The insurance industry, though conservative, is not immune to change. New market opportunities, new books on business and new sales channels are all known entities that can be accommodated without too much disruption. But change is now coming in the form of new data sources, such as in-vehicle geolocation and telemetry data, social networking sites and microblogging feeds such as Twitter. The question naturally arises of what, if any, of this data is relevant, let alone useful to insurers. Incorporating data from new sources, specifically “live” sources, into existing data warehouses is a technical challenge. Moreover, the sheer volume of this data, often referred to as big data, can be daunting. But with a slight shift in perspective, a powerful framework for analysis emerges, allowing mature legacy systems to become part of groundswell changes.

In the era of big data, new and potentially rich sources of data are continually appearing on the horizon. In addition to more data and cheaper storage, it’s become desirable to perpetually build more sophisticated back-end and customer-facing applications. Storage costs that continue to decline also allow for greater data retention, contributing to the potential enrichment of BI and analytics. Cloud storage, though not fully proven for BI, makes data readily available across a geographically diverse enterprise. But the data can be noisy and irrelevant at times.

How do we begin to make use of massive amounts of noisy data, sifting the relevant from the irrelevant, and adjusting an existing BI system to handle it all?

A Business Challenge — Not a Technology Challenge

It is a mistake to regard the incorporation of any new sources of data into existing insurance BI systems and data warehouses as a technology issue. Certainly the technological challenges are there, in the form of integrating data in different formats (including video) and transforming it into well-behaved, useable information. The timely ingest, grooming and manipulation of new forms of data is not for the faint of heart. But we must always keep in mind that a BI project is primarily a business challenge.

With this slight change in perspective – from technology issue to business challenge – a BI best-practice approach presents itself quite naturally. More often than not, BI programs are initiated, developed and even “owned” by experts in IT. This “build it and they will come” approach has strong appeal, and on the surface, it appears sound. Letting IT experts get on with their work sounds like it would produce solid results. However, a BI program that is not regarded first and foremost as a business challenge will quickly become misaligned with corporate strategies and business interests. Executive sponsorship is needed, bringing its own rewards. It imposes a corporate definition on the BI project and helps smooth out the ideological differences that can emerge between the IT team and the stated business goals.

Mind the Gap

With the adoption of this perspective, it can be seen that a methodical comparison between the current state of the BI environment and the desired BI setting is the ideal bond between a technology-centric approach and a business-aligned BI strategy. The emergent solution identifies the gaps from the perspectives of process, people and technology. Furthermore, it allows the development of a BI program with a strong alignment to the business objectives, the adherence to a clear change-management strategy and the engineering of the right decision-making architecture.

The gap analysis can manage end-user expectations and plan for delivery of new functionality in easily absorbed increments. It can also provide training for the user community. This approach is particularly relevant when functionality drills new paths into existing data or opens up new data sources for exploration and analysis.

Executives are already familiar with the importance of business plans when starting a business and strategic plans for short- and long-term planning. A gap analysis fulfills the same role for projects that impact analytical information systems. While also analyzing the technological changes required, a gap analysis must fully support the goals of the carrier’s strategic plans.

The Importance of Aligning Technologies with Business Strategies

Ensuring the success of an insurance BI program in the face of rapid technological change can be challenging. Pitfalls can be avoided by bringing in the business angle from day one and always keeping it in the crosshairs. In this way, the insurance BI program cannot fail to have a strong affinity with the carrier’s strategic plan and business ambitions. Executive sponsorship is achieved through business models, not technological attractiveness. A gap analysis aligns technology with business goals and is used to evaluate, fine-tune and troubleshoot mature insurance BI systems before upgrading or moving to the next phase of a long-term, ongoing BI and analytics roadmap. In the absence of a methodology such as a gap analysis that couples technological change with cultural change, the gap between what is needed and what can be achieved is wide.

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