I often get asked — how does MarkLogic help with analytics? Where does MarkLogic fit in the analytics landscape?

Before I answer that question – let’s look at what happens today.

The world of Artificial Intelligence (AI) and cognitive computing is all about extracting information and relationships from piles of data. The insights (or signals) are typically buried in very particular parts of the data that can be narrowed by the “features” of interest. A good example of this is a life

Polystructured data holds great promise for life sciences as it brings new insights to bear on key business challenges. But delivering on this promise takes a robust data strategy to connect petabytes of clinical, regulatory, and real-world data in order to solve these critical challenges. Enter metadata.

Metadata is simply “data about data”—data that describes, explains, locates, or in general makes it easier to find an information resource. Metadata can be structural

Healthcare organizations have started to think about data and the role that it should play in achieving business objectives in new ways. In fact, for many successful healthcare organizations, the (once bright) line between data strategy and business strategy has become blurred. With healthcare data projected to grow to a staggering 25,000 petabytes by 2020—for context, just one petabyte can hold 500 billion pages of standard printed text—developing a flexible, comprehensive data strategy

Breaking down data silos and developing an enterprise data layer or hub is fundamental to achieving any one of these strategic initiatives and use cases that are essential to our ability to innovate, modernize and survive.

That was the prevailing insight industry leaders shared at a recent evening event held during AHIP 2016 in Las Vegas. Fifteen executives from organizations representing market leading payers, providers, health IT vendors and consultants, with titles that ranged

A short time ago, I had a discussion with some architects working for one of the world’s largest health systems. They were interested in aggregating their myriad silos of medical registries (healthcare data) and wanted to accomplish this integration by converting all their data into more than 100 billion semantic triples. Here is what we proposed.

When I started on the project, the organization’s architects decided they would use semantic triples to relate petabytes of data across silos. There