Why Data Quality Management Has Become Essential to the Pharmaceutical Industry

Video Transcript – Abhijit Nimgaonkar, Principal

So in the last couple of decades we’ve seen the pharma industry really suffer from a problem of plenty, in the sense of with the advent of prescriber data, with the advent of patient data, with the advent of payer-related data. I think there’s been a huge explosion of information available about products, about markets, about customers.

This industry has leveraged [data] like probably no other industry to make sure that its customers are served ultimately in the best possible way. I think our clients have really struggled to manage the issues around integrating all of this data. And then making sure that, of course, the data is right consistently over time. As it is used for operations, for analysis, and so on.

And often the consequences of not getting the data right are very severe. Everything from leadership losing confidence in the data, and therefore, stopping using it for the decision-making, all the way to sending out maybe a wrong targeting file, so that your entire sales force is calling on the wrong customers. So the consequences are quite severe.

So there’s either been an overreliance on automated checks, or the other extreme, which is an overreliance on data stewards to do a lot of the data checking, and then implementing checks as they discover issues and creating a patchwork quilt, as it were.

So neither approach is very scalable in the sense that as more and more data comes in, and as new data sources are needed to be added into our operations and analysis—neither is a sustainable market. That it’s very hard to change the technology solution, and then on the data stewardship side, it’s very expensive to keep adding more data stewards.

I think the key to getting it right is, as I said, to make sure that you use the right combination of automated checks as well as the human judgment. That again, there’s a collaboration framework in place. It’s not contingent on a pharma company by itself to get things right. That collaborating with upstream data vendors, collaborating with consumers of information to recognize what information is going to be used for and therefore, what level of diligence needs to be put in terms of checking the data is critical.

What we have seen, though, is that with the proper discipline, around using the right framework for checking data, around using the right platforms and the people, and with the right sponsorship from executive leadership, there can be a huge difference that can be made in terms of detecting data quality issues. And in fact, even fixing them even before they occur.

So just figuring out exactly what level of checks to use, and what level of automation and human judgment to use, across all of these different users of information in the pharma industry is critical to getting it right.