BFSI Firms Still Lacking Big Data Maturity
The BFSI segment, which has already shown some momentum in big data and analytics, will see it as a top investment priority in the coming months, according to a new IDC report. However, for a successful execution of big data and analytics, these organizations need a greater maturity and competency in these technologies – something many firms currently lack, says the research firm.
The report shows that a majority or over two thirds of BFSI companies present a relatively low level of big data and analytics maturity overall. Some 12% are investing in big data and analytics competencies and another 21% are already gearing up for a greater outcome.
Those institutions that are investing in their big data and analytics competencies report a positive correlation between maturity and successful outcomes of big data and analytics initiatives, IDC says. The highest achievers are obtaining big data and analytics maturity as a multifaceted endeavor across five core dimensions — intent, people, process, technology, and data. Success depends on the absolute level of maturity in each dimension and on aligning the five dimensions at or near the same level of maturity.
On average, higher levels of big data and analytics maturity has led to better chances of generating value and achieving expected or greater-than-expected benefits in the areas of technology, staffing and other related investment decisions.
“In-market adoption of big data and analytics has reached the point where the capabilities and applications these technologies enable are becoming mainstream for a growing number of financial services firms,” Michael Versace, research director IDC says in a statement.
Michael believes that business models in BFSI industry have been data-oriented for decades. But with the adoption of big data and analytics technologies, a new platform for competition has emerged, confronting firms with the complexities of new technologies, skills requirements, and the seemingly endless opportunities to use data in ways not previously possible.
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