The time has passed when data models could hide in an ivory tower (IT) and be locked behind bars, accessible only to the IT in‐crowd. It is their data, so the data models are theirs as well. For business users, a data model is the Rosetta Stone to understand the data correctly. This new situation changes how we develop, maintain, and manage data models. Agile data modeling is no longer optional—it is essential.
Everything about data has changed. For example, we are living in the big data era now. We introduced new data storage technologies, such as Hadoop and NoSQL. Self-service business intelligence has become the preferred approach to analyze data.
Data has evolved from a simple reporting source for administrative tasks to a critical asset for many lines of business. With the right data, organizations can optimize business processes, improve customer relationships, and differentiate themselves from the competition.
As a result, the dependency of organizations on data has intensified. Data has changed, and it has transformed organizations.
But without a data model, data is not precious to an organization. A data model describes what data means, what the relationships are, and what the characteristics of data are. There was a time when experts in white coats wearing soft cotton gloves carried around data models. That time is long gone.
As data becomes more valuable in business, data models are becoming relevant. Business users are accessing and integrating data themselves and do not wait for a business intelligence competence center anymore, and so they need access to the data models. They need to know what the data they are accessing means, and what all the rules are that apply to the data.
Business users have become involved in the development, maintenance, and management of data models. With this, data models are moving to the dynamic business world. We should no longer see data models as frozen documents, or as models that are cast in concrete, whereby only specialists can change and extend them.
Today, data models have become dynamic sources of information to understand data, and this requires a dynamic approach to data modeling. This means that organizations have to adopt agile data modeling, which, as shown, is not an option, but essential.
Read the 24-page whitepaper “Agile Data Modeling: Not an Option, but Essential” by Rick van der Lans to learn more about the key requirements for agile data modeling: Data storage agnostic, collaboration, business glossary, and flexible data model.
