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.
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.