Almost every company wants to drive decisions based on data and insights and healthcare is no exception. In our experience, everyone is jumping on big data technologies like AI, machine learning, robotic process automation and hiring data scientists to answer business questions. What these companies often fail to do, however, is look at their analytics projects holistically so they can build a sustainable and scalable global analytics capability with the right operating model and processes. They can also struggle with gaps in their thinking, like not considering how “shiny,” new capabilities will tie together with existing analytics, how to evaluate the success or failure of a proof-of-concept or how to scale after a successful pilot. And the list of challenges goes on.
In this series, we’ll look at how to set-up an analytics program that is scalable, sustainable and highly functional by delving more deeply into these three key success factors:
- Be thoughtful about your global operating model. In the firms that are going global with their analytics programs, we see a wide range of impact. One of the key differences between high- and low-impact programs is in the operating model. High impact capabilities have operating models that fit with the organization’s structure and culture across various countries and experts who understand both the analytics and the business. These models make it easier and more efficient for business stakeholders to adopt a new, data-driven approach.
There are so many ways to design an operating model, it would be overwhelming to cover them all. We believe, however, that everyone should start by assembling a business-focused team and a capability-focused team and then deciding which team will do a majority of the execution and delivery. Which team handles more of these duties can vary depending on service lines. For example, ad hoc and quick-turnaround analytics are often executed by business-focused teams in each country while more scalable and widely applicable analytics are often executed by a capability-focused team sitting centrally.
You can organize these teams in several ways. Business focused teams can be organized by business unit, product, country or by activities (analytics design, execution, etc.) Capability focused teams can be organized by geography (local, regional, global); by function (omnichannel, forecasting); by analytics type (innovative, operational, basic, ad hoc) or by activities (thought leadership, execution, etc.) The key is to think systematically when organizing your teams.
- Design for innovation, scale and operations. We don’t see enough focus on enabling the twin engines of innovation and performance. Instead, we frequently see siloed analytics capabilities. For example, the data science team will drive innovation, but separately from the team that executes simple analytics. We believe analytics leaders need to think about the innovation-to-operations pipeline as one capability. What is innovation today should become operations tomorrow as it becomes embedded in the business process. This requires collaboration across data science, IT, analytics and operations. It also requires a well-defined and governed process that moves projects from innovation to scale to operations across countries along with management and adoption across diverse stakeholders and cultures.
- Focus on both production and consumption. In an over-simplified sense, analytics work happens in two phases: production and consumption. Production is about finding and producing business insights. Consumption is about adoption and the use of those insights to drive business decisions. To get real impact from your program, you need to get both production and consumption right. Production requires the right roles, filled by people who can understand the business questions and link them to the right data and best-in-class analytics methodology to produce the most relevant insights. In our experience, companies are more often focused on the production side, hiring business partners who sit with the analytics capability and collaborate very closely with business stakeholders and the analytics design and execution teams. This capability varies across countries so the fastest way to ensure consistency is through a global operating model.
We’ve seen limited to no focus on getting the consumption phase right. Even when analytics teams arrive at great insights, they fail to understand their impact or drive action. Getting consumption right is about driving change management in fragmented teams across countries. It involves changing the minds of entrenched middle management so that they make decisions based on data and not gut instinct. It requires storytelling capabilities and business understanding on the analytics team so that they can deliver recommendations in the language that the business speaks rather than analytical or technical speak. Last but not least, it requires alignment on how impact (linked to actions) will be measured at the start of each analysis.
The above three success factors will enable an organization to set-up a global analytics capability that delivers value and impact and helps the whole enterprise become more data and insights driven.