The report, “Analytics: The Real-World Use of Big Data,” was conducted by IBM and the Saïd Business School at Oxford University. It involved answers from 1,144 business and IT professionals from 95 countries across a range of businesses, and not all of them customers of IBM.
In current terms, big data is just getting going. Forty-seven percent of those surveyed are in the planning stages with big data, 28 percent are piloting and implementing plans, and the remaining respondents “have not begun” activities on big data. In another question where respondents ranked their big data adoption stage across four levels – from educate and explore on the low end to engage and execute on the high – only 6 percent were at the top of the maturity scale, having deployed two or more big data initiatives and applied advanced analytics. Seventy-one percent were either still gathering market information on the trend or developing a business strategy, according to the survey. And the survey authors attribute some of the slowness with maturity with an absence of a team approach that includes business members in the exploration process.
“Within these organizations, it is mainly individuals doing the knowledge gathering as opposed to formal work groups, and their learnings are not yet being used by the organization. As a result, the potential for big data has not yet been fully understood and embraced by the business executives,” the survey authors wrote.
The reasons businesses are in some stage of interest or execution with big data was as varied as the data itself. While nearly half reported “customer-centric outcomes” from taking on the voluminous data, others expected operational optimization (18 percent), risk and finance management (15 percent), a new business model (14 percent) or employee collaboration (4 percent).
The survey noted a few business use cases that have cropped up related to big data, like the streaming driver information to Ford Focus electric car drivers as well as engineers at the company headquarters.
Within that small sample of enterprises actually engaged in programs to dig into larger and unstructured data sets, they reported the following as their top analytic capabilities: query and reporting (91 percent), data mining (77 percent), data visualization (71 percent), predictive modeling (67 percent) and optimization (65 percent). Just one-quarter of those enterprises reported capabilities for voice or video analytics, or streaming analytics (35 percent).
Among the responses, there was an assumption by many early-stage enterprises that their existing information infrastructure is “sufficient” to layer on more analytics programs and solutions geared toward big data. Authors of the report cautioned that realistic assessments of data storage and BI are in order to properly handle new data volumes and sources.
“On the surface, a combination of adding storage and one or more larger servers can support the growth of an information management foundation. However, it is important to understand that anticipating and architecting the infrastructure is key to delivering the business value of the intended business case,” the survey authors wrote.
The leading platform components in pilot or integrated into enterprise architecture were solutions for information integration (65 percent), scalable storage infrastructure (64 percent), a high-capacity data warehouse (59 percent) and security and governance solutions (58 percent). At the lower end of implementation were Hadoop/MapReduce (42 percent), NoSQL engines (42 percent) and stream computing solutions (38 percent).