality but is a dynamic force that propels organizations forward with each structural change. We discuss the concept of data design and how this approach is shaping the future of responsible data usage in companies like Volvo Penta and Gothenburg Energy. Our dialogue uncovers the importance of integrating governance into decision-making and planning, ensuring data is not just managed but used as a strategic asset for innovation.
The finale of our discussion broadens the horizon, touching upon artificial intelligence and its relationship with traditional data practices. We challenge the status quo, urging businesses to embrace a leaner governance model that aligns with Lean and Agile methodologies. Alongside this, we unravel the subtle yet crucial distinction between data and information, arguing for a proactive business ownership in data design and governance.
Here are my key takeaways:
If you want an organization to last, someone has to define key terms.Data Governance and Data Quality should not be done reactively, but rather by design.Enterprise Architecture
Connecting the work of EA to certain project gates, is underpinning a reactiveness in EA.EA claims to be the master interpreter of business needs, yet EA artifacts are based on second hand knowledge.Architecture as well as Governance are supporting a development, not dictating it.EA is NOT the business designer, just an interpreter, a facilitator, that enables those with 1st hand knowledge.Don’t generalize away from business reality.Data Diplomacy
As long as you are working with operational data, you need to embrace business data design.You need to bridge Business with IT.The «gravity for change», mainly through external factors provide management attention.Use these external triggers to create more with less.Dont talk solutions and technology - too many opinions. Stick to the data.Focus on what data should look like. Base your work on the facts.Enable people to understand data, requires Data Governance to take a facilitator role, not an excellence role.«Being a hero once doesn’t mean you are lasting.» - you need to find a sustainable way of doing data work, beyond task based, checklist compliance.Establish a Data Governance network that represents the entire organization.A common language and established tacit knowledge can speed up processes.You need to be ready, prepared, and on the edge to ensure you are resilient to change.Integrate your data decisions into the management structure.Firefighting gets more credit then fire prevention.Traditional Data Governance is too focused on operational upkeep, laking a future outlook.Data Governance don’t rely have the means to state: What should it look like in tomorrows world?Entity Manager: taking charge of definition, label and structure of a certain data entity, of the data that we should have.A Facilitator works with these entity mangers in their respective area.Advice against top-down, classical Data Governance implementation. -
Zijn er afleveringen die ontbreken?
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«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.» -
«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.
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.
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.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.»
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 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: -
«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 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.»
«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 city’s data easily accessible.Access to a wide variety of correct and reliable data is an enabler for innovation in societal services. -
«Companies are already wanting to position themselves ahead of the legislation, because they see the value of actually adaption best practices early on and not waiting for enforcement.»
Looking at AI from a risk perspective is the right way to tackle the challenges within.Risk based approach makes sure that development is not freezed.Our job as experts in the field is to demystify compliance within the use of AI systems.Find the right balance between compliance and innovation, by assessing potential risks."The AI Act is part of the European Digital Strategy and is the first comprehensive legal framework for AI in the entire world.»CE marking forces you to have constant monitoring and compliance of the system, as well as registration in a register.Have a holistic approach to AI: How does it fit in the wider setting of my company, both from a data, business and cultural perspective?There are big differences in companies maturity to operationalizing AI for value creation.The focus on risk and safety does not correlate to the need for speed in AI adoption.It’s not about starting from scratch, but about understanding the actual use-cases and needs.The AI Act can foster innovation, because you know what your framework is."Make sure that the date you are using reflects the diversity and the reality of the people and situations that the AI system will encounter."Observe and control data quality and distribution continuously.
Prepare to dive into the risk-based approach of legislation for artificial intelligence with the insights of Laiz Batista Tellefsen from PwC Norway, who brings her expertise in AI from a legal perspective to our latest episode. We tackle the imminent European Union's AI Act with its sophisticated risk-based approach, dissecting how AI systems are categorized by the risks they pose.
Norwegian companies, listen up: the AI Act is on its way, and it's time to strategize. We discuss the necessary steps your business should consider to stay ahead of the curve, from embracing AI literacy to reinforcing data privacy. Laiz and I dissect the balance between innovation and risk management, and we shed light on how cultivating a culture of forward-thinking can ensure safety doesn't come at the cost of progress. This segment is a must for businesses aiming to turn compliance into a competitive edge.
Zooming out to the broader scope of AI governance, we offer advice for maintaining the delicate dance between compliance and cultivating innovation. Discover the vital guardrails for capitalizing on AI's potential while readying for the unknown risks ahead. We peel back the layers of the AI Act's impact on the legal sector, unearthing the nuances of intellectual property rights and data transfer laws that could reshape your organization's approach to AI. Join us for a conversation that promises to leave you not only prepared for the AI Act but poised to thrive in an AI-centric future.
Here are my key takeaways:What to consider now:
Make sure the company has very good control of known risks, like privacy.Make data risk awareness part of your culture.Understand roles and responsibilities in our organization towards data risks.Have your policies updated.Ensure your stakeholders are well trained. -
«The journey Software development went through during the last 10 years, working towards DevOps and agile development, is something that we can really benefit from in the data space.»
There is a certain push in the insurance industry towards data, AI and autiomation.Gjensidige has over 20 decentralized analyst teams.Data Mesh is about empowering analyst teams to take control over their data.By taking responsibility over their own data, analyst teams take off the load from Data engineering teams, so they can focus on the tricky stuff.MLOps, DataOps, or classic DevOps in the Data Space is about using System Development principles in the Data Space.The questions that arise within data today, are questions that software engineering went through 10 years ago.Software development also went through a maturing, that brought forth a domain driven focus, best practice focus, product thinking, etc.Documentation should live, where the code also lives. It should be part of the code.Introduce more software development best practices into the data teams.Do not think about the solution you want to develop, but the problem you want to solve.Time-box exploratory efforts into sprints.
Uncover the synergy between agile software development and data management as we sit down with Alexandra Diem, head of Cloud Analytics and MLOps at Gjensidige, who bridges the gap between these two dynamic fields. In a narrative that takes you from the structured world of mathematics to the true data-driven insurance data sphere, Alexandra shares her insights on Cloud Analytics, Software Development, Machine Learning and much more. She illustrates how software methodologies can revolutionize data work.
This episode peels back the layers of MLOps, drawing parallels with the established tenets of software engineering. As we dissect the critical role of continuous development, automated testing, and orchestration in data product management, we also navigate the historical shifts in software project strategies that inform today's practices. Our conversation ventures into the realm of domain knowledge, product mindset, and federated governance, providing you with a well-rounded understanding of the complexities at play in modern data management.
Finally, we cast a pragmatic eye over the challenges and solutions within data engineering, advocating for a focus on practical effectiveness over the elusive pursuit of perfection. With Alexandra's expert perspective, we delve into the strategy of time-boxed approaches to data product development and the indispensable role of cross-functional teams. Join us for an episode that promises to enrich your view on the interplay between software and data.
Here are some key takeaways:The pitfalls
Software Development Lifecycle vs. Data Lifecyle – they overlap, but there are clear differences, especially in the late phases.Feature-driven (or functionality-driven) vs. Data-driven: Is there a problem with software engineering mindset in data?Hypothesis - Data Science vs. Engineering mindset: Explorational vs. structural thinking can cause frictionEnvironmental challenges: How does Test-Dev-Prod split fit with data? -
«I took the time to actually go through all of my notes, all of the training courses, all of the things that I looked at over the past 30 years of work. And I thought, I want to give myself a reference book. Wherever I go, I have this single thing that will have enough information to remind me of stuff I need to consider. This is now my book of Patterns."
Get ready to have your perspectives on data management revolutionized! This Holiday special serves up a treasure trove of insights, as we dive deep into the interconnections of data, information, knowledge, and wisdom. We'll be shining light on the importance of quality data and the emerging role of data officers in organizations, challenge conventional thinking about systemic behavior changes and their impact on data management, while also stressing the utmost necessity of experimentation and testing to comprehend the ever-changing data patterns.
I was lucky to pick the brain of the experienced data expert Jonathan Sunderland, whose career has spanned an array of industries and roles. The conversation is a call to arms for organizations to have clear purposes and goals when striving to become "data-driven." Plus, you'll get an exclusive peek into our guest's impressive "book of patterns" project, which promises to be an invaluable reference for future endeavors.
This is a thought-provoking exploration of the fine balance that large organizations need to strike between agility and long-term goals. We'll confront the dangers of resistance to change and the pitfalls of a myopic focus on quick wins, offering insights on how to foster a culture of innovation without falling into the trap of over-optimization or outsourcing purely for cost reduction. Moreover, we'll dive into the world of data governance, discussing its crucial role in fostering trust with data and facilitating informed decision-making. Finally, we distill the essence of personal growth into three potent rules of challenge, enable, and inspire. So, what's your capacity? How can you elevate it to tap into your fullest potential? This episode inspires to ponder these questions and propel your personal and professional growth.
Happy Holidays! -
«Sentralt I dette med å skape verdi er tverrfaglighet og involvere hele bedriften, ikke bare et lite Data Science miljø.» / «Central to creating value is multidisciplinarity and involving the entire company, not just a small Data Science environment.»
Prepare for a journey into the landscape of data strategy with seasoned Data Scientist, Heidi Dahl from Posten Bring, one of the largest logistics organizations in Norway. She is not just engaged in strategic discussions about data, AI and ML, but also a passionate advocate for Women in Data Science, took the initiative to create a chapter of WiDS in Oslo, and co-founded Tekna Big Data.In our chat to understand the dynamics of data science and IT, we talk about their balance between research and practical development. Heidi articulates the urgency for a dedicated data science environment, exploring the hurdles that organizations often confront in its creation.
An organization that is 376 year old and has been innovative throughout all of those years.The Data Science department was stated in 2020 under Digital Innovation, now a part of Digital technology and security.The innovative potential is found through use-case based work closely integrated with the business domains.There are several algorithms that made their way into production, and that is a goal to measure against.The Data Science teams consist of cross-functional skillsets, bringing together Data Science, Developers, Data Engineering and Business users.The exploratory phase is vital, but has to have a deadline.IT driven development projects do not always match with the needs of Data Scientists.Data and IT need to work together, but for exploratory work, Data Science should be able to set ut needed infrastructure.On cloud infrastructure it can be vise to think multi-cloud to ensure availability of a specter of relevant services.Posten/Bring is looking to build a digital twin for their biggest package terminal for better insight, control and distribution of packages.
We cross into the world of logistics, shedding light on the potential power of data science to revolutionize this industry. We uncover how strategic use of data can streamline processes and boost efficiency. Finally, we underscore the importance of nurturing an environment conducive for data professionals to hone their skills and highlight the role of a data catalog in democratizing data accessibility.
Here are my key takeaways:
Digital Transformation of Posten BringStrategic use of data
How can we use data to make better decisions, be more effective and smarter?The 4 core elements of the Data Strategy:Establish distributed ownership of data and data productsIncrease the amount of self-service.Build competency tailored to your user groups needs.Strive towards the goal of great services and products based on data for your users and customers.Role based self-service capabilities .A data catalog is discussed, to gain a better understanding of the data available, security, but also context of origin and data lineage.A data catalog needs to be able to serve different user needs.Competency
There are three perspectives:How to recruit new and needed competency?How to train and share competency internally?How to retain competency?Data Engineer is a newer and more specialist role, that is hard to find on the market.You need to give your data professionals the possibility to do purposeful work, bring into production and connect to value creation.The entire organization should be aware of how to use data to make work more efficient and smart - think data literacy -
«A combination of strong buy in from top-management and strong flow of change agents (…) is a requirement for succeeding.»
Missing out!Messing up! Telecom still needs to catch up, but with a string focus on using and scaling AI technology.The biggest differentiator in the sector is applying methods and technology to provide the best customer service.To scale AI, you need to have very solid data capabilities .Cloud native data platform with various continuously upgrading technologies.Efficient and scalable storage and processing capabilities.Data Governance structures to ensure accessibility and use of data in a secure, privacy friendly, ethical way.You need that foundation before you can start building advanced AI capabilities.Apart from data you need people who are data literate and technical adverse.A strong data culture is important, not just for the data experts in your organization, but for everyone.
Eager to unlock the secrets behind building a trustful relationship with AI systems? I am sitting down with Ieva Martinkenaite, head of Telenor's Research and Innovation department to shed light on the interplay of accountability, ethics and AI technology. Through her role as translator between tech, leadership and business , Ieva brings a refreshing vantage point to the dialogue, providing a unique bridge between the tech and business spheres.
We're taking a deep dive into the creation of responsible AI within an organization. Our conversation explores the firm foundation of clear values and top management's proclamations, to cultivate a bottom-up process for a governance structure. Understand the three-layer structure of AI governance and the imperative of expert support for data professionals. Plus, we'll be scrutinizing how adopting responsible AI as a core principle can fetch a positive social impact.
In the finale of our discussion, we underscore the essence of responsible AI use and the value of investing in data professionals. Discover how individuals and companies can not only fulfill, but surpass compliance standards. Remember, it's not just about employing AI responsibly but about finding a responsible approach that fits you as an individual and your company.
Here are my key takeaways:
The two scenarios of concern with AI in Telecom:Responsible AI
ResponsibleAI should be build on a solid Data Governance foundation.The biggest concern of executives with AI is the lack of traceability with data.We need to a) understand what are the risks, b) create responsibleAI by design.Executive support and belief in the AI journey is key.Data professionals have a responsibility to communicate complexity, translate and apply their knowledge to ensure a more general data literacy.You should do anything possible to be able to explain how your models work.You need to ensure that it is save to talk about, also not understanding systems.Steps to building Responsible AI Governance:
Decide as an organization on your core principles / value - how may they be challenged by AI?Define your principles / values for AI - these should be AI specific, but adopted to your setting, concerning risk, portfolio, etc.Make these principles / values actionable.Seek endorsementBuild a Governance structureEnsure training and awarenessPositive Social Impact
Companies should feel a social responsibility to go beyond what is required to build better, more ethical systems and use of those.Ask yourself, why are you doing responsible AI and Governance? For compliance obligations or do you what to go beyond that to build based on high ethical standards? -
«How well are we rigged in Norway to handle this?»
Throughout the last years, there has been a significant change in public attention to AI, even though AI has been part of our lives for quite some time.There is a great AI community in Norway, with great research that is done.
What a fantastic talk - With so much happening in Norway in autumn 2023, I brought on Alex Moltzau for a chat in AI policy and Norway. Alex Moltzau is a Senior Policy Advisor at the Norwegian Artificial Intelligence Consortium (Nora.ai), and one of the most outspoken experts on AI policy and ethics in Norway.What is NORA.ai?
NORA is a Norwegian collaboration between 8 universities, 3 university colleges and 5 research institutes within AI, machine learning and robotics.NORA is strengthening Norwegian research, education and innovation within these fields.NORA’s ambition is International recognition of Norwegian AI research, education and innovation.NORA’s vision is excellence in AI research, education and innovation.NORA is active both in the Nordics, but also collaborating broadly on the international stage, like exchange programs for Ph.D. students, collaboration with other national institutes, contribution to eg. OECD, even contributing to shaping bi-lateral agreements, +++Why AI policy?
There is a growing concern in society about AI and its impact on our lives, how it affects elections, misinformation, our workHow can AI help us to handle information on our citizens more effectively?How does AI affect our children, their learning?There is a misconception, that we don’t have sufficient regulations for AI. Existing laws apply to AI as much as to other methods and technologies.What kind of infrastructure do we need to build in society? Is language an important infrastructure for our society?What is the public infrastructure, the public good we need to invest in as a nation?State of AI in Norway
What Government mechanisms are we going to build to handle artificial intelligence?There are three major announcements that have shaped the state of AI in Norway during the last weeks and months:The AI Billion: The Norwegian Prime minister has announced that the Norwegian Government will invest 1 billion NOK in AI over the course of 5 years.The Ministry of Defense has published their AI strategy.A new Ministry of Digitization and Governance has been established in the Norwegian Government, with responsibility of AI.Internationally there are two concerns around AI that are predominant:Security - how to ensure cyber security and reliability in models.Bias - how to tackle bias in AI systems, work with fairness and trust.We need to ensure that possibilities through AI configure to our Norwegian society.We need to think about the values we have build our society on, and how AI can support these values.Norway is earlier than most countries on actively working with regulating AI, eg. in relation to privacy.AI is about implementation - it is about trying, failing and trying again.We need to minimize possibilities for disaster, by taking learning from other countries.There need to be mechanisms to ensure that the cost of compliance with regulations is not too high.The role of Data Professionals
We would love to see data folks should take a more active role in society in regards to help everyone to understand the challenges within data and AI better.Data Management professionals can ensure safety and trust in our society going forward, and should therefore have a more active role in politics.https://www.nora.ai/
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«I think that having a very good framework, where you can put all ML and AI in, makes it much easier, much more clear. (Jeg tror det å ha et veldig bra rammeverk, der du kan putte all ML og AI inn i, det gjør at du får det mye lettere, mye mer oversiktlig.)»
Frende Forsikring, a Norwegian Insurance Company has build up a team of 6 people that work with Machine Learning (ML) and Artificial Intelligence in the company. Their goal is to ensure the companies growth through automation. Anders Dræge is the Head of the Machine Learning and Artificial Intelligence team at Frende Forsikring and he has always had an interest for data and automation.
Anders is not just an award winning Data Scientist, one of the Nordic 100 in 2023, but also a person that is happy to share his knowledge.The goal for Automation
Automation is a target that can be measured againstYou can measure both, time saving as well as saved costHigh-risk items are a good use-case to show the effect of ML: Its not necessarily about replacing work tasks, but to ensure that human focus in on the items that are of highest risk and valueAutomation is a way of scaling and growing your business, without increasing resources.The need for automation becomes more clear, and to avoid over-allocation of resources, the need is evident in the business.Your goals fro AI and automation have to be aligned with your organizations business goalsThe composition of the team
The Machine Learning team is 6 people string, consisted of2 ML engineers2 are 50% actuary (domain knowledge connection)1 data engineer that prepares data 1 MLOps developer with interest in ML to build connections with IT departmentClose collaboration with RPA (Robotic Process Automation) team and other departments.The process
The trinity of data in ML is paramount for quality results:1 set to train1 set to validate1 set to testThere are possibilities to automate testing proceduresMonitoring can and should be automatedThe technological framework
Find a framework that can control your processes, detect deviations and monitor effectively.Implementation and setting things in production is much more efficient with a proper frameworkFind a standard way of operating, will also have a positive effect on on-boarding new peopleKey factors for success
«One factor that was decisive for a very good collaboration across teams and departments is that we are very close. (En faktor som var avgjørende for et veldig godt samarbeid på tvers av team og avdelingene, er det at vi sitter veldig nært.)»Physical co-location is a success factorA lot of key competency is in-houseClear and transparent message on automationA culture that is actively striving for automation, finding ways to improveCulture is really important: People have to be receptive to the ideas of automationFind the right time to talk about automation - ideally before the need arisesHuman in the loopMonitoring of process output by humans is important for most ion the processes. This is about evaluating output with expectations from human experienceHuman evaluation becomes input for re-training of the modelThe use cases
Automatic email distributionProcessing of physical mailMonitoring of lawsFor the work Frende Forsikring has done with Natural Language Processing (NLP) for email distribution, the team won the Dataiku Frontrunner Award 2023.
https://www.frende.no/aktuelt/frende-vant-internasjonal-ai-konkurranse/ -
«There should be very little reason to say: Hey, I need a human to look these operational things for me. They are all defined as code.»
Lars Albertsson has a long career in Data and Software Engineering, including Google and Spotify. Lars is on a mission to spread the superpowers of working with data, with the vision to: «Enable companies outside of the absolute technical elite to work with data with the same efficiency or effectiveness as the technical elite companies in an industrial manner.»4 types of companies:
Born digital - Data is the basis of their business model.Born digital in a traditional market - completely natural to use data as a competitive advantage.Traditional industries «born before the internet» - big difference wether they handle information or are in the physical world.Information Handlers - Banks, Media, etc have digitalized their whole activity chain a long time ago.The differences
Significant differences in cycle-time in different industries and businesses.The only way to beat this cycle is to try out, fail fast, learn, try again.«Successful companies have been really good at failing fast.»Fast moving cultures are more effective and therefore have a better risk focus, without slowing down.To move fast in a slow moving industry, you need to choose your technology and approach wisely, keeping complexity down .Cultural slowness - «The challenge to change the way people work and people think is extraordinarily difficult.»Risk and Governance are addressed by rituals, rather then tasks.The value chain data to client outcome, needs to be anchored in a company. Have a clear picture of what this means.Getting close
Success can be measured by how close you are to the end user. The closer you get to a customer, the better the changes of success.«There is no substitute in value creation, than talking to the people you actually want to make happy.»Automation is Innovation
You need to find ways to ignite people's domain innovation capacity.Automation is a gradual process. People don’t loose their work to machines over night.Human-oversight is still really important, and there is a long journey with humans as part of the process.The focus on automation now is in knowledge workers, yet those have a different stand in society and are able to resist better, compared to the workforce during the Industrial Revolution.«If it changes quicker than one generation, there won’t be natural attrition that matches the changes in the need of the workforce.»Automated Data Management
Automating and industrializing data management processes is lower risk then software development, but still not as common.Great value to gain, from delaying simple automation processes to data management.You need to build everything from raw data to end product to find ways to automate.The raw data is the soul of the end product and the other way around. You need to keep these two outer points of the pipeline in mind, when think of data quality and data products.The limitations in Hadoop forced to work in a certain way. That way can be adopted to data management.Hadoop really pushed people in the functional Big-data patterns, that are still the basis of much of the work we are doing today.Workflow orchestration can help to know, which data you choose for a certain computation.Data Management as code is an area that is underdeveloped and under-appreciated.Minimize the technical barriers from Governance, and focus on the social aspects.Ford CEO on Software: https://www.youtube.com/shorts/HrNN6goQe50
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«How do you develop good procedures around testing or how do you drive experimentation in a product or business setting?»
Carl Johan Rising works as Director of Data at Too Good To Go, a marketplace that enables food businesses to sell their surplus food instead of throwing it in the bin.
We talked about how to form a product team, and how to rethink the role of Data Scientists in your team, shape it in an embedded team, close to domains and with expertise and customer focus. We talked about skills, product focus, business partners and much more.
«(A career in) data gives you a bit of everything.»
Data is a nice intersection between aspects of business, academia, physiological problems, and technical challenges.Business - especially understanding and decision making Academia - working with hypothesisData at Too Good To Go
If you look into how you want to use data to really drive decisions, it becomes more of a change management challenge, and not just a technical challenge«Start with a proper infrastructure foundation - a good clean data model»«Foundation building is invisible, and doesn’t by itself bring business value»The business sees the data team as one unite, without distinction between different capabilities in the team - Therefore the expectations are different«Make it very explicit what people can do and what their capacity is.» - gain understanding businessProduct Analyst:
«And soon as you have any emphasis on product, its development, its iterations, then it makes sense to have Product Analysts.»Too Good To Go works with multiple Product teams, each with their own problem specin a Product team - Product Manager, Designer, Engineering Lead, Engineers, Machine Learning Engineers, and Product Analysts embedded in the teamProduct Analysts in each team to drive good identification of problem spaces and to enable the teams to do rapid experimentationThe role will ask the "how do we drive good?" and set an experimentation agenda - driven by the Product AnalystEmphasis is on statistical knowledge and technical skills.There are two main stakeholders for Product Analyst -> Engineering Lead and Product ManagerFocus on gathering the best resources to tackle a problemWhat skills and experiences do you need in a PA role?statistical knowledgeprogrammingunderstanding of tech. aspectsability to explain results
Be a bit more explicit about what the work is and what it entailsMinimize the possible confusion between expectations on Data Scientists In a company
«I think the role of Data Scientist can mean a lot of different things.»Data Analytics Business Partner
An embedded role that is part of the business with co-ownership of the outcomesThrough this partnership it is much easier to gather context if you work with the domain -
«We (DAMA) have a role to play, (…) develop the Data Management profession ultimately for the benefit of the society.»
It is good to be back with Season 3 of MetaDAMA, and as always, we start with a DAMA-focused episode.
Nino Letteriello is one of Europes most influential data leaders, president of DAMA Italy and Coordinator for the DAMA EMEA region (Europe, Middle East, Africa). Nino started his carrier in project management, educated in civil engineering, and got involved in Data Management around 2017. Since 2019 he is the regional coordinator for DAMA EMEA.
Here are my key takeaways:
What is so special about DAMA EMEA?
Lots of passion and commitment by volunteersGiving back to society to proff how the society becomes more data literate«Whilst we are a geographical region, I see very very different scenarios, very different levels of maturity.»Middle East - Saudi Arabia:Government drove a framework, build on DMBoK for public administration This is cascading down from public agencies to the big corporations working in Saudi Arabia, and subsequently to SMEAfrican Countries show a scattered approach to Data ManagementGreat appetite for knowledge in dataThere is an enormous sense of «missing out»Mediterranean Countries and Central EuropeInitiatives of «data alphabetization» or data literacy at an early stageTeaching data management at an earlier age, eg. Program in Italy to teach DM in middel school and high school (DataHigh)NordicsDifferent level of maturity.Staring early with digital competency developmentInspirational is the nordics view on data for social good and ethical handling of dataData Literacy and awareness
Nino was involved in a WEF (World Economic Forum) study on how SMEs (Small, medium sized enterprises) are leveraging the power of dataCollected information form over 200 Small and medium sized enterprises«Interesting how many companies still consider data an IT thing, a subset of IT.»There is still an over reliance on IT, not seing data as a business problemImmature on Data Governance and formalization of rolesAwareness is not necessary followed by clear actionSMEs face the same issues as big corporations, but without the means to handle these issues accordinglySMEs have the possibility to me very agile in facing these issuesStill a lot of «reinventing the wheel» - we should use DMBok and other existing frameworks actively as a basis to work fromIs DAMA still relevant?
Importance of DAMA and DM is still large, also and especially in times of AI«Garbage in - garbage out» is still as valid as everyNew methods, new techniques, how languages, everything is dependent on the quality of the data you put inEverything starts with awarenessThe real differentiator is that data is a business asset, not an IT assetDAMA EMEA Conference
Organized for the third year in row, first time both digital and physical in Bologna November 29th - December 2nd 2023.Conference provides clear, filtered, categorized, relevant informationPossibilities to share ideas and networkClosed session for all board members in EMEA region to discuss a declaration of intents for DAMA EMEANino likes the idea to do Data Management Maturity Assessments across countries tom compare DM maturityThere is also a possible intent to work closer with European CommissionGiving DAMA EMEA a vice towards legislation makersGet more information and sign up here for the conference: https://data-emea.org
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"It's about taking a step back to ask yourself: Should we even have a data-driven system for this?" («Det handler om å ta et steg tilbake for å spørre seg: Skal vi i det hele tatt ha et data-drevent system på dette her?»)
We finish season 2 with a though-provoking episode, to maybe start som debate about data-driven public administration.Lisa Reutter is PostDoc at the University of Copenhagen connected to a project called: «Datafied Living». We talk about the importance of Social Science in Data, and how data is intertwined with our lives. Lisa is researching in the field of «Critical data and algorithm studies», at the interplay between tech, data and society.
Here are my key takeaways:
Data in Public Administration
For a modern state to function properly and to ensure citizen rights, services, security, etc is provided the state needs data.Data Management by the state for its citizens is not a new concept but has a long historical foundation.During the last years we use more, different and new data in administrative processes, and enhance technological development and a tool box to derive value from dataPublic administration has had a monopoly over management and ownership for citizen data. But this has been challenged by private companies.Data-driven systems in public sector are not there for profit, but to create value for society. Therefor they need to be build on and with the purpose to enhance our democratic values.Registers
Norway and other Scandinavian countries have established national registers to manage and administrate society.There is a reason why registers are not unified in Norway, and this is to ensure a balance of powersThe opposite example, of what can happen if a state collects information on its citizens without boundaries, is to be found in the GDR (Eastern Germany)If all data of all aspects of your life are collected one place, it is really easy to misuse this dataThrough data a state could see, predict, and control the behaviors of citizens.The public debate about data-driven
Discussions can and should be about what data are we collecting, where do we store data, what are we using data for, who could and should have access to that data, etc.Even with public debate about data use in public administration, limits and boundaries can never be defined clearly. Also because this is individual and relative to context.Datafication is a political act. The citizens need to be involved in the process of technological advancement and intelligent use of data.The debate around «data-driven public administration» in Norway, has not included the public actively.Customer-centric vs. data-as-an-asset vs. democratizing data
Is there a rhetorical ambiguity between being customer-centric and data-as-an-asset?Data democratization demands that citizens have to use their time, resources and energy to ensure that public administration is working correctly.Is making data available leading to commercial parties capitalizing on that data and building solutions, rather than creating transparency for citizens?The right education and skills are important, but it needs to be available and attainable for all parts of society.Data Literacy is an own subject that is in dispute about what it should contain.We need to understand, that this has implications on how we...
1. Trust in the state
2. Trade - what do I give my data for? What do I get in return?
3. Build in accepted ways
4. Weight opportunities against risk
5. Ensure that the responsibility for understanding does not lie with the citizen alone
6. Gain knowledge, and how everyone can get it
7. Should invite for debate
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