The Information Age has given us a staggering amount of data that is at our fingertips. Business is relying more and more on huge volumes of this big data to gauge things like customer spending patterns.
Decisions in corporate boardrooms are relying increasingly on big data and the business intelligence that can be gleaned from it. “In 2015, spending on big data related software is expected to increase to around 80 billion pounds globally” according to the IDC. The corporate landscape is changing at such a fast rate, decisions have to be made quickly for companies to survive.
SAP recently conducted a survey of 300 UK businesses that showed that “while 92% of organizations have seen their data grow over the last 12 months, most were experiencing barriers when trying to use the information”. The biggest challenge now is that our ability to analyze data has not kept pace with its volume.
Gains are being made in the hardware development of in-memory computing (IMC) which increases computing speeds and enables companies to achieve close to real-time analytics. But what about how the software ingests, deciphers and provides recommendations on this data?
Software companies are still hampered by traditional languages that require time-consuming programming. How possibly can the IT industry keep up with the demand for cutting-edge analytics when it takes months, or years, to develop the programs?
This is the question now at the forefront of faster data analysis.
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