If you have an organization that is trying to develop more analytical maturity than it has today, the answer to how you create buy-in may lie in the notion of a “prototype”. Buy-in is important in getting true lift-off with any idea, but it is certainly true with analytics because of how frequently many departments in an organization utilize various levels of analytics.

There can be numerous obstacles to creating or sustaining buy-in for analytics.  Individuals may not want to share data or ideas. They may not find the analysis to be valid; or, they may not find the analytics to represent the data well. The analytics may have limited direct-impact for them or their department. Additionally, individuals may be simply unwilling to accept automation.

Over time, our group has come to see that analytic-prototypes gaining the greatest level of buy-in from organizations have 4 qualities.

Showcase Something Difficult to Solve

Most organizations have some aspect of what is considered a difficult problem to solve. It is critical to pick a worthwhile problem to solve, but it shouldn’t be so challenging that the probability of success is questionable. It is important to note that if the problem you aim to solve is not substantial enough, then individuals within the organization will most likely doubt the strength of the analytics capability you are setting out to prove.

Key take-away: help other organizational users develop confidence in your analytic capability by solving worthy problems

Generate a High ROI

 Individuals will not simply hand over their trust. You should recognize that you will have to earn it, and the truth is you should want to earn it. If you go after a reasonably difficult problem to solve, but you are not able to communicate the value that your solution brings, your analytics prototype may be seen as worthless. Business users are not simply looking to be intellectually stimulated by what you present; they are an action-oriented people group. If you can adequately demonstrate that your solution has value and creates a high return-on-investment for them, the probability of generating analytic buy-in is high.

Key take-away: you must be clear what the solutions means to the organization

Be Continuous In Your Improvement

Imagine I told you I had a reasonably difficult problem to solve for the company, that it would generate an 107% ROI, but that it would take me 3 years to collect the data before I could deliver any analysis. Most organizations in the modern world (even small and mid-sized companies) operate at a much faster pace than they did just 5 years ago. You should aim for a quick success, and know your solution doesn’t have to be perfect to gain buy-in and inspire individuals to take action. If the problem you are attempting to solve will require a long time to simply collect the data, or the analysis is so complex that you will not be able to see results for some time, then choose something else in which to invest your analytic capabilities.

Key take-away: quick successful wins are more important than perfect wins that miss the corporate window of opportunity; great solutions will be iterative

Connect Groups That Make Sense

It is easy for any of us to become so focused on solving a problem that we suffer from tunnel vision. Being inclusive when it comes to connecting groups is critical. This open-minded approach allows you to turn your efforts toward gaining as much corporate buy-in as possible for your analytics capability. One such way of accomplishing this is to understand relationships and associations between the groups that you are solving for, and to determine if you can envelope their departments into the solution. It will not be beneficial if you are the only one that feels that a particular group of departments should be working together and sharing insights. Your group connections should resonate with all departments in your suggested grouping. If all of the departments recognize they should be a part of the solution you create, but each does not experience positive results from your solution – or the solution really is only meaningful to a small portion of stakeholders – you will limit buy-in.

Key take-away: find the relationships/associations of departments, however uncommon, and provide the greatest possible value to all of them

Our Approach Works

In our company’s data science department, we are challenged with unique and difficult problems in all aspects of healthcare organizations. In every case we have created products using the above framework. We look for reasonably difficult problems to solve with a high-ROI to our clients business, something that we can quickly win and become the standard of excellence over time in, and something that is rooted in multiple departments.

Great things in data science are built on a series of small things brought together. This includes analytic buy-in.

Here are some other posts:

• How to Build An Analytics-Driven Culture
• How Data Science Solves Healthcare Problems
• What Has Changed?
• Exactly How Does Data Science Work, Really?
• What Is Data Science Anyway?

BIO: Damian Mingle is Chief Data Scientist for WPC Healthcare, a premier provider of cloud-based operational, financial, and clinical analytic solutions. In this role, Mingle manages a team of experts transforming data into meaningful strategic insights and offering hospital systems, payers, and the HIT vendors descriptive-through-prescriptive analytics. Prior to WPC Mingle held positions with companies like Hospital Corporation of America (HCA), Coventry Healthcare, and Morgan Stanley. He is ranked in the top 1% globally as a data scientist through regular competitions.