Regardless of the amount and size of the data an organization possesses, it's becoming readily apparent that the business value of that data is increasing with each passing day.
In fact, a new survey of 476 senior executives conducted by the Intelligence Unit of The Economist on behalf of NTT finds that 60 percent of organizations are already generating revenue from their data. Just as significantly, 83 percent report that they are leveraging data to make existing products and services more profitable.
But that’s just the tip of proverbial data iceberg. Rapid advances in analytics, machine learning, and artificial intelligence (AI) are making business data more valuable with each passing day. As a result, IT service providers need to have a serious conversation about how they are collecting data not only in the context of how it is being applied today but how it will be applied going forward.
The future of data storage
In the months and years ahead, new classes of applications will leverage these technologies to automate a wide variety of business processes. Awareness of that potential is already changing everything from the amount of data that organizations collect to how that data is stored.
For example, the rise of the cloud and open source platforms such as Hadoop make it feasible to store more data than ever. But rather than simply archiving that data in case someone needs it one day, many organizations are now moving to an “active archive” approach that makes that data more readily accessible to applications.
In fact, many of the Big Data applications that organizations are now developing require them to provide access to massive amounts of historical data, which is leading many of them to put data that had been stored offline back online in one form or another.
For all the talk about Big Data, however, the key issue is not the amount of data. It’s making sure the right data gets applied to the right application. Many of the advanced analytics applications that organizations are building depend on statistical models that can draw the wrong conclusion in the absence of quality data. Just as important, many of those applications are only valuable when they are used to correlate external data sources with the internal data an organization already has. For that reason alone, many organizations will be looking to IT services providers to help them put all that data in appropriate context.
Routinely implementing machine learning applications and eventually full blown AI systems inside a production IT environment is still a ways off. But sooner than most people realize, advanced applications will be using data to transform almost every aspect of the way organizations use information. The opportunity and the challenge facing IT service providers now is to help customers get in a position to take advantage of these new advances tomorrow by making sure that best data management practices are being implemented today.