4 steps to making data work for you

Do terms like “Big Data,” “Machine Learning,” and “Artificial Intelligence (AI)” excite you? Do they also cause a bit of anxiety? If so, you’re not alone.

A 2021 report by NewVantage Partners reveals that by May 2021, 57 percent of healthcare and Life Science companies were already managing data as “critical business assets,” and over 90 percent were accelerating the pace of investment in Big Data/AI. However, fewer than 25 percent felt they had successfully created a “data culture.” So what can nontechnical stakeholders do to help build a successful outcomes-oriented and data-driven culture?

Achieving success in data and analytics

Implementing Data Science solutions can feel overwhelming for those outside of the function, and it can sometimes be difficult to know where to start. For this reason, it’s critical for today’s analytics teams to have highly client-focused approaches. As a consumer of analytics services, you’ll achieve better outcomes when consistently engaged as an integral player who is along for the journey to project and product success. There’s no benefit to making Data Science feel like a mystery.

From our perspective, as an analytics partner, we know you understand your business better than anyone, and business understanding is the first and most important of the four phases of analytics success. Let’s discuss those four key phases now:

Phase 1: Understanding the business

It takes two to tango. In the early phases of a successful data project, there is a substantial emphasis on both the “human” subject matter expert knowledge and the data sets available for use. This knowledge and data are explored collaboratively by the business and Data Science experts.

Phase 2: Identifying opportunity

Next, armed with insight from Phase One, data experts collaborate to understand what key questions can be answered by using available analytical and AI tools. If these questions align perfectly with your business needs, awesome! If not, it may be necessary to explore additional data sets or alternative tools or look at the business insights from Phase 1 from a different angle.

Phase 3: Develop, iterate and communicate

The development stage is characterized by iterating to create a valuable analysis. In this phase, communication is critical because we rarely expect the first analysis to be the last. Success is contingent on the adaptability of the team, business knowledge and the ability to identify when results make sense in the context of the business opportunity.

Phase 4: Results and building value

The final and most exciting phase is deploying an insightful solution that delivers business-friendly and valuable results. The difference between “buying data” and deploying an analytics solution is how analytic techniques such as scientific modeling help translate information and impact the opportunities we identified in Phase 2. Success is the feeling that you know more, question less and have greater confidence in your business decisions.

Bonus tip: The potential pitfalls of “turnkey”

You may notice these four phases emphasize collaboration and the value of information uniquely known by you and other business experts. It may be tempting to try bypassing this process and seek solutions that deliver insights with little or no collaboration or “human touch.” However, these tend to offer greatly diminished value. Although machine learning is unbelievably good at producing complex, science-based analyses, experienced analysts are exceptionally skilled at finding the useful picture in all that output. These experts can help you connect the dots and maintain the human touch.

Final thoughts

Knowing when to use data and analytics and how to successfully partner with analytics teams is a great first step toward reaping the rewards these advanced technologies can bring to Life Science companies. The great news is that you don’t need a Data Science degree to help build a data-driven culture. In an ideal process, the partnership between analytics teams and business leads is both fluid and collaborative. There is much one can learn from the other.