MetaDAMA - Data Management in the Nordics

«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 takeawaysCentralized vs. DecentralizedDecentralization 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 ChainThe 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:LegalTechLegal 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 ManagementOptimizing 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 TechnologyFind 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 CDOThe 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 StrategyThe 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."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: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.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.»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: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.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.»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: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.The pitfallsSoftware 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.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 BringAn 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.Strategic use of dataHow 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.CompetencyThere 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.»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: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.Responsible AIResponsibleAI 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 ImpactCompanies 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?»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.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 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 NorwayWhat 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 ProfessionalsWe 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/

«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 AutomationAutomation 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 teamThe 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 processThe 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 frameworkFind 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 casesAutomatic 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 differencesSignificant 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 closeSuccess 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 InnovationYou 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 ManagementAutomating 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

«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 GoIf 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«I think the role of Data Scientist can mean a lot of different things.»Be a bit more explicit about what the work is and what it entailsMinimize the possible confusion between expectations on Data Scientists In a companyData Analytics Business PartnerAn 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 awarenessNino 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 ConferenceOrganized 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

"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 AdministrationFor 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.RegistersNorway 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-drivenDiscussions 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 dataIs 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 state2. Trade - what do I give my data for? What do I get in return?3. Build in accepted ways4.  Weight opportunities against risk5. Ensure that the responsibility for understanding does not lie with the citizen alone6. Gain knowledge, and how everyone can get it7. Should invite for debate

«How can we consolidate data and describe it in a standardized way?»Scientific Data management has some unique challenges, but also provides multiple learnings for other sectors. We focused on Data Storage and Operations as a knowledge area in DMBok. A topic that is often viewed as basic, often not in focus, but is a fundamental part of data operations.I talked to Nicolai Jørgensen at NMBU - Norwegian University of Life Sciences. Nicolai has a really diverse background. His journey in data started in 1983! In his free time, Nicolai spends time with photography and AI for text to image generationHere are my key take aways:Scientific Data ManagementTo describe data in a unified way, we need standards, like Dublin Core or Darwin Core for scientific data.Data is an embedded part of Science and Research - you can’t have those without data.You need to make sure you collect the right data, the right amount of data, valid data, +++You need to optimize your amount of time, energy and expenses when collecting and validating data.You need to standardize the way you collect data, to ensure that it can be verified.There needs to be an audit trail (lineage) between the data you have collected and the result presented in a publication.Data needs to be freely available for research and testing hypothesis.Data needs to be findable, accessible and interoperable, but a also reusable.ML algorithms can help extract and find changes to scientific data, that is internationally available.Describing data is key to tap into knowledge - for that you need metadata.In times of AI and ML, Metadata is still the key to uncover data.The development of AI models is a race - maybe we need to pause and get a better picture of cause and effect, and most of all risk.Standardizing InfrastructureHow can were standardize on the infrastructure for research projectsMinimize or get rid of volatile data storage and infrastructureStandardize data storage solutionsSecure what needs to be securedSplitt out sensitive or classified data and store separate (eg. Personal data)Train your end users and educate data stewardsHave good guidelines for researchers on how to store, use and manipulate data.There is a direct correlation between disc-space use and sustainability.Storage is cheap, is a correct saying, if you look at its in isolation - but in the bigger picture the cost is just moved.Just adding more storage doesn’t solve your problems, it might just yet increase them.Long-term Preservation & IntegrityTo preserve data for long-term you need to Encapsulate data at a certain levelStandardize the way you describe the dataUpload data package to a common governed platformEnclose if there is a government body that can take responsibility to preserve your data for the time necessaryEnsure that metadata is machine-readableFormats like XML provide the possibility to read the data by both machines and humans Research integrity: conducting research in a way which allows others to have trust and confidence in the methods used and the findings in that result.Ensure lineage and audit trails for your scientific data.Fake data, data fabrication, are serious issues in research - the understanding and methods for keeping data integrity at the highest possible level is not getting easier, but increasingly important.Changes to data (change logs, change data capture, etc) can be studied as well; you can build models to build scenarios around data changes.You can fetch data from other sources to enrich the quality of your data.

«Some times, you look at the problem and think it’s valuable, you spend a lot of time on it, and then you find out that this is really a NO!»Ida Haugland and I talked about the importance of Data in Shipping, about a concrete examples for success through digitalization and mainly about the focus on Product Management.Ida works as Principle Product Manager at Omny. At the time of recording Ida was part of Klaveness Digital and is presenting from her perspective from her work there. She started here career with data, while working for Customer Satisfaction for Facebook in 2010.Here are my key takeaways:Shipping & LogisticsWe are talking about really complex lines, that need to be managed. Data is the basis for everything.In Shipping and Logistics it is vital to «Put together a comprehensive plan to see cause and effect.»Shipping and Logistics is on a way to detailed insight in to their supply chain, as much as logistics for finished good.« A Lot of principles and thinking behind other products can be applied to shipping.»Carbon Emissions and the role of DataCarbon Emissions can be minimized based on data driven insights.Concrete actions based on data:Slow down - timing shipping and arrival at port with possibility to embark and birth the shipload.Use bigger vessels - more cargo, less voyages.Use the full tonnage of the vessel - fill it up to capacity.Reduce ballast distance - find vessels close to you, to reduce empty voyages.Digital Product ManagementThere is a lot of hype around product management in data and digital.Product thinking means turning your attention to the user/consumer of the product and their needs.You need to collect data, ensure quality, display its in a way that makes sense, and display it in the right context - that means products.There has been a development in product management to become more data informed and customer focused.There are still misinterpretations between product thinking and project thinking.Projects have a set scope and an end-date. Projects are defined by their constrains, on scope, time, or quality.Product development does not have nan end-date and cannot be done in isolation.In Product management you have constrains on features, not on the entire product.In Product management you can deliver a lot of value, but you don’t always know when.You can apply Project Management methods in product management.When you deliver a product you need to have your consumer, customer and end-user needs in focus throughout the entire development of that product.Separate your own opinions and preference out of the product during development.Don’t just ask what products people want, but rather elicit requirements through a controlled process, to find out what they need.A product manager needs to facilitate and get customer, designer, software engineer, etc. on the same page.Skills for a Digital Product ManagerUser empathyStorytellingElicit needsData AnalysisCross-departmental communicationAnd the Number One rule as a Digital Product Manager: «Don’t mistake your own opinion for the right opinion.»

«When technology evolves really fast, also the skills you need to hire for evolve really fast.»Within a rapidly changing environment, fueled by technology and great ideas, it can be hard to define a stable career path. So I brought in an expert on developing companies and building legacies. Pedram Birounvand has a background in quantum physics, data engineering, experience from Spotify and moved into private Equity 6 years ago. Now Pedram started a new chapter in his career as the CEO of a startup working with Data Monetization.Here are my key takeaways:Data Skills for the FutureAs a leader in the data domain, you need to be a storyteller, to tell the story about the necessity of data, like quality or governance.The Job titles for Data Scientist, Data Engineer, etc stayed consistent, but what we expect from someone with that job title changed greatly through the last yearsHard skills in data are not as important as they where for every companyMake sure you know, what you are optimizing for in your careerAre you optimizing for flexibility, self-employed consultant is bestAre you optimizing for building a legacy, be an entrepreneurAre you optimizing for leading people and see people grow, become a line managerDont become a manger if your passion is within engineering. You will need to optimize your time for coaching people, not working on problem solving as an engineer.«The technology of applying AI and ML becomes more and more simple and becomes more and more commoditized.»Dont hire Data Scientist to build models that you can buy out of the box.Don’t hire Data Scientist if you need Data Analysts. They work entirely different and the work is not comparable.If you hire a Data Scientist before having good Data Engineers, then the Data Scientist cannot create any value«In order to be successful as an engineer, you need to have a really transformative mindset.»You need to enjoy the learning process, if not focus on something else in the IT-domainAdopt an agile mindset. Agile fundamentals are key to todays work life.«You need to embrace to be able to incubate things.» Build incubator squads as soon as a good idea pops up.RecruitmentIn a small company you need to be much more flexible and broader in the way you tackle problems, than in a larger company where you can be more specializedAs a hiring manager, don’t lean too much on the titles, but make sure you understand what you need in your company. This is key to writing a good add and attracting the right talentIn a job advertisement be specific: What does it mean to be a Data Engineer in the context of your business?What is important for you as a company today, based on the trends coming?Building code has become so much simpler. Do you still need developers that need to know all the details about a certain language?Maybe a person that can be close to the business, and not so deep in programming can add more value?«You have to know what it is you are optimizing for.» If you have an extremely complicated technology stack you need deep knowledge, if not don’t hire it.In a recruitment process, focus on soft skills of rapid learners that can adjust to new situations and having an interest in understand your business use cases.«My interview secret: Share a whiteboard session with me»Try to figure out how self-sufficient a candidate can be in understanding how the business works and where to get relevant dataTest how candidates react in uncomfortable situations, with customers that are not always happy about results and solutions.Look for candidates that show resilience in new and uncomfortable situationsCareer models should be technology agnostic

«Infants with guns!»Are we mature enough to track, collect and handle data responsibly, according to ethical standards? I talked with Director of Data Innovation at IIH Nordic Steen Rasmussen about the Business impact of Data Ethics.Here are my key take aways:If were track, collect, and keep all data for any random opportunistic purpose, we put your companies at risk.This includes a «commercial curse» of budget-heavy tracking and budget-light management and business value creation through data.ROT, Norwegian for clutter, is an acronym for Redundant, Obsolete, Trivial - the dat that clutters your way to find valuable data.Collection and tracking of data is still too much dependent on people: If there is a change in personal, you get situations where new people «clutter the clutter»Marketing & SalesFor many companies it was Marketing & Sales that drove the data-driven agenda.The big value of Marketing & Sales is to add the market-dimension to the data.You can actually relate your product to the market, ship to where the market is.Analyzing market data is «putting a fixed entity on a moving target». The market changes to rapidly to provide good analysis.The more you push behavioral forecasting into the future, the bigger your uncertainty.Business value & EthicsCorporate irresponsibility is an issue.Sometimes we get involved in a projects for the projects sake.Data projects have for a long time been theoretical, so the impact was not visible.Chat-GPT is a black box. Should we really give it more firepower, if we don’t know how it works?The market determines that there is a business value in being first.The speed of innovation doesn’t give time for reactive regulatory bodies to regulate efficiently.Companies need data ethical guidelines to say, how they will, shall and can use data.Who should define data ethical guidelines in a company? It is still done on a user-level, whilst senior management is looking at market situations and weighting them against ethical guidelines.We need regulatory and top-level guidelines that cannot be bent according to market situations.«Ideally, but highly unlikely we need a global set of data ethical guidelines.»The more trustworthy you are as a company, the more relevant data is shared with you.With that trust and data, you can understand the market better than companies that are not trustworthy and basically flying blind.Personal Data Literacy is important, and we need basic digital skills in our society.There is also a lack of understanding, when it comes to measures set in place for peoples benefit, eg. Cookie-banners.We are still lacking good privacy approved alternatives to the tools we are using on an everyday basis.Everyone has to follow ethical guidelines. We cannot have a DarkOps department in our company.Data Ethics guidelines should be something everyone can refer to.Ask yourself: What is the minimum of data we require to collect? Anything else should become an ethical question.Data Protection Laws:There is a difference between the interpretation of regulations in the EU.Nordic countries interpret law relatively, is this just, fair, reasonable?Southern European countries use a more napoleonic or dogmatic approach, where «the law is the law, and the law must be obeyed.»Both Chat-GPT and Google Analytics have been handled differently by data protection authorities.Data Protection Authorities generalize to much, and don’t look at differences in technology.Is a strict, generalized interpretation creating panic, for users of eg. Google Analytics?