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I’ve recently participated in a number of executive forums on the rewards and realities of creating data-savvy and analytically-enabled cultures. Interestingly, one key theme comes up repeatedly in audience Q&A: how to make the case? Seems simple enough, but this step often causes those who understand the opportunity and problem space well to falter.
Why? Well, we don’t answer “why” properly (if at all). Rather, we fall prey to the curse of the expert. Diving into specific capabilities and complicated implementation tactics – complete with dizzying charts and lists of issues or risks – from the get. Well before ensuring our audience understands WHY and then WHAT we are talking about in simple sensible terms.
First: Why.
The wrong why? We need to make sure people use our data properly. Because our EMR doesn’t play nice with others. Because the problem is complicated and we need an algorithm to understand it. Because we have too many systems that all have their own data. Because one agrees on what [or who] a customer is. Because our data warehouse can’t handle social media data. Because everyone else is doing “it”.
These are (almost) all perfectly valid rationales. The problem in these problem statements? They don’t answer why solving the problem matters. Or why addressing the item at hand is more important than the laundry list of other opportunities or issues confronting the company.
More specifically, these rationales don’t provide a why that resonates with overarching corporate and personal objectives. Why? Because we aren’t relating to what the company or individual is passionate and/or compensated and measured against.
A better why? To save lives. To reduce the number of avoidable medical complications. To reduce ER overcrowding and improve outcomes. To prevent juveniles from graduating from the foster system to adult detention [aka prison] where they become customers for life. Because we are cannibalizing our customer base and alienating our most loyal customers in the process.
Articulated properly, why simply answers: how does this capability create revenue, increase productivity, lower costs or complexity, mitigate risk or improve regulatory compliance? What <mission, objective or outcome> will this help us achieve?
Sell the why and the “what it takes” conversation becomes much easier.
Then: What.
Care must also be taken at this stage to avoid the curse of the expert. Popular taglines including big data, data governance, data management, BI, and analytics are umbrella terms. Each refers to dozens of discrete capabilities or practices. All perhaps related, but not all relevant in any given context.
Therefore, as the so-called experts, it is incumbent upon us to clearly define what, explicitly, we are asking. Master data management? Creating a consolidated view of a patient’s encounters across departments and providers. Analytics? Well, it depends. Forecasting ER demand over the next 3 months. Or, predicting which patients are at risk for complications or non-compliance. Or, reporting number of adverse medical complications. Any or all of the above?
These are the easy examples: big data? Data science? Mention the term and no one will admit ignorance. They will not be lying: everyone has heard of and has a mental model or impression of what these things mean. Whether their definitions agree, well…
A senior executive recently came to this realization after a grueling discussion: “After a lengthy discussion, I realized we were using the same term to mean different things. As a result, there was no agreement on what we had just agreed to.” Sound familiar?
When it comes to enlisting support and marshalling the troops, the specifics matter. Else? Agreement in principle will lead to inaction in practice.