Top Predictions for Big Data in 2014

Adaptive Computing, a cloud management company, recently announced its top predictions on the future of computing and Big Data analytics for 2014 and beyond. These predictions encompass several emerging trends – including the collision of cloud computing, high-performance computing (HPC) and Big Data – that are expected to accelerate the ways enterprises extract insights from their data.

Adaptive Computing solidified its predictions after new findings from a hands-on survey the company recently conducted in Q4 of 2013 at the Supercomputing, HP Discover and the Gartner Data Center conferences in December 2013. More than 400 data center managers, administrators and users participated in the Adaptive Computing survey from a number of vertical markets, including education, oil and gas, financial and insurance services, government, manufacturing, technology software and telecommunications.

Click through for Big Data predictions for 2014, as identified by Adaptive Computing.

Enterprises will combine computing resources for a better Big Data solution. According to Adaptive Computing’s survey, 91 percent of organizations believe some combination of Big Data, HPC or cloud should occur. As the collision of cloud computing, HPC and Big Data intensifies, Adaptive Computing predicts that organizations will gain a competitive advantage by investing in software capable of scheduling and optimizing data center resources, which increases utilization by simultaneously orchestrating compute jobs over multiple computing platforms.

More organizations will turn to HPC as a Big Data solution. According to Adaptive Computing’s survey, 44 percent of organizations use HPC as a Big Data solution. As HPC hardware costs continue to decrease, Adaptive Computing forecasts that HPC will become an attainable Big Data solution for more organizations, including midmarket enterprise companies.

Big Data analysis process will become more automated. The majority of organizations (84 percent) have a manual process to analyze Big Data, according to Adaptive Computing’s survey. A manual approach is time-consuming and typically results in underutilized, siloed computing environments, which explains why 90 percent of survey respondents would achieve greater satisfaction from a better analysis process or workflow. To process simulations and data analysis more effectively, Adaptive Computing predicts that more organizations will automate the workflow, minimizing costly and error-prone manual work.

The volume and complexity of Big Data workflows will begin to impact businesses on a larger scale. Adaptive Computing’s survey found that 72 percent of organizations believe workflow impacts their business. This is due to the complexities that accompany setting up different types of data sets and databases, and the corresponding application needed for each job. Running compute- and data-intensive Big Data workflows with no automation tends to cause logjams and delay results. Adaptive Computing foresees greater emphasis on automating workflows to eliminate logjams and help extract key information from Big Data, accelerating insights for the business.

More efficient Big Data analytics will increase revenue streams. Research published by Gartner in January 2014 titled “User Survey Analysis: Driving Efficiency and Reducing Cost Is King When It Comes to Decision Making for New Technology Solutions” found that “mobility, Big Data and analytics were rated as being of greater importance to an organization’s strategy than social.” “This aligns well with the data received from a recent vendor survey conducted by Gartner in which 2,015 providers expect analytics to account for three times the revenue stream of social.” Adaptive Computing predicts that Big Data analytics will drive greater revenue by increasing efficiency, reducing internal costs and enabling new business models.