Decisions Exercise: Where and How To Start the Big Data Journey

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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:
Where and how do you start to address this initiative?
What data might you need to support this initiative?
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.
Step 1:  Identify and Brainstorm the Decisions
“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:
What streets and intersections need maintenance?
What storm sewers are blocked?
What is blocking those storm sewers?
What sort of maintenance is needed?
What is the impact of street cleaning and debris removal on flooding?
What streets and intersections should we fix first?
How busy are the streets and intersections?
What worker skills are needed to fix the street?
What equipment and materials are needed to fix the street?
What time of the day / day of the week is ideal for doing that maintenance work?
How many workers are available?
Do I have access to temporary workers?
How much overtime can I afford?
How do I warn residents that a road is flooded?
What options do I give residents when the major arteries are flooded?
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).
Figure 1: Brainstorm Decisions Across Different Stakeholders
Some key process points about Step 1:
Allow individuals to brainstorm on their own at first. When it is entirely a group exercise, some folks go quiet and we potentially lose some good ideas.
Be sure to capture each decision on a separate Post-It note for later usage.
Place the decisions/Post-it Notes on a flip chart (or two).
You don’t need to group decisions by business function. I just did it here to demonstrate the process.
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.
Step 2:  Group Decisions Into Use Cases
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
What streets and intersections should we fix first?
What streets and intersections are busiest at what times of the day?
What are the alternative route options during maintenance?
What are the alternative transportation options during maintenance?
What business parks or malls will be disrupted by the maintenance work?
Which streets and intersections raise safety concerns for bikers and pedestrians?
Estimate Maintenance Effort
What streets and intersections need maintenance?
What storm sewers need maintenance?
How much maintenance is needed?
What type of maintenance is needed?
What worker maintenance skills are needed?
What types of equipment and materials are needed?
Optimize Maintenance Effort
What worker skills are needed to fix the street?
How many workers with those skills are available?
What equipment is available to fix the street?
What tools are needed to fix the street?
What materials (concrete, asphalt) are needed to fix the street?
How effective is street cleaning and debris removal in preventing flooding?
Minimize Traffic Disruptions
Which streets are bottlenecks for schools and at what times of the day?
Which streets are bottlenecks for shopping malls and at what times of the day?
Which streets are bottlenecks for business parks and at what times of the day?
What are the alternative route options?
What are the public transportation options?
Minimize Maintenance Costs
How many workers are available?
To what temporary workers do we have access?
How much overtime can I afford?
How much maintenance budget is available?
Improve Resident Communications
What streets and intersections are likely to need maintenance?
What are alternative travel routes?
What are alternative transportation options?
Increase Resident Satisfaction
How many residents did the flooding impact?
How long were those residents impacted?
What comments or feedback are most important and/or relevant?
What phone calls are most important and/or relevant?
What social media postings are important and/or relevant?
See Figure 2 for an example of how the end point of Step 2 might look.
A key process point about Step 2:
Ideally you will end up with 7 to 12 use cases. If you have fewer than 7, then look for ways to break up some of the groupings.  If you have more than 12, then look for ways to aggregate similar use cases.  Not sure why, but 7 to 12 use cases always seems to work out to the right level of granularity in the use cases.
Step 3:  Prioritize Use Cases
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).
Figure 3: Prioritization Matrix
Guiding the Envisioning Process: Prioritization Matrix Worksheets
Summary
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.
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About Bill Schmarzo
CTO, Dell EMC Services (aka “Dean of Big Data”)
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.
Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.
Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.
Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.