Ovum forecasts big data software to grow by 50%
Date: Thu, 07/30/2015 - 12:41 Source: Ovum press department
Tom Pringle, Ovum
Image credited to Ovum
Practice Leader, and co-author of the report, Tom Pringle, said, “The experimental era of big data is coming to an end, organizations are formalizing their use of big data technology to realize the business value they expect to find.”
The report highlights the fact that while big data software in 2015 is just a small part of the overall market for information management, it is set to increase at a CAGR of 50% through 2019 and play an increasingly important role that will position big data analytics as a core capability for many enterprises by 2019.
According to Pringle, “big data, as an open source technology, has been accessible without creating huge financial impact on the market. Ovum believes that situation is changing, with commercial Hadoop distributions and a fast-growing ecosystem of enabling and extending technologies pointing toward a bright future for big data.”
The report suggests that the overall market for information management software is growing at a significant CAGR of 11%. Aside from the major impact of big data’s growth, business intelligence (BI) and analytics are also strongly contributing to growth – close to doubling by 2019, from a market size of US$15.85bn in 2015. “Self-service BI, enabling a whole new universe of users, is driving the expansion of the market,” said Pringle, “With easier to use self-serve tools becoming mainstream, and moves to the cloud and mobile providing accessibility, barriers to growth in this market are being eroded.”
Day-to-day data management remains the largest part of the market, according to the report, accounting for in excess of 40% of total spend at US$24.1bn in 2015. “Even as the biggest dollar spend on information management, data management continues to grow strongly, with a CAGR through 2019 of 9%. This expansion speaks not only to the growth in data volumes, but particularly the newer challenges of managing a much more heterogenous range of data types and the speed with which those data are generated,” concluded Pringle.