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I believe organizations fail in creating sustainable business value with Big Data capabilities because of the Peter Principle
Jul. 7, 2017 02:00 PM
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).
Figure 1: Big Data Business Model Maturity Index
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.
“Peter Principle”: The Destroyer of Great Ideas
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:
- “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.” Never before have we had an opportunity to create and leverage superior customer, product, operational and market insights to disrupt business models and disintermediate customer relationships…never. Consequently, current business leadership lacks the experience to know what to do to make this happen. Organizations likely need a new generation of management (which we are seeing in the “born digital” companies like Amazon, Google, Uber and Netflix) or a massive un-education/re-education of their current business leadership (like what we are seeing at GE…more to follow on the GE transformation, so keep reading!!) to realize that analytics is a business discipline to drive differentiation and monetization opportunities.
- “Managers rise to the level of their incompetence” which means that those in power are very reluctant to embrace any new approaches with which they are not already familiar. And we have all met these folks who can’t embrace a new way of thinking because they are so personally or professionally invested in the old way of thinking. Consequently, new ideas and concepts die before they are even given a chance because these folks are threatened by any thinking that did not get them to where they are today.
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!!
Decision Modeling: Predictions Exercise
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).
- First, list the use cases. In Table 1, we will start with just one use case: “Increase Store Traffic Via Local Events Marketing.”
- Second, list the decisions that one would to address to support the use case. For example, we would need to make a decision about “Which local events to support and with how much funding?”
- Next, for each decision, brainstorm the predictions that one would need to make to enable the decision. It’s useful to start the predictions statement with the word “Predict.” For example, in support of the “Which local events to support” decision, we would need to “Predict attendance at the local events”.
- Then, list the potential analytic scores that could be used to support the predictions that we are trying to make. The potential scores were identified in Step 7 in the “Thinking Like A Data Scientist” methodology, but this decision modeling exercise gives us a chance to validate and expand upon those potential analytic scores.
- Finally, brainstorm the potential variables and metrics that might be better predictors of performance. Step 6 in the “Thinking Like A Data Scientist” methodology identified many of those variables and metrics, but again this decision modeling exercise gives us a chance to validate and expand the potential variables and metrics.
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?
- Predict attendance at local events (sporting events, concerts)
- Predict composition of attendance at local event (parents, kids, teenagers)
How much staff do we need to support the local events?
- Predict how many workers are required by hour to staff the store
- Predict what special skills are needed by hour to staff the store
- Predict how much overtime might be required
How much additional inventory do we need?
- Predict how much additional food inventory is required to support the local event
- Predict how much many additional utensils and bowls inventory required to support local events
- Predict store waste/shrinkage
- Predict when we need to replenish store inventory and with what
From what suppliers do we source additional food inventory?
- Predict suppliers excess capacity by food item
- Predict time-to-delivery for food inventory replenishment
- Predict (prioritize) what suppliers to engage for additional food procurement
- Predict quality scores of the new suppliers
|Economic Potential Score
- Local demographics
- Increase in home values
- Local economic indicators
- Local unemployment rate
- Change in city budget
- Average income levels
- Average education levels
- Number of local IPO’s
Local Vitality Score
- Miles from high school
- Miles from mall
- Average mall attendance
- Miles from business park
- Number of college students
- Number of local sporting events
- Number of local entertainment events
Local Sourcing Potential
- Number of local suppliers
- Miles from stores
- Supplier production capacity
- Supplier quality
- Supplier reliability
- Delivery feasibility
Table 1: Predictions Exercise Worksheet
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.
Summary: The GE Story
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:
- Transforming their operating model with the creation of GE Digital to help lead their digital business transformation.
- Creating a partner open software ecosystem that enables collaboration with partners and third-party developers to deliver business and financial value for all participants (Customer, Partner and GE).
- Transforming (un-education and re-education) management leadership with lean startup principles that emphasizes iterative innovation, space to experiment, and a fail-fast mentality.
- Exploring new or alternative business models by focusing on delivering outcomes and creating sustainable business value with big data capabilities.
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.
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