Big data is a concept that every company should be striving to embrace. However, just having mounds of data is useless. It needs to be properly analysed and sorted before it will offer any use to a company, says GARY ALLEMANN, MD of Master Data Management.
Big data is one of the most significant trends currently affecting organisations. As data volumes have continued to grow, enterprises have been challenged with the task of storing and managing it effectively. However, the value of big data can only be leveraged if organisations move beyond IT-driven storage to business-driven analysis of data for insight and competitive advantage. As enterprises begin to realise the potential value and importance of big data, so the trend has moved from an IT issue to a business problem. Big data projects are increasingly being driven not by the IT department, but by business departments looking to harness data to solve specific business problems. To cater to this demand, the big data role needs to shift away from data science toward business analysis, and technologies such as self-service big data analytics are growing to address this changing need.
Research conducted in Europe and the US, by big data pioneers, Datameer, showed a shift in ownership of big data initiatives over the second half of 2014. In a webinar titled Big Data Predictions for 2015 they noted that “there was a marked shift in investigating big data offerings from IT to business. In the second half of the year business executives far surpassed their IT counterparts”.
As big data technology matures the focus is shifting from understanding the technology to realising the business value. Big data has the potential to deliver significant business insight in multiple areas, including identification of new trends, advanced customer profiling and more. As such, many organisations are focussing on empowering the business to become more big data driven.
Big data requirements are being driven by specific business cases or problems that require timely, accurate answers. As a result, organisations are now beginning to ask how they, from a business perspective, can use technology appropriately to achieve business goals through the analysis of big data in business time. The long development life cycles typical of enterprise data warehousing projects simply are unacceptable.
This shift to the business is also moving big data away from the pure ‘science’ approaches. The much hyped “data scientist”, once seen as the sexiest job of the 21st century making way for the more value-driven role of the business analyst. The shift is evident – analytics is no longer viewed as a technology function, but rather a business function that needs to cross the boundaries between IT and business. As a result, the importance of involving business-focused staff such as analysts and managers is becoming clear. Bridging the business-IT gap is essential and business staff must be more directly involved in big data analytics. The data scientist will survive, for specialist analytics that require their unique combination of skills, but day to day analytics is shifting to the business.
The governance and integration of big data from multiple sources into a single usable format remains a challenge. This, as well as the current mind-set shift, is driving a new technology trend – the emergence of self-service analytics, which makes relevant information available to business for faster time to insight. In addition, self-service big data analytics frees the IT department from the provision of information, enabling them to address other areas that will enable the organisation to make better use of big data assets.
In an IDC Analyst Connection report, Datameer posed several pertinent big data questions to a top big data and business analytics analyst at IDC on behalf of its customers. To read the report, download the white paper here: http://info.datameer.com/IDC-Self-Service-LOB.html
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