What Is Data Modeling?

As the value of data and the way it is used by organizations has changed over the years, so too has data modeling. In the modern context, data modeling is a function of data governance and intelligence, allowing organizations to align data assets with the business functions they serve.

While data modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to accelerate and ensure the overall success of data governance in any organization. With the right approach, data modeling promotes greater cohesion and success in organizations’ data strategies.

Put simply, organizations rely on data modeling to see a clear picture of their data and get the most impact from it in an organized, easy-to-visualize manner. This in turn supports better decisions, drives more robust application development, helps the organization stay compliant with data regulations, and powers innovation. Enterprises that want to advance artificial intelligence (AI) initiatives, for instance, won’t get very far without quality data and well-defined data models.

For decades, data modeling has been used to define, categorize and standardize data, so it can be leveraged by information systems. This is more important than ever in a modern data landscape, where data can be structured or unstructured and can exist on-premises or in the cloud. In the face of massive volumes of data, automatically generating data models and database designs is a cost-effective way to increase efficiency and reduce errors while raising productivity across the board.

Of course, different organizations have different needs. For some, the legacy approach to databases meets the needs of their current data strategy and maturity level. For others, the greater flexibility offered by NoSQL databases makes NoSQL databases – and by extension, NoSQL data modeling – a necessity. Bringing data to the business and making it easy to access and understand increases the value of data assets, providing a return on investment and a return on opportunity. But neither would be possible without data modeling providing the backbone for metadata management and proper data governance.

The three defining properties of big data are known as “the three Vs.” These describe the volume (amount), variety (type) and velocity (speed at which it must be processed) of data. Data’s value grows with context, and such context is found within data. That means there’s an incentive to generate and store higher volumes of data.

Typically, an increase in the volume of data leads to more data sources and types. And higher volumes and varieties of data become increasingly difficult to manage in a way that provides insight.

Without due diligence, the above factors can lead to a chaotic environment for data-driven organizations.

Therefore, the right approach to data modeling is one that allows users to view any data from anywhere – a data governance and management best practice we dub “any-squared” (Any²).

Organizations that adopt the Any² approach can expect greater consistency, clarity and artifact reuse across large-scale data integrations, master data management, metadata management, big data and business intelligence/analytics initiatives.