«Don’t make it hard to understand for the business. Make it simple and clear.»

Get new perspectives on Data Governance with Valentina Niklasson from Volvo Penta as she talks about certain patterns, stages in the acceptance of Quality Management or Lean, that Data has to go through. Her rich experience in making Data Governance business-centric emerges, showcasing how you can get an organization engaged in Data.

Gain insights on the synergy between lean methodology and effective Data Management. We explore the application of the PDCA Deming circle in Data and discuss how common languages and methodologies bridge the gap between Data, IT and business. This convergence is not just theoretical; it's a practical pathway to tapping into customer insights, translating needs into strategies, and fostering a culture where continuous improvement reigns.

Finally, we delve into the human aspect of Data and Data Stewardship, emphasizing the importance of people over technology in cultivating a data-driven culture. By engaging the curious early and involving them in the development of business information models, we build ambassadors within the business, ready to champion change. Valentina and I talk about the dynamic role of Data Stewards and the approach to involving business personnel, ensuring the smooth adoption of new processes and strategies.

Here are my key takeaways:
Quality management as inspiration

• Data is still treated as an IT problem, but should really be treated as a business problem.
• We need to find a better way to communicate across data, IT and business.
• Use the same methodology wherever possible and try to reduce complexity in processes.
• Try to adapt to the ways of working in the business. Not creating own ways on digital, data or IT.
• You need to understand customer relations, end customers and the entire value chain to define needs correctly.
• Standardized ways of working can help to do right from start.
• Deming Cycle, PDCA, can be directly adopted to data. Think of data as the product you are building, that should have a certain quality standard.
• Don’t make it hard to understand for the business:
  • Using the same forms and approaches.
  • Business data driven process.
  • Let the business take part in the entire process.
• Lean Methodology should take a bigger place in data.
• A product management mindset makes data quality work easier.

Data Stewardship

• You need to ensure owning the problem as well as the solution.
• High data quality is vital for data-driven organization. Someone needs to ensure this.
• Stewardship can have a negative connotation.
• The technical demands on Data Stewards are really big today.
• Data Stewardship works if the Data Steward is part of a broader team.
• The role of Steward needs to be adjusted to the fast-speed reality.
• Data Stewards need to be able to solve problems, not only report to a central organization.
• Data Stewards should be approached in the business. You need that domain knowledge, yet they cannot perform the entire stewardship role.
• Most important to empower Data Stewards to start working and analyzing the challenges ahead.
• Don’t force Data Stewards to be technical data experts. That should be a supportive role in the Digital / data organization.
• If you build something new, engage Data Stewards from the beginning.
• You cannot take responsibility for something you don’t understand.
• If you want to be sustainable in Data, you need to help the people in your organization to be part of the journey.
• It’s not only about hiring new competency, but engaging with the knowledge you have in your organization.

«Dataen i seg selv gir ikke verdi. Hvordan vi bruker den, som er der vi kan hente ut gevinster.» / «Data has no inherent value. How we use it is where we can extract profits.»

Embark on an exploration of what a data-driven Police Force can be, with Claes Lyth Walsø from Politiets IT enhet (The Norwegian Police Forces IT unit).
We explore the profound impact of 'Algo-cracy', where algorithmic governance is no longer a far-off speculation but a tangible reality. Claes, with his wealth of experience transitioning from the private sector to public service, offers unique insights into technology and law enforcement, with the advent of artificial intelligence.

In this episode, we look at the necessity of integrating tech-savvy legal staff into IT organizations, ensuring that the wave of digital transformation respects legal and ethical boundaries and fosters legislative evolution. Our discussion continuous towards siloed data systems and the journey towards improved data sharing. We spotlight the critical role of self-reliant analysis for police officers, probing the tension between technological advancement and the empowerment of individuals on the front lines of law enforcement.

We steer into the transformation that a data-driven culture brings to product development and operational efficiency. The focus is clear: it's not just about crafting cutting-edge solutions but also about fostering their effective utilization and the actionable wisdom they yield. Join us as we recognize the Norwegian Police's place in the technological journey, and the importance of open dialogue in comprehending the transformations reshaping public service and law enforcement.

Here are my key takeaways:

• Norwegian police is working actively to analyse risks and opportunities within new technology and methodology, including how to utilize the potential of AI.
• But any analysis has to happen in the right context, compliant within the boundaries of Norwegian and international law.
• Data Scientists are grouped with Police Officers to ensure domain knowledge is included in the work at any stage.
• Build technological competency, but also ensure the interplay with domain knowledge, police work, and law.
• Juridical and ethical aspects are constantly reviewed and any new solution has to be validated against these boundaries.
• The Norwegian Police is looking for smart and simple solutions with great effect.
• The Norwegian Police is at an exploratory state, intending to understand risk profiles with new technology before utilizing it in service.
• There is a need to stay on top of technological development of the Norwegian Police to ensure law enforcement and the security of the citizens. This cannot be reliant on proprietary technology and services.
• Prioritization and strategic alignment is dependent on top-management involvement.
• Some relevant use cases:
  • Picture recognition (not necessarily face-recognition) - how can we effectively use picture material from e.g. crime scenes or large seizure.
  • Language to text services to e.g. transcribe interrogations and investigations.
  • Human errors are way harder to quantify and predict then machine errors.
• This is changing towards more cross-functional involvement.
• The IT services is also moving away from project based work, to product based.
• They are also building up a «tech-legal staff», to ensure that legal issues can be discussed as early as possible, consisting of jurists that have technology experience and understanding.
• Data-driven police is much more than just AI:
  • Self-service analysis, even own the line of duty.
  • Providing data ready for consumption.
  • Business intelligence and data insights.
  • Tackling legacy technology, and handling data that is proprietary bound to outdated systems.

«If you want to run an efficient company by using data, you need to understand what your processes look like, you need to understand your data, you need to understand how this is all tied together.»

Join us as we unravel the complexities of data management with Olof Granberg, an expert in the realm of data with a rich experience spanning nearly two decades. Throughout our conversation, Olaf offers insights that shed light on the relationship between data and the business processes and customer behaviors it mirrors. We discussed how to foster efficient use of data within organizations, by looking at the balance between centralized and decentralized data management strategies.

We discuss the "butterfly effect" of data alterations and the necessity for a matrix perspective that fosters communication across departments. The key to mastering data handling lies in understanding its lifecycle and the impact of governance on data quality. Listeners will also gain insight into the importance of documentation, metadata, and the nuanced approach required to define data quality that aligns with business needs.

Wrapping up our session, we tackle the challenges and promising rewards of data automation, discussing the delicate interplay between data quality and process understanding.

Here are my key takeaways
Centralized vs. Decentralized

• Decentralization alone might not be able to solve challenges in large organizations. Synergies with central departments can have a great effect in the horizontal.
• You have to set certain standards centrally, especially while an organization is maturing.
• Decentralization will almost certainly prioritize business problems over alignment problems, that can create greater value in the long run.
• Without central coordination, short-term needs will take the stage.
• Central units are there to enable the business.

The Data Value Chain

• The butterfly effect in data - small changes can create huge impacts.
• We need to look at value chains from different perspectives - transversal vs. vertical, as much as source systems - platform - executing systems.
• Value chains can become very long.
• We should rather focus on the data platform / analytics layer, and not on the data layer itself.
• Manage what’s important! Find your most valuable data sources (the once that are used widely), and start there.
• Gain an understanding of intention of sourcing data vs. use of data down stream
• «It’s very important to paint the big picture.»
• You have to keep two thoughts in mind: how to work a use-case while building up that reusable layer?
• Don’t try to find tooling that can solve a problem, but rather loo for where tooling can help and support your processes.
• Combine people that understand and know the data with the right tooling.
• Data folks need to see the bigger picture to understand business needs better.
• Don’t try to build communication streams through strict processes - that’s where we get too specialized.
• Data is not a production line. We need to keep an understanding over the entire value chain.
• The proof is in the pudding. The pudding being automation of processes.
• «Worst case something looks right and won’t break. But in the end your customers are going to complain.»
• «If you automate it, you don’t have anyone that raises their hand and says: «This looks a bit funny. Are we sure this is correct?»»
• You have to combine good-enough data quality with understanding of the process that you’re building.
• Build in ways to correct an automated process on the fly.
• You need to know, when to sidetrack in an automated process.
• Schema changes are inevitable, but detecting those can be challenging without a human in the loop.