How-To Create Corporate Buy-in for Powerful

Analytic Solutions

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

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.