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Agile Data Modeling: Not an Option, but Essential

The time has passed when we can tuck away data models in an ivory tower (IT) and locked behind bars only for the IT in‐crowd to be consulted. It’s 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. It means that agile data modeling is not an option anymore, it’s essential.

Everything about data has changed. For example, we are living in the big data era now. We have introduced new data storage technologies, such as Hadoop and NoSQL. And self-service business intelligence has become the preferred approach to analyze data. Data has turned from a simple reporting source for administrative tasks to a critical asset for many lines of businesses. With the right data, organizations can optimize their business processes, improve their customer relationships, and differentiate themselves from the competition. With that, the dependency of organizations on data has intensified. Data has changed, and it has changed the 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. With the increasing business value of data, data models are on the mind of business users. While there was a time when only experts in white coats wearing soft cotton gloves touched data models. That time is long gone. 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’re 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 see data models as frozen documents, or as models that are cast in concrete, which only specialists can change and extend. 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.

This whitepaper describes the following key requirements for agile data modeling:

  • Data storage agnostic
  • Collaboration
  • Business glossary
  • Flexible data model

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