Survey: Spending On Operationalizing Data Set to Double

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Big data investment in enterprise is growing quickly, but expected returns on that investment are growing even more dramatically.

A survey of 500 IT decision makers conducted by the market research firm Vanson Bourne on behalf of SnapLogic, a provider of a self-service integration framework, suggests that while a lot of organizations intuitively understand that data is the new oil, not many of them are especially good at refining it just yet.

The survey finds that on average organization expect to generate a 547 percent return on their data investments, increasing revenue by an average $5.2 million by using data more effectively. But the survey also finds that on average organizations are using only half (51%) the data they collect or generate, and data drives less than half (48%) of the decisions being made.

To rectify that issue the survey finds organizations will on an average spend $1.7 million to operationalize data over the next five years, which is more than double what they currently spend.

See also: 5 tips to turn your data to your competitive advantage

A big driver of that investment increase is digital process transformation. A full 98 percent of respondents say their organization is either in the process of implementing some form of digital process transform or are planning to initiate one, the survey finds. The survey also notes that one four percent of respondents says their organization is ahead of schedule on those projects.

A total of 80 percent of respondents say outdated technology holds their organization back from taking advantage of new data-driven opportunities and only 29 percent have complete trust in the quality of the data their organization relies on.

A lot of executives appear to be hoping that artificial intelligence (AI) will resolve these issues. A full 83 percent of respondents see potential in AI to help tackle their data challenges, with 27 percent saying they are already investing in AI and machine learning technologies. Another 56 percent plan to invest in AI technologies. An area where AI technologies such as machine learning algorithms will help their organization include automating data analysis (82%), data preparation (73%), software development (66%), and application integration (63%).

Interest in all things AI is rapidly rising across the enterprise as organizations begin to view AI as a more strategic imperative, notes SnapLogic CEO Gaurav Dhillon.

“AI has become a ‘go for the jugular’ issue,” says Dhillon.

The degree to which everyone within an organization appreciates the complexities associated with developing and maintaining the AI models required to drive advanced applications is debatable. But Dhillon says the need to deliver better customer experiences is driving AI investments. The challenge many organizations initially face is figuring out how to aggregate all the data required to drive those algorithms in a single repository to which the algorithms can be applied. Machine and deep learning algorithms require access to a massive amount of data to be effective.

It may take a while for AI to become pervasive across the enterprise. But it’s apparent that organizations are finally starting to treat the data that will drive those applications as a precious resource. The challenge now is collecting enough of that precious resource to drive a refinery big enough to turn all that data actual actionable intelligence.

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