Last week, Health IT Analytics ran an article about a recent IDC report that projected the volume of big data to grow faster in healthcare than any other sector over the next seven years.
While the rapid increase of data volume (driven by advancements in big data analytics tools and medical imaging along with the availability of more real-time data) adds to the opportunities for all kinds of data-driven benefits like more advanced and customized care as well as faster drug development, it also means healthcare organizations will be managing “extremely large” data assets, which is creating some big challenges.
“As the amount of data in healthcare continues to increase,” the reporter writes, “Finding solutions to these issues will only become more critical.” IDC’s report therefore recommends big investments in health IT, blockchain and analytics tools along with effective strategies for digital transformation.
A big part of the process will also involve identifying all the data these healthcare organizations are absorbing in automated fashion. This is where Waterline Data’s data cataloging solutions come in, helping customers like Kaiser Permanente and GSK use artificial intelligence to automatically tag and match the large streams of incoming data to glossary terms that in-house data analysts can then use to generate the intelligence to serve patients in new and more effective ways. Without automated data cataloging, all the data that’s pouring in simply continues to pile up, unused.
As our founder Alex Gorelik often says, “It’s like looking for a specific book you want at a flea market. You can look all you want, but it’s going to take a long time to find it.” You can invest all you want in faster data processing, faster analytics and faster response times. But if a healthcare organization can’t discover, understand and utilize their data fast enough—if they can’t quickly convert all that data into actionable business intelligence—they will have a tough time serving patients, let alone improving the health and lives of people on a grand scale.