Many of our medtech clients have started thinking about investing in building an analytics program to drive their business forward. Increasing provider consolidation, focus on value and outcomes, and a shift in the customer base from a clinical decision maker to an economic decision maker have forced medtech organizations to find newer ways to grow and innovate, and improved data and analytics capabilities are one avenue to sustain or rev up growth.
However, many companies struggle with how and where to start their analytics journey, and how to measure their analytics maturity. What we’ve been suggesting to clients is that analytics maturity isn’t an end goal in and of itself. Rather, analytics maturity is a means to achieve value for your commercial organization in terms of revenue improvement, optimizing commercial spending, increasing operational efficiency or improving compliance, with the ultimate objective of improving patient care and outcomes. Creating a charter to define the end objectives and purpose of your analytics organization is a great first step.
Once you’ve defined the purpose and are ready to assess and improve your analytics maturity, ask yourself these six key questions:
1. What’s our organizational mindset for analytics? The key areas to probe are the intent and culture elements of your organization to create differentiation through analytics. Are your reps focused too much on relationships and gut instinct to drive business, or are they willing to balance intuition with data and analytics to make decisions? How much is experimentation and a change in mindset encouraged within the organization? Organizations that have senior-level sponsorship to experiment and develop initiatives to foster analytics-driven pilot programs are more likely to reap value from analytics.
2. What’s our data strategy? Data should be considered a core asset of a firm, and there should be a clear strategy on how to procure the right kind of data, how to integrate the data into your data foundation with the right integrity and quality, and how to manage the data on an ongoing basis in a way that the value of the data is maximized. Leading organizations that are high on the maturity curve have the capability to store poly-structured data using scalable data technologies, have data governance and data stewardship processes established to govern the use of data, and have established data democratization to ensure that the data is easily accessible for a variety of uses.
3. How sophisticated are our analytics? It’s quite important to define analytics and become aware of where you stand. Ask yourself: What’s the mix of transactional analytics (the “what”) vs. strategic analytics (the “so what” and “now what”) in our organization? How much of our analytics are descriptive vs. prescriptive? How many of our analytics-driven insights are leading to actions? The degree to which you can attach a business outcome to an analytics-driven insight determines your analytics sophistication.
4. What’s our operating model to deliver analytics? A sustained operating model and governance process for your analytics group is key to becoming analytically mature. An operating model comprises an optimal organizational design with clearly defined roles and responsibilities to meet the business needs of your commercial stakeholders (such as the sales, marketing and pricing teams), a governance process for the intake of requests and prioritization based on business impact and feasibility, the ability to assess and communicate the impact of the analytics efforts and, finally, the ability to industrialize and standardize oft-repeated questions to create scale and reduce inefficiencies.
5. Are we nurturing analytics talent in the right way? Despite the buzz around artificial intelligence, we believe that people are the single most important dimension to drive success in your analytics organization. While AI and robotic process automation can take care of the transactional analytics, you’ll need people to drive strategic analytics, and to bring in the collaborative and empathetic elements—what corporate strategist and writer Frederic Laloux refers to as “teal” elements of management’s evolution—to your group.
Talent in an analytics organization can range across four broad profiles: analysts, technologists, process experts and researchers. Regardless of the number of roles, each team member should have a clear understanding of his or her role and responsibility, a competency model and a clear career progression path based on that competency model. There should be programs to “onboard” and “upskill” team members at various levels, as well as the ability to complement their skills through an outsourced model to take care of short-term capacity and capability crunches.
6. Are we building the right analytics infrastructure? Based on your organization’s stage of business growth and the maturity of other dimensions, you should make purposeful investments in the right analytics infrastructure to enable production of a wide variety of analytics, the right tools and technologies to meet the evolving needs of the analysts and consumers, and the ability to scale up and down based on the needs of the business. Many leading medtech organizations are adopting a hybrid model, which involves leveraging the on-premise assets, such as the enterprise data warehouse, as the data foundation, and working with an insights services provider to move all analytics and reporting to the cloud. The right infrastructure should enable you to democratize data and analytics, and to do so rapidly.
Building a world-class analytics organization is a journey that will require discipline and a willingness to change. Defining a charter followed by a clear plan to address these six questions will increase your organization’s likelihood of moving the commercial needle.
BLOG POST: Three Steps for Medtech's Analytics Success
BLOG POST: Five Key Data and Analytics Trends for 2017