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

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Map Scores to Recommendations and Decisions

 
In order to ensure that we are building the most relevant or highest priority scores, we will map the potential scores to the recommendations and decisions necessary that support the prioritized use cases (see Figure 14).

The worksheet in Figure 14 ensures that we are building the most relevant scores, and understand upfront where and how the analytic scores will be used to support the decisions being made by the business stakeholders in support of the prioritized use case.

So at this point in the CDO Toolkit process, we have identified, prioritized and valued the data and the analytics necessary to support the organization’s key business use cases. Now we need to introduce the concept of Analytics Profiles as the structures (key-value stores) that standardize the collection and re-application of analytics across multiple use cases.

Create Analytic Profiles

 
Next is the creation of the Analytic Profiles. In Analytic Profiles are created by applying data science to uncover behaviors, propensities, tendencies, affinities, usage trends and patterns, interests, passions, affiliations and associations at the level of the individual entity (i.e., customers, students, patients, employees, cars, wind turbines, washing machines, devices, machines).

The scores (and propensities, clusters and predictive indicators) that comprise an Analytic Profile are built one use case at a time (similar to the way that data is loaded in the Data Lake one use case at a time). For Chipotle, this might mean:

Figure 15 shows an example of a Chipotle Customer Analytic Profile that is being built out one use case at a time.

Notice in Figure 15 how Use Case #4 upgrades the Customer Loyalty Index from version 1.0 to version 2.0. This may have been the result of new data sources and/or improved data enrichment techniques and/or improved analytic modeling. Either way, the improved score has positive impact on all other use cases that use that score (such as Use Case #2 in Figure 15).

Eventually, our Analytic Profiles leverage new sources of data and analytic techniques to create a wide variety of scores and analytic results that can be leveraged across more and more business initiatives and use cases (see Figure 16).

CDO Toolkit Checkpoint: Where Are We Now?

 
We extended the CDO Toolkit process to facilitate the capture, sharing and monetizing the organization’s analytics. At this point, we should be able to answer the following questions:

• Have you mapped the organizations Business Entities (around which we will build out Analytic Profiles) to your use cases?
• Have you identified the decisions (and supporting recommendations) that we need to make in support of the identified use cases?
• Have you identified the analytic “scores” that we could create to support the decisions and recommendations?
• Have you applied the “By Analysis” technique to brainstorm those potential variables and metrics that might be better predictors of performance?
• Finally, have you created and started to populate the Analytic Profiles for each individual Business Entity with scores driven by the prioritization of use cases?

CDO Toolkit Summary

 
Finally, all of this data and analytics intellectual capital gets pulled together in the data lake. The raw data, the refined data and the resulting analytics that support the different use cases can all be stored, managed and shared from the data lake. The data lake becomes the organization’s “collaborative value creation” platform that facilitates the sharing of data and analytics across the organization and across multiple business initiatives and use cases (see Figure 17).

The CDO Toolkit concept is still evolving. My University of San Francisco School of Management students will get a chance to test and refine these methods first hand this upcoming spring semester. I’ve also asked a couple of my CDO friends to review the material in this blog. I’ll be sure to share the results.

Watch this space!

[1] “Orphaned Analytics” are analytics that are developed for one-off business problems. While these orphaned analytics provide a one-time financial value, they do not get operationalized in a way that allows the organization to reuse and monetize the analytics across multiple use cases.

Original. Reposted with permission.

Related:

• Big Data Dilemma: Save Me Money Versus Make Me Money
• Determining the Economic Value of Data
• Tips for Data Scientists: Think Like a Business Executive