«We are going to treat our data at the highest level, making sure that we can use it as a competitive advantage. Then it’s a strategic choice.»

Unlock the strategic potential that lies at the heart of Data and AI with our latest discussion featuring Anna Carolina Wiklund from IKEA. Embark on a thought-provoking journey with us as we dissect the significance of robust strategies in shaping digital landscapes. From the role of data as the lifeblood of digital commerce to the ways it can radically alter customer behavior, this episode promises insights that redefine the boundaries of e-commerce and digital merchandising.

 We explore the complex interplay between business, digital and data, revealing how the alignment of strategies across various organizational levels can forge a path to  business impact. Learn how a coherent vision can transform not just marketing strategies, but also those of HR and other departments, and the critical importance of shifting from output to outcome-focused objectives to measure success.

Finally, we navigate through the evolution of strategy in the face of AI's relentless march, examining the essential need for agility and visionary thinking to keep pace with a rapidly transforming arena. This episode is a masterclass in instilling a culture of excellence, accountability, and collaboration that can propel companies forward. With real-world examples and actionable insights, we offer a clarion call for businesses to reassess and adapt, ensuring that their strategies are not just surviving, but thriving, in the AI era. Join us and fortify your strategic acumen for an increasingly digital future.

My key takeaways:

• «When we talk about product mindset its all about how we work as a team.»
• It is important to ensure aligned autonomy, when working in a compartmentalized organization with product management. You are delivering a piece to the totality.
• «Now, we need to have an adaptive Strategy everywhere.»
• Digital is the totality, the ecosystem that you are creating. Data has to flow in that ecosystem.
• There is no digital without data, but there is data without digital.
• People are coming and going within your company, and are bringing data along.

One Strategy

• The goal of strategy is to create one clear direction for the company.
• If you have multiple strategies, you will pull people in different directions.
• Break down strategies in where you deliver the value.
• Organizational models and actual value creation do not always overlap.
• There are transversal strategies that stretch throughout the entire organization (eg. HR, product), whilst there are specific strategies that strive towards one goal (eg. marketing).
• You can no longer afford to have business and digital separated.
• Digital tools do not deliver any value unless they are part of a process and used by the business.
• Ensure that you measure that matters, what is the value that you are creating.
• You need to work on a mindset for the totality of the organization, not a digital vs business mindset.
• OKRs can help to get that forward leaning mindset and to become more process oriented.
• The strategic part is really the choices you have, while plan is the actions you take towards these choices.
• A plan is about creating transparency in the company, so everyone understands what they are delivering and how it fits together.
• You need to have a goal to work towards. Your Strategy is laying out the logic to get there.
• «Culture eats strategy for breakfast»

"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 pitfalls

1. Software Development Lifecycle vs. Data Lifecyle – they overlap, but there are clear differences, especially in the late phases.
2. Feature-driven (or functionality-driven) vs. Data-driven: Is there a problem with software engineering mindset in data?
3. Hypothesis - Data Science vs. Engineering mindset: Explorational vs. structural thinking can cause friction
4. Environmental 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 Bring

• 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.

Strategic 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:

1. Establish distributed ownership of data and data products
2. Increase the amount of self-service.
3. Build competency tailored to your user groups needs.
4. 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:

1. How to recruit new and needed competency?
2. How to train and share competency internally?
3. 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:

1. Missing out!
2. 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 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:

1. Decide as an organization on your core principles / value - how may they be challenged by AI?
2. Define your principles / values for AI - these should be AI specific, but adopted to your setting, concerning risk, portfolio, etc.
3. Make these principles / values actionable.
4. Seek endorsement
5. Build a Governance structure
6. Ensure training and awareness

Positive 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?»

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 work
• How 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:

1. 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.
2. The Ministry of Defense has published their AI strategy.
3. 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:

1. Security - how to ensure cyber security and reliability in models.
2. 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/

«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 against
• You can measure both, time saving as well as saved cost
• High-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 value
• Automation 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 goals

The composition of the team

• The Machine Learning team is 6 people string, consisted of
  • 2 ML engineers
  • 2 are 50% actuary (domain knowledge connection)
  • 1 data engineer that prepares data 
  • 1 MLOps developer with interest in ML to build connections with IT department
• Close 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 train
  • 1 set to validate
  • 1 set to test
• There are possibilities to automate testing procedures
• Monitoring can and should be automated

The 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 framework
• Find a standard way of operating, will also have a positive effect on on-boarding new people

Key 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 factor
• A lot of key competency is in-house
• Clear and transparent message on automation
• A culture that is actively striving for automation, finding ways to improve
• Culture is really important: People have to be receptive to the ideas of automation
• Find the right time to talk about automation - ideally before the need arises
• Human in the loop
• Monitoring of process output by humans is important for most ion the processes. This is about evaluating output with expectations from human experience
• Human evaluation becomes input for re-training of the model

The use cases

1. Automatic email distribution
2. Processing of physical mail
3. Monitoring of laws

For 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:

1.  Born digital - Data is the basis of their business model.
2. Born digital in a traditional market - completely natural to use data as a competitive advantage.
3. Traditional industries «born before the internet» - big difference wether they handle information or are in the physical world.
4. 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