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Last week, I had the pleasure of speaking at the Halloran Speaker Series about “embarking on your data literacy journey” – a discussion that built upon two recent perspectives on data quality that I penned (parts one and two). As a proud, but recovering statistician, it is mind-boggling how many discrepancies still exist when it comes to gathering, analyzing and reporting data in this industry. With artificial intelligence and machine learning gaining momentum in life sciences, the emphasis on quality data is paramount. And as we continue to automate data-driven processes, errors relating to poor data quality will be embarrassingly magnified.
Being in an industry that relies on data to excel and survive, there is still so much that we need to get right. For instance, did you know that only 3% of companies’ data meets basic quality standards? 1. This could be for several reasons — lack of skill sets (analysis and interpretation), biases and hidden data factories. In preparing for this discussion, I pulled a public safety dataset from the San Diego Police Service Calls for 2020 to see how their data was captured and stored. The screenshot of the spreadsheet below indicates all the areas where data was inputted into the database incorrectly, this includes dates, priority levels, call types and addresses.
To effectively navigate and set ourselves up for success, I encourage your organization to build a culture of quality with an eye on the following:
So, what are you waiting for? Give it a go and see what appetite your organization may have for making some changes. Need some help? Drop us a line.
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1. Harvard Business Review, September 11, 2017