Protecting a Vital Insurer Asset

A Perspectives interview with Peter A. Marotta, AIDM, FIDM, Enterprise Data Administrator, ISO

For years, you’ve been a champion of data management and data quality in the insurance industry. Why is maintaining data quality so important?
Data is the essential raw ingredient of most insurance products. It drives the major financial and operational decisions within a company, and it’s critical to strategic planning, investment decisions, and merger-and-acquisition considerations. Data drives decisions regarding a company’s customer base and how a company serves those customers.

Like any other major corporate asset, data must be managed and controlled properly. If the reliability, availability, or timeliness of the data a company uses or produces is in jeopardy or doubt, the value of the data erodes. Less than optimal operational decisions can result, an organization may delay or misdirect corporate initiatives, and personnel and customer dissatisfaction and frustration can arise. In this light, it can be a costly endeavor for a company to reestablish the credibility of its data.

On the upside, with the potential for new and enhanced uses of data, the value of data assets can appreciate markedly. To continue to grow, prosper, and maintain a competitive advantage, a company will need more data of increased variety and sophistication. Maintaining the quality of that new data will require even more tools, as well as different data-management and control expertise.

An important step in positioning for future success is to evaluate the quality of data and the data-management practices in use throughout the organization. The goal is to identify processes that fail to produce the information required and engineer quality into those processes to minimize the cost of correction and change afterward.

But can’t that be an expensive proposition?
Engineering data quality into every phase of data management does come with a price. But the property/casualty industry recognizes that the price is always less than the cost of doing nothing to protect this valuable asset. Planning and prevention at the outset of any process — that is, engineering quality into the process — can substantially mitigate the cost of correction and change afterward and ensures quality results. In addition, while a goal of 100 percent data quality is laudable, perfection in data should only be required when perfect data is critical to an analysis or process. For most operations, data-quality mitigation will reach a point of diminishing returns.

Where should a company start?
To engineer quality into any data process, a company must start with a thorough, coherent plan and a clear definition of quality ― an essential foundation for all data-management best practices. So what is the definition of quality data? Some define quality as meeting customer expectations; others relate quality to the number of defects. But a simple definition applicable to the insurance industry is that quality data is data fit for its intended use.

How can an insurer determine whether data is fit for its intended use?
Insurers can measure the quality of data by determining whether the data contains a number of indispensable characteristics, such as accuracy, precision, validity, timeliness and other timing criteria, depth, latency and volatility, completeness, reasonability, consistency, uniqueness, accessibility, availability, and cohesiveness.

How can companies promote data quality?
Within an organization, adherence to key data-management guiding principles and best practices will help promote data quality. Companies should follow data-management guiding principles, which include:

Data is a corporate asset that should be managed with the same rigor as financial and material assets.
Data should be fit for the intended use, but not necessarily for all potential uses.
Data need not be perfect, especially if imperfect data does not compromise the intended use and the costs of perfect data are too high in relationship to the benefits.
Data should flow from the underlying business processes.
Data quality should be managed as close to the source as possible.

The following data-management best practices support those principles:

data stewardship
data and data-quality standards
organizational issues ― Structure the organization to promote good data management and data quality.
operations and processes ― Establish processes to maximize data quality and utility.
data-element development and specification ― Design and maintain data, systems, and reporting mechanisms in a manner that promotes good data management and data quality.
data-management and data-quality tools ― Develop tools that promote and foster good data management and data quality.
measurement ― Establish data quality as a performance metric.
individual support ― Support data management and data quality at the individual level, as well as the organizational level.
data privacy — Comply with regulatory and contractual requirements when data is sourced, stored, and used.

What tools and technologies are available to help ensure data quality?
Insurers can use a growing number of tools and techniques to support data-management and data-quality best practices. These include metadata repositories, data dictionaries, data models, data and process flows, master data management (MDM), detailed specifications, audits and controls, data and text mining, and encryption.

What changes in data quality and data management do you see in the next few years?
To continue to grow, prosper, and maintain a competitive advantage, companies will need more data of increased variety and sophistication. Maintaining the quality of that new data will require even more tools, as well as different data-management and control expertise. The challenge is for data managers and their organizations to evolve as their data needs evolve.