From remote monitors to wearables to the array of IoT devices that seems to be growing daily, the amount of unstructured data health IT managers are being asked to manage is exploding. This diversity of data types is creating islands of data not easily applied to operational use, and without a mechanism to collect, analyze and apply the results to operational systems, much of the value is lost.
As Bill Peterson senior director, industry solutions, for enterprise software provider MapR Technologies, views the ever-expanding data landscape, “what’s needed is a modern global data fabric that allows organizations to use data regardless of its location or format.”
Peterson’s recent commentary extends well beyond healthcare, and in it he explained that “data fabric refers to technology that supports the processing, analysis, management and storage of disparate data. This includes data in files, tables, streams, objects, images, and even edge or sensor data, all of which can be accessed using different standard interfaces. Through the data fabric, applications and tools can access data through a number of user interfaces . . . This data fabric both modernizes an organization’s data management strategy and unlocks the business value to transform day-to-day operations.”
There are, Peterson points out, many roadblocks to mining value from data. “You likely have to move the data from one place to another, which requires the manual intervention of an IT administrator. Then numerous access points and permission requirements must be navigated to access that data. At any point, this process can break down, resulting in delays and disruptions to your business.”
So how does one maintain an effective “data fabric,” especially as organizations move their IT systems into multi-cloud set-ups, thus potentially spreading relevant data across multiple locations?
First, Peterson says, you need “continuous coordinated data flows,” a category heading that “refers to the ability to coordinate new analytic data with historical data.” Next, simply put, is “location awareness, (with which) you always know exactly where the data is and where it originated within your data fabric. Unlike data stored in a data lake, location awareness gives your data context and lineage. Knowing the data’s point of origin, what it refers to and where it falls in the hierarchy of data gives value to your data and allows you to mine insight from it.”
Finally, you need “global strong consistency,” which is the “consistency of data within the cluster from a time, availability and lineage point of view, (and) ensures that your data is accurate and available regardless of where it is located globally within your organization.”
With a global data fabric, Peterson says, “as long as you are authorized to access the data you can run a query on data anywhere in the world without having to move it and without even having to know where that data is. . . . The data fabric and the underlying file system give you the ability to do this, providing transparency not only to the end user but to the application as well.”