Your boss’ boss needs some EHSQ data stat and you go into a panic thinking, “well, I have that information in my system, but can I trust it?” Your mind begins to wonder about the data you’re going to have to provide. You ask yourself:
How reliable is it?
How long do I have to review and clean it up first?
What can I do to quickly fill in the missing fields?
You're not alone. In fact, a study by Harvard Business Review found that “only 16% of executives strongly agree that they are confident about the accuracy of most of the data underlying their business decisions.” The study identified that missing and/or inaccurate data were the leading factors contributing to a lack of confidence.
The average financial impact of poor data quality on organiations is $9.7 million per year. - Gartner
With people’s lives and corporate reputations on the line, EHSQ professionals are now being asked to keep up with their colleagues who are using Big Data applications that support immediate data-driven decision-making.
According to Gartner Research Vice President Andreas Bitterer, “Data quality is not an IT problem. IT can help fix it, but the business must own the problem. For example, company culture can have a significant influence. Organizations need ‘data stewards’, people within the business who are responsible for the quality of the information. However, technology will play a role in fixing many data quality issues.”
Learning from our development efforts
Cority recognized the depth of the impact of poor quality data while developing its Predictive Analytics solution. Cority has safety findings from over 1.5 million safety events and 6.5 million employees. As we developed our solution, our team of data scientists and statisticians worked with our customer Product Advisory Board (PAB). These PAB members agreed to share their data as part of a joint initiative to build our predictive models. The models will be used to identify how likely an employee is to be injured over a 12-month period based on different variables. As part of this process, Cority needed to clean-up and normalize the data prior to it being incorporated into the data models because as you can imagine, the quality of the predictive models are dependent on the quality of the data they are built upon. During this process, we realized the importance and value in helping companies understand how clean (or dirty!) their data is. As a result, the Cority Data Quality Score was created.
The Data Quality Score is a key metric allowing you to identify and understand where data collection needs to be improved. It’s an immediate visual representation of your EHSQ data accuracy and data completeness. The Data Quality Score instills confidence in the information you report on and deliver to the organization, reduces an ongoing investment in data cleaning, and removes the bad data costs and consequences that escalate from it across the organization.
Immediate feedback from our clients has been overwhelmingly supportive. They have identified it as a critical tool in driving a culture of data cleanliness within their organizations. The Data Quality Score can be easily implemented, monitored and leveraged to ensure a solid foundation for the ongoing management of workplace safety initiatives and is unique to Cority.
To learn how to improve the accuracy and completeness of your EHSQ data, download our best practice guide:
About the AuthorMore Content by Jessica Shields