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By Jaclyn Jaeger | September 7, 2016
Escalating enforcement of anti-corruption laws around the world is driving chief compliance and risk officers to get savvier about how they monitor their anti-corruption compliance programs. Enter data analytics.
Analyzing data to ferret out potential acts of bribery and corruption is not a new concept, but traditionally it has been limited by the archaic manual process of analyzing structured data—such as spreadsheets and database records. In an era of Big Data, however, most of the vast and deep oceans of information companies collect every day—from social media, mobile devices, e-mail, and more—hold absolutely no value.
In addition to monitoring for fraud, other pharmaceutical companies are using data analytics to not only meet regulatory mandates, but streamline operational efficiencies.
Take the example of a global pharmaceutical company that entered into a corporate integrity agreement (CIA) with the government after its sales representatives were caught promoting the off-label use of the company’s blockbuster drugs to physicians. Under the terms of that CIA, the company was required to produce a monthly report that, in part, had to identify which of their sales representatives completed mandatory compliance training.
At the time, the CIA training team worked with a legacy human resources system to assign training to employees based on criteria such as country, job code, and job category. The HR information was manually entered into a learning management tool to select the “covered individuals” who needed training. This process was often repeated several times.
As a result, the company had a difficult time producing the reports required by the CIA, because the data it needed resided in seven different learning management systems, explains Ramon Chen, chief marketing officer at Reltio, a data management solutions provider. Also missing was a complete analytical picture of which individuals truly needed to take the intense four-hour online certification course to satisfy government requirements, he says.
That’s when the pharmaceutical company turned to a combination of data-driven applications and modern data management. The new system enabled the company to track and manage training assignments, as well as “covered person” status, by aligning the legal criteria required under the CIA and mapping those with the attributes stored for their employees.
By combining data from its main HR system and multiple training systems, and matching and consolidating records, the company could get an accurate handle on who actually needs to take the training, Chen says. With this insight, the company was able to reduce from 10,000 to 5,000 the number of employees taking the training, effectively refocusing 20,000 hours of employee time, and freeing the compliance team to focus on other responsibilities, he says.
In the compliance space, the use of data analytics is still evolving. But those compliance programs that are ahead of the game are already realizing its benefits, reducing bribery and corruption risks and achieving operational efficiencies.