Why it’s important to operationalize big data into daily tasks

Big data analytics can do more than just deliver reports to decision makers. It can help with a company's day-to-day work, too.

Big data analytics is no longer a nice thing to have for enterprises: It's now mission-critical.

In 2019, Veritas said, "In just a few years, big data has advanced from scattered experimental projects to achieve mission-critical status in digital enterprises, and its importance is increasing. According to IDC, by 2020, organizations able to analyze all relevant data and deliver actionable information will earn $430 billion more than their less analytically oriented peers. Big-data analytics, once performed on an occasional basis, are now performed daily at many enterprises, including, Amazon, Walmart, and UPS." 

Yet organizations continue to experience difficulty in trying to operationalize it. 

Gartner defines big data operationalization as, "the application and maintenance of predictive and prescriptive models. Both clients and vendors are placing an emphasis on the importance of moving data science out of a prototype environment and into a state of production and continuous improvement." 

In other words, to operationalize big data, you have to move it out of the test sandbox and into an active role in the business.

The most active roles for big data in the business to date have been in decision support. 

All of these examples illustrate a first tier of big data analytics deployment in that they use unstructured big data and their role is in providing static reports to managers that can be acted upon.

However, when you fully operationalize analytics, there is also a second-tier active stage of engagement in which companies embed big data analytics directly into the daily workflows of their operations. In these instances, the analytics continue to inform decisions but they also automate certain tasks in company workflows based upon the intelligence they glean from data.