How to Make Analytics Projects Self-Funding
How to Make Analytics Projects Self-Funding
Getting Complex Business Problems Solved “for Free”
By: George E. Danner
The great paradox of corporate life is this: the companies with the greatest need for analytics at the are the same ones that have the biggest financial challenges to the investment in analytics. Often the financial challenge itself is driving the need for analytics. Perhaps this is a company whose industry has undergone a massive disruption. Or perhaps it is a company that has grown tremendously and is caught between small company tools and big company aspirations and needs. Or maybe it is a company trying to catch up to its competitors in the level of sophistication of its product or delivery functions. In any case, these are frequently the companies that struggle to invest in things like optimization models or forecasting techniques, or even algorithms to price the product better. If left unchecked year upon year, the company will be at a decided disadvantage to more capable competitors, or perhaps susceptible to a customer base that increasingly demands more sophisticated suppliers.
We understand. Budgets are hard. The pressure to squeeze every dime from investments is unrelenting. Wouldn’t it be great if I could just get the data-driven discipline and problem solving for free?
We believe there is a way to do this. We call this concept self-funding. Here’s how it works: by carefully choosing certain analytics projects at the tail end of the fiscal year, executing those projects with precision early in the following year, and realizing minimum breakeven returns later in the year you can effectively, on an annual basis, get analytics capability in a P/L neutral way. In other words, free.
Implementing the Self Funding Idea
The process begins somewhere around 3Q in the prior fiscal year. Companies will gather some of their very best talent to start “ideation” around the kinds of business problems that would be very valuable to solve. Perhaps a cursory ROI calculation goes along with all of the ideas that are generated in order to shorten the list. What we have found over many years of working on business models is that companies generally don’t lack for ideas—rather they lack the analytical mechanisms and resources to carry the ideas forward into execution.
Ideation in practice looks like whiteboarding. The language of business problem solving is diagrams, so we encourage lots of clear, simple diagrams to explain the problem, and to discuss what a model of that problem might look like. What are the data inputs? What does the model produce as its output? What is the right scope and granularity of the model? What is the right kind of visualization form? These are the kinds of questions that teams should answer using a series of well structured diagrams with layers of commentary. We also encourage companies to save all of the material—a problem that doesn't make the cut one year may be pressing the next.
Project selection is vital. Not every great analytical problem-solving project meets the criteria of self funding. For example, projects can be too large to generate returns in the same year that they are executed. Rather, we look for projects with a very tightly controlled scope—let’s say an operational improvement that is only applied to one production line or one product category. Scale across similar functions comes later when we have the financial momentum to do so.
We usually encourage a handful of projects to be included in the mix. Some might fail, some might run into technical barriers, still others might perform spectacularly well. By having a small portfolio of projects we reduce the risk of failure of any one effort.
Execution is where the real investment begins—both dollars and resources.
Projects that are self funding tend to be tightly scoped, and the execution is similarly controlled. Agile teams have a blueprint that had been previously created in ideation, along with Subject Matter Experts (SMEs) at the ready to guide the teams through the complexities of the business problem. Often it makes sense to supplement less experienced teams with experienced analytics practitioners, including experts outside of the company. In self funding there is little room for error or slowdowns—knowledge and experience serve as valuable insurance against this.
The cursory ROI models that are generated in ideation are brought forward and refined to serve as a guide for the assessment of actual return as the solution(s) go live mid year.
After the FY is over
Two positive outcomes happen in companies that engage in self funding. First, they get valuable business problems solved, often problems that have been hanging around for years with no energy or focus to solve them. And the benefits continue for years, as companies find new ways to juice even more value from the initial solution.
Second, and perhaps more importantly, companies begin an important journey to the process of becoming an analytical problem-solving culture. People will think deeper, and in a data-driven way about the corporate functions they are part of, and perhaps will use the self funding process year on year to promote candidate business problems toward solution.
And the best part of all, it's free.
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