Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 2

Read the second and final part of this overview of the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.

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The first part of the CDO Toolkit provided the CDO with a process of identifying, assessing, and estimating the value of the organization’s data in support of the organization’s key business initiatives.

CDO also needs a process for capturing, refining and re-using the organization’s analytics in support the organization’s key business initiatives and the use cases. Let’s expand upon what we have already done as part of the CDO Toolkit to create a process and supporting framework for capturing, refining, sharing and ultimately monetizing the organization’s analytics.

What Are Analytic Profiles?

The capture, refinement and reuse of the analytics are built around a simple concept called the Analytic Profile. Analytic Profiles are structures (models) that standardize the collection, application and re-use of the analytic insights for the key business entities at the level of the individual (human or physical object). We build Analytic Profiles for each individual Business Entity such as customer, store, employee, product, supplier and local events (see Figure 10).

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Figure 10: Analytic Profiles

Analytic Profiles provide a framework for ensuring that the organization’s analytic efforts are being coordinated around a larger analytics master plan. Analytic Profiles enforce organizational discipline in the capture and application of the organization’s analytic efforts and minimize the risk of creating one-off, “orphaned analytics1.” Analytic profiles help organizations prioritize where and how to invest their valuable data science resources by forcing the identification of the analytics needed to support the organization’s top priority business use cases.

Map Analytic Profiles to Use Cases

Next we want to map the Analytics Profiles (Business Entities) to each use case to understand which Analytic Profiles are important across which use cases. To continue our Chipotle exercise, we map the Chipotle Analytic Profiles against Chipotle use cases (see Figure 11).

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Figure 11: Map Analytic Profiles to Use Cases

Brainstorm Variables and Metrics

For our top priority use case (which we prioritized in Figure 5), we want to identify or brainstorm those variables and metrics that might be better predictors of performance (remembering our definition of data science). To accomplish this, we will apply the “Predictive Questions” exercise.

In the “Predictive Questions” exercise, we will take a descriptive question that the business users are asking to monitor the status of the business (e.g., “What were revenues last week?”) and transform it into a predictive question with the addition of the following prefix:

What data might I want in order to predict [Descriptive Question]?

Applying the “Predictive Questions” technique, we come up with the following predictive question:

What data might I want in order to predict what will revenues be next week?

Then if you follow good facilitation techniques (see blog “How To Run A Workshop: Guidelines and Checklist”), we end up with a wide variety of metrics and variables that might be better predictors of performance (see Figure 12).

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Figure 12: Identify Potentially Predictive Variables and Metrics

You will want to capture each variable or metric on a separate Post-it Note, as we will want the ability to group these variables and metrics later.

Identify Potential Scores

In the next step, we bring the business stakeholders together in order to have them group the variables and metrics into common subject areas (and potentially brainstorm additional variables and metrics in the process). These subject areas might be utilized to develop “scores” that would support the key decisions that make up the prioritized use cases (see Figure 13).

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Figure 13: Create Actionable Scores

Scores are a dynamic rating or grade standardized to aid in comparisons, performance tracking and decision-making. Scores help to predict likelihood of certain actions or outcomes (e.g., FICO score measures the likelihood of a borrower repaying their loan). Scores are actionable, analytic-based measures that support the decisions your organization is trying to make, and guide the outcomes the organization is trying to predict.

We identified two potential scores in Figure 12:

  • Local Economic Potential which measures the economic potential of the area around a store including changes in local demographics, local house values, and local economic conditions
  • Local Vitality which measures the amount of activity or “life” around a store including miles from a high school, miles from a mall, miles from a business park, local sporting events and local entertainment events

Understanding the local economic potential and local vitality around a store may impact business decisions around staffing, inventory, production, promotions and sponsorships.