In collecting our thoughts for a white paper about how life sciences companies can achieve next-level data applications, we found ourselves debating a confounding question: Are there any pharma or medtech manufacturers using data in a way that their peer companies in other industries – say, finance, retail or technology – would recognize as mature?

To be sure, there are companies like Medtronic and Amgen that have been building their data capabilities for quite some time now, and that are doing some very exciting things in this area. There are others, such as Novartis and Roche, that have recognized the importance of data to their business and invested accordingly. But by and large, the life sciences are behind the curve when it comes to these emerging applications of data.


In fairness, it’s an apples-to-oranges comparison. Finance firms, for example, have really cracked the nut when it comes to integrating data operations into their businesses (real time monitoring, credit alerts, etc.), but then, they come to data work with enormous advantages over their life sciences peers. The daunting complexity of healthcare – in commercial, clinical and regulatory terms – is an overriding factor, as is the messiness of healthcare data compared to other industries. For a Chase Bank, the challenge of data work is primarily one of volume. For pharmas and medtechs, wrangling incomplete, inconsistent and fragmented data poses far more difficult and resource-intensive problems to tackle.

But beyond that, there are some common missteps companies make, as we’ve observed over the 400+ analytics projects we’ve executed for life sciences orgs in the past three years. For starters, many companies struggle with acquiring the right data to answer their business questions. It’s tougher than it sounds! Here’s how companies get it wrong:

  • They see data acquisition as an end in itself rather than as a first step, one which must be aligned with clear objectives in order to be successful. Building a data lake is all well and good – but be sure you know what you want to accomplish before you start digging.
  • They don’t know what they’re seeking to know. The foundation of effective data acquisition is understanding what business problems you’re trying to solve for in your data work. If you don’t know what questions you’re trying to answer, chances are that you’ll end up with a lot of irrelevant data sets sitting around, chewing up resources. Start from the business need, and then ask: What sorts of data assets can I use to resolve this?
  • They don’t understand their data’s strengths and weaknesses. The significance of these strengths and weaknesses will vary according to the business question being asked, but if, for example, the product you’re posing questions for is primarily used by seniors, and your data source’s coverage is spotty in Florida, you probably won’t be getting reliable insights out of your efforts. Ensure that you’ve profiled your data sources for any potential soft spots in terms of completeness or accuracy, etc.

Becoming a truly data mature organization is a process for companies large and small alike in the life sciences space. It’s one in which nearly all of us are, at one stage or another, engaged. Some organizations are farther along than others, but there’s yet time for the laggards to catch up, and they will benefit from the hard-won learnings of innovators.

We’ll be exploring some of these pitfalls and best practices in a series of blog posts over the coming weeks. For a more holistic view, take a look at our white paper for further insights on this important topic. We’re eager to share what we’ve learned helping pharmas and medtechs at both ends of the maturity spectrum establish priorities and processes for data management, and through integrating vast quantities of claims and EHR data into our own Real World Data repository. If you’re in the process of trying to support successful data efforts for your life sciences org, please get in touch – and stay tuned for the next installment in this series, looking at best practices for mastering and integrating data. You can also download our white paper, How to Make Health Data Work for Life Sciences Companies, here.

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