Such clogging of an insurer's information arteries could have serious consequences. Awareness of the full scope of data available across the organization may be stymied by system limitations. Many lack the ability to readily aggregate and analyze available data, which can be a real impediment to effective decision-making, the delivery of a positive customer experience, as well as the attainment of overall business growth.

Meanwhile, regulators and rating agencies are increasingly focusing on enterprise risk management as a foundational element of effective compliance reporting, making information management transformation more of an essential investment for insurers.

Many carriers that have already spent and continue to spend heavily on core-system and general technology modernization still find their efforts have fallen short of expectations and needs when it comes to information management. Therefore, if data is expected to be realized as a strategic asset, insurers can no longer continue to merely tweak existing systems and operating models.

Building an information-intelligent organization can be a multi-year journey, during which the insurer will advance through several maturity stages. Progression will enable insurers to increasingly generate insights that can drive decisions from operational to strategic.

The execution of a new enterprise information management solution – involving technical, process, and cultural changes – requires a highly coordinated approach. Yet a recurring theme that has often plagued the implementation of such transformation projects is the absence of a roadmap. Objectives, tasks, resources, and milestones should be prioritized to effectively guide required investments and drive projected returns.

While perhaps appearing simplistic in some respects, this comprehensive approach can allow data management transformation to accommodate emerging needs in priority order, as insurance organizations become ever more complex and information requirements begin to transcend the traditional way most carriers operate.

Executive- and board-level endorsements are also central elements in executing a successful, company-wide data management program. Without such high-level backing, such initiatives will likely stall or fail as line of business and functional teams independently assess the value and cost of participation and compliance.

Once a firmly supportive tone is established at the top of the organization, change management initiatives are essential to secure enterprise-wide buy-in and get everyone on the same page. Workshops that unite business unit and IT leaders along with middle management can facilitate the identification and prioritization of short- and long-term goals while publicizing the program's benefits within departments and across the company.

After establishing buy-in for the transformation initiative across the organization, management will need to identify the business unit or units that will initially champion an enterprise view of data. The Marketing, Actuarial, or Finance functions may be best suited to kick off such an initiative, as they each have a vested interest in achieving enterprise-wide data management. A senior executive leader, frequently positioned as the Chief Data Officer (CDO), should also be identified and empowered to drive this initiative forward as the conduit facilitating collaboration among diverse business operations, functions, and the overall IT operation.

The assigned data champion (whether or not designated as the CDO) should be recognized as an influential leader throughout the organization, and properly empowered to execute the vision and plan. The CDO could lead a formalized Data Management Organization (DMO), consisting of stewards appointed from across the business and IT to help coordinate and propel the project.

It may also be advisable to opt for a preliminary strategy that is initially low in complexity or limited in scope, but high in value. Such an 'easy win' could potentially foster greater buy-in among potential doubters in the organization. 

One challenge that could undermine efforts at information management transformation is the dearth of data science skills available. This problem is exacerbated by the aging of the insurance IT workforce, with a growing number of retirements expected over the next few years.

Carriers will need to identify alternative sources for skills that cross the disciplines of both information management and quantitative processing, often recruited from outside the industry. They may also have to engage third parties with the necessary expertise in such transformations to bridge the immediate competency gaps while the company incubates its own internal capabilities.

The opportunity for an insurer to reposition data as a strategic enterprise asset and leverage it for enhanced business performance is enormous and inviting, albeit perhaps daunting. Yet through a well-articulated shared vision and commitment to collaboration and change, both at the top of the organization and through its ranks, available information can be transformed into a strategic asset, while meeting increasingly demanding regulatory requirements.

The potential payoff for the investment is significant and compelling – improved market competitiveness, enhanced customer relations, increasingly responsive and reliable compliance reporting, more insightful risk management and decision-making, as well as solid business growth.

For more on how insurers can transform their information management systems, see Deloitte's research report, "Information Rich, Knowledge Poor: Overcoming Insurers' Data Conundrum."

Sam J. Friedman ([email protected]) is the research team leader at Deloitte's Center for Financial Services in New York. For many years, he was the Editor in Chief of National Underwriter's P&C edition. Follow Sam on Twitter at @SamOnInsurance, as well as on LinkedIn.

Michelle Canaan ([email protected]) is a manager with the insurance research team at the Deloitte Center for Financial Services in New York, and co-author with Jaykumar Shah of "Information Rich, Knowledge Poor: Overcoming Insurers' Data Conundrum."