Big data has become an increasingly important issue for tier-1 insurers as the management and utilization of big data is critical to competitiveness within the insurance industry; all companies must start to tap the benefits of big data before it is too late, according to a recent report from Gartner.

However, the report, titled “P&C and Life Insurers Not Ready for Big Data,” goes on to state that most insurers are still struggling to understand what big data is.

The transformational impacts that big data initiatives have on insurers, as well as the lack of data governance, IT foundation and IT investments to support big data initiatives, may be what’s behind the struggle. According to the report, big data initiatives will require larger investments in data storage, new analysis tools and possible legacy system replacement.

Gartner Research VP and Distinguished Analyst Kimberley Harris-Ferrante said in her report that insurers are beginning to make investments in data analysis; Gartner observed growing year-over-year investments in information management, including business intelligence (BI) and analytics. They are now considered key elements in business success as information assets are leveraged for decision support, compliance and improved competitiveness.

However, making the necessary investments isn’t enough.

The report says these initiatives fail to address the main problems associated with information management and governance—data integrity, storage, accessibility, user requirements and processing speed. P&C and life insurers need to understand the emerging issues related to big data. These include volume, velocity of data, complexities, data management, governance and the necessary IT funding. These risks must be tackled in order for big data initiatives to be successful overall, the report says.

Poor data quality will limit the ability to leverage data for decision support and improved outcomes, and also drive erroneous decisions as models are applied. Similarly, The inability of existing core business systems to support runtime and real-time data requirements will affect productivity, introduce errors and lead to user dissatisfaction.

According to the report, “many of the challenges associated with big data have been ongoing data integrity, data trapped in source systems (especially legacy core business systems), a focus only on structured data, data consistency and data ownership battles (political, by line of business without data sharing in many cases and lack of data governance) and are all struggles that need to be faced in 2012 and 2013 to set the stage for big data success.”

To address these challenges, in addition to evaluating and making investments, Gartner outlines several best practices: assess existing BI and data management approaches to identify gaps; indentify limitations of legacy systems; tackle data cleansing and consistency through a multistage approach; make sure all enterprise data matches the selected data model to improve data consistency; determine what decisions need real-time data analysis; and address core business systems to determine the processing window for batch systems and real-time data.

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