With the field of predictive analytics becoming a more mainstream business discipline, the end objective for many organizations is to operationalize this discipline in order to truly leverage the full business impact. The notion of “operationalizing” this discipline is that its solutions are used and applied by all sectors of the organization.
With the field of predictive analytics becoming a more mainstream business discipline, the end objective for many organizations is to operationalize this discipline in order to truly leverage the full business impact. The notion of “operationalizing” this discipline is that its solutions are used and applied by all sectors of the organization. Predictive analytics within this context is a key component of the corporate DNA or culture. But how do organizations evolve into this mindset keeping in mind that the operationalization of predictive analytics is still the exception rather than the norm.. Like any discipline to become mainstream, it needs to be socialized throughout the organization. But this is easier said than done. Let’s see why?
One of the biggest barriers in the socialization of predictive analytics is the inability to demonstrate what the results of predictive analytics projects truly mean to the overall organization. This poses a significant barrier towards the goal of “operationalizing “ predictive analytics within the corporation. But what is causing this barrier?
The “business” people often have difficulty with this discipline as their initial perception is that this discipline relies heavily on advanced analytics or statistics. Most business folks have limited knowledge of stats and have forgotten the basics of Stats 101 which they learned in 1st year business school. The data scientists or analytics practitioners, being well-versed in stats, will build tools and solutions where the solution’s benefits utilize statistical jargon that is the familiar terrain of the data scientist. However, the business people cry the familiar refrain: “I don’t understand and what does this mean to the business” . The real bottom-line impact of these solutions is never effectively communicated to those key business individuals. As a result, companies are unwilling to invest in further resources thereby creating this catch-22 where analytics proponents cry the familiar refrain “How can I demonstrate benefits without the necessary resources”. This circular debate leads to frustration for c-suite executives and for analytics practitioners who in many cases will opt for jobs in greener pastures. The real answer in addressing this dilemma is that the business people need to clearly understand the $ benefits of predictive analytics while at the same time having a solid understanding of what comprises the solution. This does not mean that the business people need to take refresher courses in stats but they do need to gain enough understanding such that predictive analytics is not viewed as this gigantic “Black Box”. But how do we accomplish this?
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Let’s take the simple example of a simple response model to a marketing offer. In this situation, it is evident what the data scientist/analyst needs to do. Obviously, the creation of a solution that best targets people most likely to respond is the desired objective Statistical jargon is eliminated as it is the data scientist’s responsibility to understand the statistics behind the output. Yet, the data scientist needs to take this output and to then transform it to output that is meaningful to the business end user. In the case of this simple response model, we can do the following:
- Describe what the ideal responder would look like
- Demonstrate the $ benefits of the model
Different types of reports such as EDA(Exploratory Data Analysis Reports),decile/gains tables,etc, can be produced with the objective of conveying learning and insight that the business understands . But reports in and of themselves are only one step in the socialization of predictive analytics. Verbal communication is the 2nd key component here. This can comprise one on one meetings or small group meetings with the business end users. These more informal type meetings, albeit requiring more work, allow the analyst to build a network of strong support in this area.
At one organization ,we built credit-risk models where every Friday morning, small informal meeting were held to different groups of client service reps. Both myself(the modeler) and the director of client services conducted sessions with a short presentation along with a Q & A that would help to illustrate what the solutions meant to the client service rep. The client-service reps were then able to integrate these solutions as part of their decision-making process with clients.
Through this above type approach , we are in effect building a network of strong allies for that all-important C-Suite presentation for more funding as we attempt to expand this discipline throughout the organization with the end objective being the operationalization of predictive analytics throughout the organization. But socialization represents the “soft skill” of predictive analytics that is most required to achieve our end objective .