Step 3: Brainstorm and Prioritize Decisions (Use Cases)

The next step in the Value Engineering process is to brainstorm the Decisions that each of the different stakeholders needs to make in support of the targeted business initiative. My findings are that if you identify the right set of stakeholders in Step 2, then the brainstorming and prioritizing of decisions flows very quickly and naturally. Why? Because these stakeholders inherently know the decisions that they have to make in support of the business initiative as they have been trying to make these decisions for year…decades…maybe even generations. Examples of such decisions include:

• Who are my most valuable customers?
• Which students are at risk of attrition?
• What products are likely to break?
• How much inventory am I going to need?
• Which marketing promotion is optimal for the target audience?
• What's the optimal price?
• What's the optimal discount to get the customer to buy?
• What are the right dietary recommendations for this individual?

My observation is that while the decisions have not changed over the years, what has changed—courtesy of massive datasets and advanced analytic algorithms like AI, Machine Learning, and Deep Learning—are the answers. And that's where the Data Scientists who are trained to optimize decisions come into play.

DEAN OF BIG DATA TIP:

A Decision by its very nature is actionable; a conscious pronouncement to take an action. A Question, on the other hand, is useful for validating information or provoking out-of-the-box thinking, but on its own does not imply an action to be taken.

At this stage of the Value Engineering process, we need to aggregate decisions into Use Cases or clusters of decisions around a common subject area that have measurable financial ramifications. To facilitate the aggregation of the decisions into use cases, we use the frame of the targeted business initiative to guide the process.

In Figure 2.3, the targeted business initiative is to "Increase Same Store Sales." On the left side of Figure 2.3 are the brainstormed stakeholder decisions that support the targeted business initiative. Then we use the "Increase Same Store Sales" business initiative to group the individual decisions into clusters of decisions (use cases) around common subject areas such as increase store traffic, increase shopping bag revenue, and increase corporate catering.

Figure 2.3: Pivoting from Decisions to Use Cases

We then label the use cases in Figure 2.3 in an Action Format:

1. Identify the appropriate [Verb]. Example Verbs could include Increase, Decrease, Optimize, Reduce, Consolidate, Rationalize, and so on.
2. Identify the [Metric] we are looking to impact. Examples Metrics could include Customer Retention, Margins, Visits, Inventory, Unplanned Downtime, Fraud, Waste, Shrinkage, and so on.
3. Give the use case a [by X%] goal. Note: you don't need an exact goal at this time in the Value Engineering process. It is sufficient just to use the generic goal of [by X%].

Finally, we use the Prioritization Matrix to drive consensus across the different stakeholders on the top priority use cases based upon the value and implementation feasibility of each use case vis-à-vis each other over the next 9 to 12 months. The Prioritization Matrix process provides a framework for driving organizational alignment around the relative value and implementation feasibility of each of the organization's use cases (see Figure 2.4).

Figure 2.4: Prioritization Matrix

Some key points about the Prioritization Matrix:

• The Prioritization Matrix process weighs the "value" (financial, customer, operational, and environmental) of each use case against the implementation feasibility (data, architecture, technology, skills, timeframe, and management support) of those same use cases over the next 12 to 18 months.
• The Prioritization Matrix process gives everyone an active voice in the identification, discussion, and debate on use case value and implementation feasibility.

The Prioritization Matrix is the most powerful business alignment tool I've ever used. It works every time…if you do the proper preparation work and are willing to put yourself in harm's way as the facilitator.

DEAN OF BIG DATA TIP:

Note: Steps 1 through 3 are covered in excruciating detail in my previous book, The Art of Thinking Like a Data Scientist, including templates and hands-on exercises.

After completing Step 3, everything else is "easy" because now you have a framework against which to make the analytics, data, architecture, and technology decisions.