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Clearly, data privacy is important, and it seems unlikely for anyone to substantially dispute that point. Given both the evolution of technology and the importance of competing on data (which includes the ever-increasing types and volume of data being collected by companies), the focus on protecting both personal and corporate data will only continue to increase. Cybersecurity (protecting systems, networks and data from digital attacks) is all the rage. Organizations continue to devote more resources to this important task as they try to stay ahead of those that would seek to compromise their systems and data. In addition, cyber insurance, which is protection against the financial loss due to a cyber-related event, has continued to grow in importance and premium volume.
about the quality of the data that is being protected? Who is looking at that
and are resources being devoted to ensure that data is of high quality? Often
referred to as “data hygiene,” it’s the much less glamorous side of the big
data equation. However, while it may seem obvious, if the data being used for
those increasingly sophisticated algorithms and important purposes is not of
good quality, then the results can be questionable at best and flat out wrong
at worst. Garbage in, garbage out. The best predictive modeler can apply the
most advanced techniques that technology can support, but he or she can’t
really overcome the issue of the data being bad. Not only can bad data lead to
less-than-optimal analytics results, but it can also lead to other (and
possibly more severe) consequences, such as poor servicing of customers or
No data is perfect, so there is usually considerable effort that goes into any analytics project around making sure the data is as useful and accurate as is reasonably possible. Ideally, companies are taking steps well before any analytics work is invoked to provide for high quality data. Some of these steps can include a strategy for how data is stored and retrieved. Is it able to be connected across various sources in an efficient and effective way? Are there controls being put in place both as the data is being built (allowed value checks, data cleaning procedures, providing for a single source of the truth, etc.) as well as after the data store has been established (auditing/balancing of information, periodic surveys of the data, regular updating of data, etc.)? Finally, is there useful and up-to-date metadata (information about the data) that is being developed and readily available for end users of the data to consume?
While it may not get the glory, those charged with developing the processes around and maintaining the quality of a company’s data store serve a critical role in the success or failure of analytics (big data or otherwise). The importance of the results, and really the ability for a company to successfully compete, now and into the future, relies on it. Data management practices must not be overlooked—the investment of both financial and human capital will most certainly be worth it!
Greg Frankowiak is a Senior Consulting Actuary with Pinnacle Actuarial Resources, Inc. in Bloomington, Illinois and has over 20 years of property and casualty actuarial experience. Greg has extensive experience in predictive analytics for both pricing and underwriting, product management and strategy development, underwriting/operations, ratemaking for private passenger automobile and homeowners insurance, as well as regulatory and filing support and business intelligence. He is a Fellow of the Casualty Actuarial Society, a Member of the American Academy of Actuaries and a member of the CAS Ratemaking Committee.
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