Tuesday June 20, 2017

New Solutions for Identifying Emerging Risks

By Don Mays and Greg Swinehart

Companies that make and sell products, and the agencies that regulate them, are inundated with massive amounts of data received from a wide variety of sources. Whether the data come from a company’s customer service department or from postings on social media, the voluminous amount of data has become an overwhelming task to manage. But managing it is critical; often buried within those mountains of data are faint signals that can indicate emerging issues that may pose significant safety or quality issues. It has always been challenging to detect emerging risks before they become crises, but our former reliance on spreadsheets, disparate databases, and unaided human judgment has given way to more sophisticated methods for extracting, analyzing, and visualizing emerging risks.

Early detection of emerging issues can save lives, prevent injuries, and reduce property damage. From a manufacturer’s standpoint, early detection can also reduce the scope of a recall, reduce warranty claims, and lessen the potential of product-liability lawsuits. In fact, if a safety problem is caught early enough, it may obviate the need for a recall altogether.

The challenge is to separate valid signals from the noise. New technologies can extract and organize data from multiple channels and apply advanced statistical techniques to help analysts see previously hidden patterns. Once a tool for marketing teams, big data analytics—also called advanced analytics, predictive analytics, and sensing analytics—has made its way into the product safety world. The technology involves integrating “structured” data, from, for example, the CPSC’s National Electronic Injury Surveillance System (NEISS) database, with “unstructured” data, such as comments on social media platforms, to expose potential emerging product safety concerns. Natural language processing methods can parse key words and phrases, recognize their relationships, and create richer datasets than were previously available.

Sometimes it is not what your customers say but how they say it. Big data analytics can process context through sentiment analysis. It can discern the difference between a “hot” product that customers are raving about and a “hot” product that will burn you. Furthermore, the technology can process the data in near real-time, and intuitively display the data on computer dashboards, making it much easier to spot trends or emerging issues. The technology allows analysts to sift out meaningful clues and deliver the early warning insights that are needed to help prevent a product safety crisis. The output from early warning systems can be incorporated directly into a company’s decision support systems.

Software that enables the ability to sort out safety issues from typical consumer complaints does not include the level of human intelligence required to ensure prudent decision making with respect to product safety and quality disposition. That’s where people come in; sophisticated machine output combined with subject matter expertise can lead to a more “intelligent” process, sharpening the output of the results and highlighting issues that pose the highest levels of risk.

Whether it be from posts, blogs, news articles, or consumer complaints, advanced analytics can increase the size of datasets by magnitudes over what can be practically analyzed by conventional methods. Once a labor-intensive process involving line after line of data entries relegated to voluminous spreadsheets, spotting emerging issues is now easier, faster, and more precise, thanks to advanced analytics.

Don Mays is a Deloitte Risk and Financial Advisory managing director of product safety and quality, Deloitte & Touche LLP. dmays@deloitte.com, 917-561-2906. Greg Swinehart is a Deloitte Risk and Financial Advisory partner in the safety and quality practice of Deloitte Financial Advisory Services LLP. gswinehart@deloitte.com, 212- 436-2089.

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