Big Data confusion slows BI spending

Big Data to blame for soft analytics market

Big Data confusion will constrain spending on business intelligence and analytics software to single digit growth for the next two years, according to Gartner.

However, the CIO focus on BI looks set to continue and Gartner has mapped out the progress of the proliferation of Big Data, giving key predictions until 2017.

Gartner said that the benefits of fact-based decision-making are clear to business managers in a broad range of disciplines, including: marketing, sales, supply chain management, manufacturing, engineering, risk management, finance and HR. Managing vice president, Ian Bertram, said major changes were imminent in the world of BI and analytics including the dominance of data discovery techniques, wider use of real-time streaming event data and the eventual acceleration in BI and analytics spending when big data finally matures.

"As the cost of acquiring, storing and managing data continues to fall, companies are finding it practical to apply BI and analytics in a far wider range of situations," he said.

But despite the strong interest in BI and analytics, confusion around big data is inhibiting spending on BI and analytics software.

Until 2016, service providers will garner business by closing the gap between available big data technology and business cases.

As big data matures and more packaged intellectual property is available, big data analytics will become more relevant, mainstream and, ultimately, hugely disruptive. Recent Gartner surveys show that only 30 percent of organizations have invested in big data, of which only a quarter (eight percent of the total) have made it into production.

This leaves room for substantial future growth. Analytics plays squarely into the big data trend, where the growing volume, velocity and variety of data create opportunities outside of the traditional, established BI domains and buying centers. However, that also makes the sourcing of analytics bigger and more technically complex than what has been done before. Paradoxically, the confusion that surrounds the "big data" term and the uncertainty about the tangible benefits of big data are partially to blame for the soft BI and analytics market.

Procurement cycles have slowed while budget holders try to match the right tooling to the right business case.

In the interim, BI and analytics continue to remain at the forefront for CIOs, and service providers will attempt to bridge much of the confusion.

The gap will completely close when those services will become targeted and packaged.

This, in addition to a confluence of technology maturity cycles, is expected to occur around 2016.

Beyond 2016, when the discussion has matured from technology to business, and when there will be more off-the-shelf capability available, big data analytics will pervade almost everything that we do, helping push society unequivocally into the digital age. But by 2015, the majority of BI vendors will make data discovery their prime BI platform offering, shifting BI emphasis from reporting-centric to analysis-centric. Over the past several years, the BI platform market has grown largely through companies investing in IT-led consolidation projects to standardize IT-centric BI platforms for large-scale systems of record. These have tended to be highly governed and centralized, where IT production reports were pushed out to managers and knowledge workers.

Gartner predicts companies will shift their future investment away from IT-developed reporting solutions toward business-user-led analysis solutions. IT will focus most of its effort on data modeling and governance. As a result, data discovery will displace IT-authored static reporting as the dominant BI and analytics user interaction paradigm for new implementations by 2015. Bertram said BI leaders should scrutinize the road maps of both data discovery and IT-centric vendors to determine their suitability to meet growing business user and enterprise requirements.

"It's important to acknowledge that one size rarely fits all," he said. By 2017, more than 50 per cent of analytics implementations will make use of event data streams generated from instrumented machines, applications and/or individuals. According to Gartner, as enterprises continue to recognize the economic value of information, and see the opportunity to capture and apply ever greater volumes of detailed data, they will come to expect access to analytics technologies capable of making sense from event streams.

This goes beyond traditional and mainstream BI to a breed of technologies capable of producing autonomous insights and inferences quickly. To produce and harvest this data from physical assets and other event sources, the market will expand for flexible, multipurpose sensors for temperature, humidity, vibration, pressure, sound, light/color, electrical or other utility flows, motion, facial expressions, voice inflection, health monitoring and other systems.

Moreover, such event data from physical assets is sometimes combined with event data from administrative information systems to develop richer, more powerful holistic systems.

Several SaaS application vendors in particular, have already intensified their ability to collect more usage data and are devising quid-pro-quo arrangements with customers that allow leveraging their de-identified data for alternate commercial purposes.

Bertam said nontech business leaders should create an inventory of the range of current data collected by their products and services, then consider what additional high-value information could be captured through further instrumentation.

"Application and other technology managers should ensure that the data collected from IT systems, applications, devices and users is maximized with equal consideration for performance implications and probable future business relevance." According to Gartner, by 2017, analytic applications offered by software vendors will be indistinguishable from analytic applications offered by service providers.

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Tags Ian BertramGartnerManaging vice presidentbig databusiness intelligencesoftware analytics

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