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The Biggest Problem with Managing Unstructured Data & What To Do About It

Paul Boal
A Deluge of Unstructured Data
The digitization of healthcare is bringing in a flood of data, creating a daunting data management task for all stakeholders within the industry. Dean and professor Stefano Bertozzi of the UC Berkeley School of Public Health describes this digital shift telling his students, “when I was a graduate student, data was extensive and analysts were plentiful. Now, data is ubiquitous and the bottleneck is our analytic capacity.” What makes the matter more complicated is that Gartner estimates (gated) that as much as 80% of enterprise data is unstructured. The National Center for Biotechnology Information similarly found that “the majority of data in health care is unstructured…” and “...is often fragmented, dispersed, and rarely standardized.”

To make matters even worse, as storage costs fall many organizations are keeping ahold of data with a poorly developed, “just in case” strategy that negatively influences organizational culture and behavior. Like an obsessive hoarder, organizations end up with even more data from which they derive even less value. The belief is that “since I have the data stored, I don’t have to think as much about the data.” Thinking about the data, though, is exactly what organizations need to be doing more of. What you end up with is large stacks of data that are excluded from your analytics, making you less competitive than those who are managing their unstructured data to drive key business decisions.

Failing to process and apply structure to unstructured data and leave it in an unmanaged state is a huge missed opportunity. Managing unstructured data can be challenging but when done properly the unstructured and structured data can be integrated to deliver a newly enhanced data set, providing analysts with a more comprehensive view of operations and performance.

Unstructured Data Analytics

Unstructured data is nearly always human-generated and therefore needs a higher level of analytic sophistication to create real business value. Our conversations, for example, can be captured and processed using natural language processing (NLP), converted into a variety of more structured formats (remember diagraming sentences in seventh grade?), tagged with metadata, analyzed for sentiment and stored in a relational format that exposes this data to your analytical models in real-time. This can be done with other sources like handwritten information, scanned documents and IoT/device data and tied back to the structured data in neat columns and rows.

Integrating unstructured data into your data analytics strategy allows analytical insights and business opportunities to emerge which would otherwise go unnoticed and unexplored. Take advantage of this opportunity to improve your data-driven decision-making by managing unstructured data. Be sure to reach out to us here if you’d like to talk more about how you can incorporate your unstructured data and reach your business goals.