"Vi modellerer for å forstå, organisere og strukturere dataene." / "We model to understand, organize, and structure the data."

This episode with Geir Myrind, Chief Information Architect, offers a deep dive into the value of data modeling in organizations. We explore how unified models can enhance the value of data analysis across platforms and discuss the technological development trends that have shaped this field. Historical shifts toward more customized systems have also challenged the way we approach data modeling in public agencies such as the Norwegian Tax Administration.

Here are my key takeaways:
Standardization

• Standardization is a starting point to build a foundation, but not something that let you advance beyond best practice.
• Use standards to agree on ground rules, that can frame our work, make it interoperable.
• Conceptual modeling is about understanding a domain, its semantics and key concepts, using standards to ensure consistency and support interoperability.

Data Modeling

• Modeling is an important method to bridge business and data.
• More and more these conceptual models gain relevance for people outside data and IT to understand how things relate.
• Models make it possible to be understood by both humans and machines.
• If you are too application focused, data will not reach its potential and you will not be able to utilize data models to their full benefits.
• This application focus which has been prominent in mainstream IT for many years now is probably the reason why data modeling has lost some of its popularity.
• Tool advancement and new technology can have an impact on Data Management practices.
• New tools need a certain data readiness, a foundation to create value, e.g. a good metadata foundation.
• Data Modeling has often been viewed as a bureaucratic process with little flexibility.
• Agility in Data Modeling is about modeling being an integrated part of the work - be present, involved, addressed.
• The information architect and data modeling cannot be a secretary to the development process but needs to be involved as an active part in the cross-functional teams.
• Information needs to be connected across domains and therefore information modeling should be connected to business architecture and process modeling.
• Modeling tools are too often connected only to the discipline you are modeling within (e.g. different tools for Data vs. Process Modeling).
• There is substantial value in understanding what information and data is used in which processes and in what way.
• The greatest potential is within reusability of data, its semantics and the knowledge it represents.

The role of Information Architect

• Information Architects have played a central role for decades.
• While the role itself is stable it has to face different challenges today.
• Information is fluctuant and its movement needs to be understood, be it through applications or processes.
• Whilst modeling is a vital part of the work, Information Architects need to keep a focus on the big picture and the overhauling architecture.
• Information architects are needed both in projects and within domains.
• There is a difference between Information and Data Architects. Data Architects focus on the data layer, within the information architecture, much closer to decisions made in IT.
• The biggest change in skills and competency needs for Information Architects is that they have to navigate a much more complex and interdisciplinary landscape.

Metadata

• Data Catalogs typically include components on Metadata Management.
• We need to define Metadata broader - it includes much more than data about data, but rather data about things.