A recent Forrester report finds that from 60 percent to 73 percent of all enterprise data goes unused for analytics. This statistic highlights one of the biggest challenges experienced by data scientists and organizations hoping to gain insight from their data.
As the volume of data increases, tapping its value and generating accurate reports has become a Herculean effort. Considering the many data initiatives healthcare organizations have in place, and the significant investments made, coming up short in data discovery and analytics represents a huge missed opportunity.
Familiar hurdles organizations face when using data for analytics include:
For organizations to effectively leverage data to differentiate products and services, improve decision-making and maintain competitive advantage, they need a comprehensive, enterprise-wide data strategy—one that ensures data becomes a valuable asset.
In recent years, data lakes have emerged as a viable solution to store massive amounts of data cost effectively. A data lake is centralized repository that can store an enormous amount of raw data, enabling different users to analyze it and gain actionable insight. However, despite their promise, many lakes are overflowing and organizations are struggling to operationalize this data.
Data lakes have massive scale and tremendous flexibility. They accommodate vast amounts of structured and unstructured data. And getting data into a lake is simple.
These very attributes, however, contribute to making it easy to lose track of what’s in the lake. In the rush to aggregate data somewhere, the lakes often serve as data junk drawers—a place where data is dumped for the moment, with the best intention to put it in its proper context later.
This isn’t surprising. In 2014, Gartner warned that data lakes (without the right level of governance) would be nothing more than disconnected data pools. A data lake requires a set of processes and policies around how data is collected, defined and secured. Without this kind of framework, it’s impossible to know what data is in the lake, where it came from, who owns it and its overall value to the organization.
