Big data analytics are a leading area of interest for companies investing in cloud storage solutions. Business intelligence has proven instrumental in driving revenue, making better decisions and improving brand awareness, but enterprises need cost-effective, high-performing cloud setups in order to make it feasible.

The types and sources of big data are evolving. According to research from Kapow Software, companies can draw information not just from documents and storage systems, but also from social networks, business applications and the burgeoning Internet of Things. Even devices such as medical sensors and cameras now generate data that can be useful for streamlining business initiatives.

More specifically, organizations such as Jawbone, the maker of the wearable Up fitness/health tracker, have seen rapid increases in cloud storage utilization. VentureBeat's Matt Marshall stated that Jawbone's servers collect the equivalent of 60 years' worth of sleep data each night from user wristbands that are synced to smartphones.

Jawbone's case illustrates how enterprises have to be prepared for rapidly shifting data requirements. As they deploy new applications and services, they will rely on infrastructure that can scale and accommodate these changes. Moreover, building custom cloud solutions may contribute to the bottom line by reducing upfront investment in proprietary hardware and reliance on single vendors.

"We are seeing in all our research more empowerment of business users and business analysts," stated Ventana Research's Tony Consentino. "This is being driven by a number of internal factors such as industry competition but also by the ability of business users to rent applications from the cloud without incurring significant capital expenses and being dependent on their IT groups."

Consentino stated that although enterprises are realizing the benefits of big data, they face key challenges in human resources. Nearly 80 percent of respondents to a Ventana Research survey identified staffing and training as obstacles to analytics projects, underscoring the importance of educating more IT professionals on big data cloud infrastructure.

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