Decisions Exercise: Identifying Where and How To Start the Big Data Journey
- April 17, 2017
The recent deluge of rains in Northern California have flooded streets, brought down trees and plugged storm sewers. As I was trying to make my way around the neighborhood, I thought of a classroom exercise to help my MBA students to identify the use cases upon which they could focus data and analytics. In this exercise, I’m going to ask my students to pretend that they have been hired by the city to “Optimize Street Maintenance” after these rainstorms. In particular, the students need to address the following questions:
These are classic questions that I hear all the time when I meet with clients about their big data journeys. Let’s walk through how I’ll teach my students to address this challenge.
“Where and how to start?” is such an open ended question. How does one even begin to think about that question? We recommend that organizations start by identifying the decisions that need to be made to support the targeted business initiative, which is “Optimize Street Maintenance” in this exercise.
I will break up the students into small groups (3 to 5 students) and ask them to brainstorm the decisions that need to be made with respect to the “Optimize Street Maintenance” initiative. Those decisions could include:
This brainstorming is much more effective when you have brought together the different business stakeholders who either impact or are impacted by the “Accelerate Street Maintenance” initiative (see Figure 1).
Some key process points about Step 1:
Finally, “all ideas are worthy of consideration.” This is the key to any brainstorming session; to create an environment where everyone feels comfortable to contribute without someone passing judgment about his or her thoughts or ideas.
Next, we want to group the decisions into common subject areas or use cases (which is much easier to do if each decision is captured on a separate Post-It note). I will bring all the students together around the decisions on Post-it Notes, and have them look for logical groupings.
Looking over the decisions captured above, we can start to see some natural “Accelerate Street Maintenance” use cases emerging, such as:
Prioritize Streets and Intersections
Estimate Maintenance Effort
Optimize Maintenance Effort
Minimize Traffic Disruptions
Minimize Maintenance Costs
Improve Resident Communications
Increase Resident Satisfaction
See Figure 2 for an example of how the end point of Step 2 might look.
A key process point about Step 2:
Not all use cases are equal, and some use cases are dependent upon other use cases. The prioritization matrix takes the different business stakeholders through a facilitated process to prioritize each use case vis-à-vis its business value and implementation feasibility (see Figure 3).
For more details on the prioritization process, check out these blogs:
The news really surprised no one: “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” From the press release:
“The partnership between IBM and one of the world’s top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year. MD Anderson is actively requesting bids from other contractors who might replace IBM in future efforts. And a scathing report from auditors at the University of Texas says the project cost MD Anderson more than $62 million and yet did not meet its goals.”
If big data were only about buying and installing technology, then it would be easy. Unfortunately, companies are learning the hard way that the “big bang” approach for implementing big data is fraught with misguided expectations and outright failures.
Organizations are so eager to realize the business benefits of big data, that they don’t take the time to do the little things first, like identifying and prioritizing those use cases that offer the optimal mix of business value and implementation feasibility. While I applaud all efforts to cure cancer (my mom died from cancer, so I have a vested interest like so many others), sometimes “curing cancer” might not be the best place to start. Identifying and prioritizing those use cases that move the organization towards that “cure cancer” aspiration is the best way to achieve that goal.