The Economic Value of Data Theorems

One of the eye-opening revelations for me from the University of San Francisco Economic Value of Data research study was the differences in an accounting versus an economic asset valuation approach:

• Accounting uses a "Value in Exchange" methodology for determining asset valuation based upon the acquisition cost of an asset; that is, the value of an asset is determined by what someone is willing to pay you for that asset or what you paid to acquire that asset.
• Economics uses a "Value in Use" methodology for determining asset valuation; that is, the value of the asset is determined by how much value you can create from using that asset.

When we change our frame from an accounting to an economics perspective, understanding how to determine the value of one's data and analytic assets almost become self-evident. Once we stop trying to "force fit" data into our balance sheet, then our minds...

EvD Theorem #1: Data, By Itself, Provides Little Value

It isn't the data itself that's valuable; it's the trends, patterns, and relationships (insights) gleaned from the data about your customers, products, and operations that are valuable.

In Chapter 1, The CEO Mandate: Become Value-driven, Not Data-driven, I introduced Phase 4 of the Big Data Business Model Maturity Index—the "Insights Monetization" phase and discussed how organizations should not focus on trying to monetize their data by selling it.

That's an accounting mentality. Instead, organizations should adopt an economics (value in use) mentality to identify opportunities to use their data to create new sources of customer, product, and operational value (see Figure 5.1).

Figure 5.1: Insights Monetization Phase of Big Data Business Model Maturity Index

Many organizations are associating data monetization with the selling of their data. Selling data is a business...

EvD Theorem #2: Predictions, Not Data, Drive Value

It is the quantification of trends, patterns, and relationships that drive predictions about what is likely to happen.

It is the quantification of trends, patterns, and relationships around customers, products, services, operations, and markets that drive operational, management, and strategic predictions. And it is the value of these predictions (in support of the top-priority business and operational use cases) that ultimately determines the economic value of your data. And the best way to codify and ultimately monetize those trends, patterns, and relationships is through the use of Analytic Profiles.

As I discussed in Chapter 3, A Review of Basic Economic Concepts, Analytic Profiles or Digital Twins, provide a mechanism for capturing the metrics, predictive indicators, segments, scores, and business rules that codify the behaviors, preferences, propensities, inclinations, tendencies, interests, associations, and affiliations...

EvD Theorem #3: Predictions Drive Value Through Use Cases

Predictions drive monetization opportunities through improved (optimized) business and operational use cases.

To fully exploit the economic value of data, the research found that a use case by use case approach enables organizations to attribute the economic value of their data based upon the reuse of that data across multiple use cases. That is, the use cases provide the linkage between the business stakeholders—who understand the financial and business value of the use cases—and the data science team—whose expertise is in identifying and codifying the variables and metrics that are better predictors of performance (see Figure 5.3).

Figure 5.3: Use Cases Provide the Technology-to-Business Linkage

As an example, in Figure 5.4, the same dataset (Customer Point of Sales) can be used by Sales to increase promotional effectiveness by 2.5%, and then that same data set at a marginal cost of...

EvD Theorem #4: The Data Economic Multiplier Effect is the Real Game-changer

The ability to reuse the same data sets across multiple use cases at near-zero marginal cost is the real economic game-changer.

The use case-by-use case approach highlighted in EvD Theorem #3 is the key to exploiting the unique economic characteristics of data—an asset that never depletes, never wears out, and can be used across an unlimited number of use cases at near-zero marginal cost (yeah, there's the economic multiplier effect again). This use case by use case approach powers the economics "value in use" methodology for determining the value of a data set based upon the financial value of each use case (see Figure 5.5).

Figure 5.5: Ascertaining Data Value Use Case by Use Case

In Figure 5.5, Use Case #1 (Improve Vendor Product Quality) is worth $60M annually and requires 3 data sets (A, B, and C) to optimize that use case. Using a straight-line financial allocation...

EvD Theorem #5: Predictions Enable "Do More with Less"

Trying to optimize across a diverse set of objectives can yield more granular, higher fidelity business and operational outcomes that enable "doing more with less."

As we discussed in Chapter 3, A Review of Basic Economic Concepts, the challenge with the Economic Value Curve is the Law of Diminishing Returns. The Law of Diminishing Returns is a measure of the decrease in the marginal (incremental) output of a production process as the amount of a single factor of production is incrementally increased, while the amounts of all other factors of production stay constant. We can transform the organization's Economic Value Curve by applying fine-grained predictive and prescriptive analytics in order to focus investments on those aspects of the independent variables that have the highest impact on the business outcomes (dependent variables).

This enables the organization to "do more with less"...

The Economic Value of Data Calculation

We will end this chapter with the mathematical formula for the Economic Value of Data calculation. If you can't put the formula into math, well, it'll be hard to lay claim to that Nobel Prize in Economics that I so badly desire!

The Economic Value of a Dataset (EvD) equals the sum of the Attributed Financial Value (FV) of a specific Use Case (Use_case_FV) that each dataset provides to that specific Use Case:

where m is the number of use cases, n_j is the number of data sets per use case j, and a₀ is a bias.

Using Figure 5.5 (repeated below as Figure 5.8 for simplicity), you apply this formula as such:

• The first iteration covers the Vendor Quality use case [Use_case_FV₁], where [Use_case_FV₁] equals $60M. [Count Data_Set] = 3 since there are 3 data sources (data sources A, B, and C) that support the analytics for [Use_case_FV₁]. Each of the 3 data sources is attributed (1 ÷ [Count Data_Set]) of the [Use_case_FV...

Summary

I fully expect the number of theorems to grow as the Economic Value of Data concepts mature, especially as organizations expand their value creation expectations for data and analytic assets to fuel the organization's digital transformation. For example, I can see another theorem on "variable predictability" and its importance in attributing financial value to the appropriate data sources. I guess that one will have to wait until my next research project!

We will continue to explore, learn, and share as we seek to perfect the Economic Value of Data methodology that can guide organizations along their digital transformation journey through optimizing their data, analytics, and technology investments.

Further Reading

1. Data Curation: https://en.wikipedia.org/wiki/Data_curation

Homework

1. EvD Theorem #1: Data, By Itself, Provides Little Value. It isn't the data itself that's valuable; it's the trends, patterns, and relationships (insights) gleaned from the data about your customers, products, and operations that are valuable.

   

   EvD Theorem #2: Predictions, Not Data, Drive Value. It is the quantification of trends, patterns, and relationships that drive predictions about what is likely to happen.

   

   EvD Theorem #3: Predictions Drive Value Through Use Cases. Predictions drive monetization opportunities through improved (optimized) business and operational use cases.

   

   EvD Theorem #4: The Data Multiplier Effect is the Real Game-changer. The ability to reuse the same data sets across multiple use cases is the real economic game-changer.

   

   EvD Theorem #5: Predictions Enable "Do More with Less." Trying to optimize across a diverse set of objectives can yield more granular, higher...