Understand Analytics to Drive Results

Analytics is not a new buzz word. Data analytics have been applied to improve business results since the early 1980s, particularly in the financial services industry. Yet there’s still confusion about what, exactly, “analytics” means, and how and why to apply the science of data analytics within the healthcare industry.

A bit of puzzlement is understandable because analytics isn’t a “one size fits all” proposition. There are various types of analytics and some most definitely are more valuable than others—especially when it comes to optimizing revenue cycles!

Another challenge lies on the data side. Specifically, unchaining the vital data trapped in organizational silos. A whopping 82 percent of company IT leaders said they have a “high” or “moderate” degree of data silos. They also said integrating data is their second highest challenge, with “speeding up data analysis” ranking as the number one challenge. (Trends in Data Management online survey conducted by CompTIA in December 2019.) It’s no different in healthcare, where only between 10 to 20 percent of the value of data and analytics is being captured. (McKinsey & Company)

Why bother with analytics at all?

Despite the complexity, a strong data analytics strategy is extremely valuable in generating bottom line results. Cutting through the clutter means asking the right questions and engaging the right technology to achieve clearly defined goals. When done properly, results are big and scalable: cutting costs and increasing revenue chief among them.

A few definitions might help clarify the vagueness casting shadows on truly understanding and harnessing the power of data analytics in healthcare. To keep it simple, we’ll focus on the three principle types:

Descriptive: As you probably guessed, descriptive analytics refers to the reports and dashboards that measure things that have happened in the past. For many in healthcare, descriptive is the extent of their analytics. Unfortunately, descriptive analytics don’t explain the “why” behind trends, or more importantly, “how” to influence them. Some might say that descriptive analytics really isn’t analytics at all, but merely reporting.
Predictive: Again, it sounds like what it is: a look at past trends and behaviors to predict what will happen in the future. While more sophisticated and useful than simply describing what has happened, predictive analytics also fall short.
Prescriptive: The holy grail of data analysis, prescriptive analytics not only eliminates guesswork and experimentation, it allows organizations to shape consumer behavior in ways that support important organizational goals.

Next time we’ll take a closer look at prescriptive analytics and how to use it to drive true insight and organizational results!