Better data utilization and analysis results in better performance for insurance organizations.
In a recent research note, I referred to data as the DNA of an insurance company. While I thought I was being clever, I actually missed the broader implications of the analogy. The point was that just as DNA helps define the molecule—which becomes the cell that forms the tissue that becomes an organ that leads to a body—so too does data help define the information that leads to the tasks that become activities that are part of processes that define business operations. From an operations standpoint, good data is a key building block for cost-efficient and organizationally effective insurance transactions.
Missed was the tremendous impact that DNA research and analysis is having on medicine. Identifying changes in the structure of DNA promises to revolutionize the diagnosis, management and treatment of disease.
Better understanding of the impact of small changes in DNA provides the basis for determining healthier behaviors that add vitality to longevity, earlier diagnosis of potential problems to minimize risk and better management of treatment to speed recovery.
So too does the focused analysis of data lead to determining the health, well being and ultimate treatment of the modern insurance company. Effective use of data analysis, business intelligence and predictive analytics has had a transforming effect on the insurance industry and most other industries today. Like the impact of a better DNA understanding on medicine, better data utilization and analysis leads to a healthier, more dynamic and more agile insurance organization.
Not surprisingly, initial activity in data analysis has been focused operationally, with pricing precision, fraud identification and straight-through processing as examples. Data as the basis for enterprise performance management is now gaining importance.
Applications including enterprise risk management, demand management, distribution channel management and product management are all being actively pursued by the insurance organizations with which I’m familiar.
The exciting opportunities are in the more aggressive utilization of data as the basis for strategic decisions. Product innovation, market segment extension and distribution channel expansion are examples of the emerging utilization of data analysis to improve the competitive health of the insurance organization. This more strategic utilization of data holds significant hurdles for the traditional insurance company. Some of these difficulties include:
* Too much data with no common data lineage and no single authoritative source. In other words, even if insurers know where the data came from, not much may be known about how it’s changed over time.
* A single term can have multiple meanings, leading to poor data quality.
* The lack of strong centralized enterprise data governance processes. Individual business units often don’t often understand the risks involved with distributed data governance — with disparate groups managing the data, and the issues filtering down the chain. This needs an enterprise approach.
* Insurers are not capturing all relevant data for analysis. One insurer I visited reported that 90% of all data stored is unstructured, while 10% is structured. Only structured data in a data warehouse is available for analysis.
* Most organizations have limited understanding of the value of a comprehensive data strategy and data architecture. This means data management is often not a corporate priority.
So what’s the answer? Like the DNA analogy implies, data must be both operationally and strategically important in improving the quality of decisions made. The proper focus on a data strategy needs to happen.
One insurance CFO startled me with the assertion that the decision is often not a financial one. The fear is the loss of competitive positioning in a territory or line of business. The decision is not financial, it’s strategic. Does the organization want to remain in a line of business if the only way to compete is to be more data driven? The business answer to the question makes the decision easy, but the implementation of the decision is still problematic.
The first step is in defining and implementing a data strategy focused on making data more actionable. In my experience, insurers often don’t have a comprehensive data strategy in place. For the ones that do, master data management is often about reducing redundancy of data and determining a “single version of the truth.” Consider the budget focus for one insurer, who reported rationalizing and simplifying the amount of data they have available. They need to reduce cost, eliminate the redundancy of data, improve the quality of the data retained, improve the performance of the data warehouse and improve user access to the data. This is all part of a robust and actionable data strategy.
Next is the need for a comprehensive, enterprise data model. This has proven problematic in an industry where the perceived differences among the various business units make collaboration difficult. Hope lies with a broader, industry-wide focus.
The variety of work being done by industry groups such as ACORD and OMG to develop and make available industry standards and reference model(s) is encouraging. This work requires more overall industry support and focus to assure successful completion of these deliverables.
Finally, a metadata strategy that focuses on both technical metadata definitions and business metadata definitions is needed to help address concerns about data accuracy, data hygiene and overall data management. It also sets up the opportunity to adopt the emerging model driven architectures and event driven architectures that are starting to emerge in insurance.
While a proper data strategy, corporate data model and both technical and business metadata definitions won’t solve all an organization’s issues in today’s marketplace, just as DNA analysis can’t overcome the effects of genetic makeup or poor health habits, they do provide promise for and industry evolving rapidly in an ever changing market.
Mark Gorman is a principal with Mark B. Gorman & Associates, a Minneapolis consulting firm.
(c) 2008 Insurance Networking News and SourceMedia, Inc. All Rights Reserved.
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