Data from sources like social media, along with powerful analytic software, are giving companies insights into customer buying habits that never before were possible, says Raj Devarajan of Symphony Analytics. He discusses this and other implications that big data holds for supply chain management.

Until recently, projects were largely initiated by and implemented for IT departments, rather than business, and the focus was primarily on eliminating silos and moving from departmental to enterprise programs. That focus has shifted and the shift has led to a $14bn global market for business intelligence software in 2013"”one expected to eclipse $17bn by 2016, according to Gartner Group estimates.

While the press and many companies continue to marvel at the sheer volume of data being generated and captured in the internet era, forward-leaning corporations have already recognized that big data's transformative potential to generate value in both the digital and physical realms will go largely unrealized unless it's complemented by speed. Fast data can unlock the value-creating power of big (and small) data.

Over the past 30 years, most companies have added new C-level roles in response to changing business environments. The chief financial officer role, which didn't exist at a majority of companies in the mid-1980s, rose to prominence as pressures for value management and more transparent investor relations gained traction. Adding a chief marketing officer became crucial as new channels and media raised the complexity of brand building and customer engagement. Chief strategy officers joined top teams to help companies address increasingly complex and fast-changing global markets. Today, the power of data and analytics is profoundly altering the business landscape, and once again companies may need more top-management muscle.

ABI Research forecasts that the M2M analytics industry will grow a robust 53.1% over the next 5 years from $1.9bn in 2013 to $14.3bn in 2018. The forecast includes revenue segmentation for the five components that together enable analytics to be used in M2M services: data integration, data storage, core analytics, data presentation, and associated professional services.