Editor's picks

We live in a connected world. When trying to determine the influence of health systems and physicians, simple analysis based on claims or prescription volumes isn’t enough. With a graph database, real world events in healthcare can be mapped to create unique relationships between physicians, healthcare organizations and vendors. By allowing users to quickly traverse millions of connections per second, graph databases can help commercial life-science teams derive deeper and more targeted insights.

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Graphs are hidden in data models everywhere we look from social networks and the world-wide-web to genealogy and patient journeys. While their structure may be hidden from users, we have been using them for solving various problems. This piece covers the evolution of data storage technologies leading to graph data storage we use today.

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At Compile.com, we deal with a variety of datasets both big and small. Often, there is a need to run analysis on top of 3rd party datasets that we haven’t ingested to see if it’s worth the effort. This particular dataset contained over 300 GB of historical data that contained multiple identifiers, the codes they were associated with and some … read more

For all the heavy-duty data science used by sales and marketing teams, it’s ironic that company firmographics data, that old workhorse of list generation, hasn’t really evolved. This post has a simple example of how better company intelligence, one that is analytics ready, can bubble up insights that were previously inaccessible.

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