Peter Principle: The Destroyer of Great Ideas…and Companies
- April 24, 2017
Wikibon just released their “2017 Big Data Market Forecast.” How rosy that forecast looks depends upon whether you look at Big Data as yet another technology exercise, or if you look at Big Data as a business discipline that organizations can unleash upon competitors and new market opportunities. To quote the research:
“The big data market is rapidly evolving. As we predicted, the focus on infrastructure is giving way to a focus on use cases, applications, and creating sustainable business value with big data capabilities.”
Leading organizations are in the process of transitioning the big data conversation from “what technologies and architectures do we need?” to “how effective is our organization at leveraging data and analytics to power our business models?”
We developed the Big Data Business Model Maturity Index to help our clients to answer that question; to be able to 1) understand where they sit today with respect to how effective they are in leveraging data and analytics to power their business models, and 2) what is the roadmap for creating sustainable business value with big data capabilities (see Figure 1).
So why do organizations struggle if it’s not a technology or an architecture challenge? Why do organizations struggle when the path is so clear, and the business and financial benefits to compelling?
I believe that organizations fail in creating sustainable business value with big data capabilities because of the Peter Principle.
The Peter Principle is a management theory formulated by Laurence J. Peter in 1969. It states that the selection of a candidate for a position is based on the candidate’s performance in their current role, rather than on abilities relevant to the intended role. Thus, employees only stop being promoted once they can no longer perform effectively – that “managers rise to the level of their incompetence.”
There are two key points in this concept that are hindering the wide spread adoption of data and analytics to power – or transform – an organization’s business models:
How do you teach the existing generation of management to “think differently” about how to leverage data and analytics to power their business models? How does one get an organization to open their minds and stop focusing on just “paving the cow path,” but instead focus on data and analytics-driven innovation? Let’s try a little exercise, my guinea pigs!!
The Challenge: Can we transform business thinking by changing the verb from “automate” to “predict?” Instead of focusing on automating what we already know, in its place let’s try focusing on “predicting” what is likely to happen and “prescribing” what actions we should take.
“Automate” assumes that the current process is the best process, when in fact; there may be opportunities to leverage new sources of data and new data science techniques to change, re-engineer or even delete the process. Can we drive a more innovative approach by instead of focusing on “automation,” we focus on what predictions (in support of key business decisions) we are trying to make and prescribing what actions we should take?
Let’s demonstrate the process using the Chipotle key business initiative of “Increase Same Store Sales.” (Note: this decision modeling exercise expands upon Step 8 in the “Thinking Like A Data Scientist” methodology).
Table 1 shows the results of this process for one use case (Increase Store Traffic Via Local Events Marketing) that supports the “Increase Same Store Sales” business initiative.
|Chipotle Business Initiative: Increase Same Store Sales
|Decisions -> Predictions
|Increase Store Traffic Via Local Events Marketing
|Which local events to support and with how much funding?
How much staff do we need to support the local events?
How much additional inventory do we need?
From what suppliers do we source additional food inventory?
|Economic Potential Score
Local Vitality Score
Local Sourcing Potential
In the workshop or classroom, we would repeat this process for each use case (e.g., improve promotional effectiveness, improve market basket revenues). This analytics-driven approach can bring more innovative and out-of-the box thinking to the organization.
A recent article titled “You Can’t Outsource Digital Transformation” discusses what GE is doing to prepare for–if not lead–digital business transformation disruption. To quote the article:
“It’s the threat of a digital competitor who skates past all the traditional barriers to entry: the largest taxi service in the world that owns no cars; or a lodging service without any real estate; or a razor blade purveyor without any manufacturing.”
The author, Aaron Darcy, describes what GE is doing to “think differently” – that is to unlearn and relearn – regarding digital business model disruption. This includes:
Nothing threatens the existence of your business like the Peter Principle. An organization’s unwillingness to “un-education / re-education” will ultimately be the undoing of the organization. Because as IDC believes “By 2018, 33% of all industry leaders will be disrupted by digitally enabled competitors.” Ouch.