As my colleague Maria mentioned in her recent blog post, there’s no dearth of data for medtech companies to start their analytics journey. The right data assets, combined with an intelligent way of consuming them, lay the foundation for differentiating yourself through analytics.
That being said, organizations still need to develop and strengthen their data capabilities before they can unleash the potential of analytics. Our clients still have many questions on what to do with their current data assets and when to acquire new ones. As consultants, our response is the typical, “It depends, and here’s a framework!” We then go on to say that it’s better to reflect on how your current analytics needs are being met, and how you could be enabled to ask new questions and develop newer business models. Get started by identifying the business needs that you’re trying to address, which will help you decide when and what kinds of data assets to invest in. Here are four questions to ask yourself that will enable you to make the right data investments.
Question 1: How can my current data assets enable new business questions? Taking a fresh look at your current data and its possibilities will help you come up with new ways to harness your current data assets to the fullest, without the need to invest in new ones. For example, just by integrating and enriching your existing sales, inventory and pipeline data, you could better predict purchasing patterns, perform cross-sell opportunity analytics and create models for SKU-level forecasting to prevent inventory stock-outs, resulting in additional revenue opportunities. You could also look at your device or IoT data to see if you could analyze it to come up with new revenue streams, detect defects and preempt product recalls, or even optimize your supply chain.
Question 2: What additional value can I derive from my current data assets? Before you think about going after additional business questions or acquiring new assets, consider whether your existing data is consistently answering your current analytics and business questions. The best way to extract value in this case will be to automate those analytics processes, whether it’s scoring a customer’s value or estimating marketing ROI. Automating and operationalizing the reoccurring data and analytics processes will free up time and resources for companies to become strategically focused again, and to go after new business questions or acquire new data assets.
Question 3: How can I obtain deeper insights into my current business questions? After fully leveraging and automating your current data assets, you might be ready to invest in newer data sources. Depending on the value that you believe that the additional insights will generate, you can adjust your data investments accordingly (such as by going with a commercial data provider or leveraging a publicly available data source).
For example, in order to understand an IDN customer’s local influence better, we helped one of our clients acquire and integrate many external sources, such as customer potential data from a data vendor, web-scraped data from the websites of health systems, and even social data from professional networking sites such as LinkedIn. We then integrated all of that information to gain better insights on how that IDN is influencing local dynamics through acquisitions, joint ventures, academic partnerships, etc., which ultimately improved the company’s key account strategy and penetration.
Question 4: What additional business questions can I answer with new data investments? Before investing in a new data asset, ask yourself if these investments could enable you to expand your thinking beyond existing questions to explore new hypotheses and scenarios, and unlock new categories of business models.
For example, one of our clients in the cardiovascular space invested in patient claims/procedure data to initially use patient claims as a proxy for customer (health system/hospital) potential, but then brainstormed internally to figure out how that data could help create a machine learning model to predict patient progression for heart disease. Inspired by the ability to look at the patient journey and influence the outcomes, the company created a direct-to-consumer awareness campaign on the importance of surgical intervention for aortic stenosis, which ultimately led to greater adoption of their products and better outcomes.
This framework—like I said, consultants love frameworks—provides a visual guide for mapping out your business questions with your data acquisition strategy to ensure that you’re maximizing the value of your data assets.
Analytics present a huge opportunity for medtech companies to both optimize their commercial models as well as create new revenue streams, but in order to truly unleash the value of analytics, you need to have a sound data acquisition strategy. Hopefully this framework will get you started.