What is MDM?

Master data is the most highly governed set of data within an organization, touted for serving as a “single source of truth.” Master data is matched and merged in order to resolve any discrepancies between various data sources. This increases the accuracy and reporting ease of this high-value data. While MDM is still an integral part of overall data strategy, it isn’t a silver bullet for every use case. Specifically, it can be impossible or prohibitively expensive to master externally sourced, highly heterogeneous, machine-generated, or unstructured data.

How can knowledge graphs fix a reactive data strategy?

Most enterprises are stuck with reactive data management strategies. Multiple schemas, or data models, are required to manage an enterprise. This can lead to the proliferation of data within an organization, degrading data quality and causing uncertainty over which copy is the source of truth. Then, when faced with a new project that requires making existing applications speak to one another, effort is wasted on patchworks of otherwise unnecessary code.

All this leads to slow responses to questions. When unanticipated questions or needs arise, work grinds to a halt as the data preparation starts anew. This reactive data strategy leaves teams flat-footed when the market shifts or new questions arise. Enterprises require a more responsive data strategy, one that keeps pace with the needs of the business.

With a data landscape that is increasingly hybrid, varied, and changing, it’s increasingly common to augment MDM strategies with tools that can easily represent multiple versions of the truth — situational knowledge. Stardog is designed to simultaneously support different use cases, orgs, lines of business, and apps in sharing and reusing connected data. A data fabric creates a single, reusable data foundation which can power multiple applications, even if data has to be defined differently across use cases. 

What use cases can EKG support alongside MDM?

• Mapping influence networks for strategic engagement with Key Opinion Leaders (KOLs). In the pharmaceutical industry, sales have shifted from direct sales to individual doctors into a complex game where you need to influence a network of payers and providers. Knowledge graphs are particularly well suited to this challenge; they take real-world relationships and affiliations and make them machine-understandable. 
• Creating a digital twin of your operations. Translating the “physical to digital” is a particularly strong use case for knowledge graphs—and doing so makes your assets easier to manage and optimize.