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This post introduces a data economic valuation process that uses an organization’s key business initiatives as this basis for establishing prudent value.
By William Schmarzo, EMC.
[Note: I have been trying to write this blog for several years. But instead of trying to perfect the concept, perhaps the best approach is to simply put the idea out there and let it percolate amongst my readers. My University of San Francisco Big Data MBA students will get a chance to test and refine the approach outlined in this blog.]
Data is an unusual currency. Most currencies exhibit a one-to-one transactional relationship. For example, the quantifiable value of a dollar is considered to be finite – it can only be used to buy one item or service at a time, or a person can only do one paid job at a time. But measuring the value of data is not constrained by those transactional limitations. In fact, data currency exhibits a network effect, where data can be used at the same time across multiple use cases thereby increasing its value to the organization. This makes data a powerful currency in which to invest.
Nonetheless, we struggle to assign economic value to an intangible asset like data. Being able to attach economic value to data is key if we want organizations to truly manage data as a corporate asset. However, accounting already has a mechanism for quantifying the value of an intangible asset like data. It’s called goodwill. In the accounting vernacular:
Goodwill is an accounting concept [attaching] value [to] an entity over and above the value of its assets. The term was originally used in accounting to express the intangible but quantifiable “prudent value” of an ongoing business beyond its assets.
From this definition of goodwill, it seems that being able to express the intangible but quantifiable “prudent value” of data should be possible. So the challenge is developing a formula for establishing “prudent value.”
In this blog, and soon-to-be classroom exercise, we will introduce a data economic valuation process that uses an organization’s key business initiatives as this basis for establishing prudent value. We will outline an approach to quantify the value of the data by considering its relevance to the business decisions required to support the organization’s key business initiative(s). And while this process will not make the data economic valuation calculation exact, it will provide a general basis that can be used to help make thoughtful data investment decisions.
Key Business Initiatives as the Basis for Prudent Value
Organizations launch business initiatives to support their overall business strategy. These business initiatives coalesce an organization around a critical few projects that are designed to deliver measureable financial value. A business initiative is a “cross-functional project, championed by executive leadership, to deliver measurable financial or business value to the organization, typically within a 9 to 12 month timeframe.” These business initiatives are often called out in annual reports and quarterly analyst reviews.
Some example business initiatives include:
- Increase the number of products held by banking household from 6.8 to 8.0 within the next 12 months
- Reduce customer churn of our most valuable customers by 20% over the next 12 months.
- Increase private label sales from 20% to 24% of total retail sales over the next 9 months.
- Increase the overall member satisfaction index by 5 basis points over the next 9 months
Starting the data economic valuation process by focusing on a key business initiative provides the following benefits:
- The business initiative typically has some financial value attached to it. For example, increasing the number of products held by banking household from 6.8 to 8.0 has measurable financial value (e.g., revenue, margins, profits) to the organization. And while the financial value may not be exact, most companies can determine a range of financial value against which they can measure the success of that business initiative.
- It enables us to frame the data economic valuation process around the business decisions that need to be made to drive the targeted business initiative. It helps us quantify the ways in which we “might” utilize data, and what impact that data might have on the success of the targeted business initiative.
We start the data economic valuation process by focusing on an organization’s key business initiative. Once we have identified a key business initiative upon which to focus, then we will triage that business initiative to identify 1) the business decisions that need to be made to support the business initiative, and 2) the data that might be useful in enabling “better” or improved decisions (see Figure 1).
Figure 1: Economic Data Valuation Decomposition Process
The data economic valuation will cover the following process:
- Step 1: Determine Financial Value of the Targeted Business Initiative. The first step should identify the targeted business initiative, and then capture the key financial metrics in order to create a rough estimate of the financial impact of the targeted business initiative.
- Step 2: Identify Business Decisions that Support Targeted Business Initiative. The second step combines Business stakeholder interviews with a facilitated workshop to identify / brainstorm the decisions that the stakeholders need to make in support of the targeted business initiative.
- Step 3: Quantify Value Of Individual Decisions. The next step determines a rough order of magnitude financial value for each of the decisions. Note: the financial value is likely to be a range, but we will pick the most likely value (mean, mode, median) in order to keep the exercise manageable.
- Step 4: Assess Value of Each Data Source to Each Decision. Next, we need to determine a rough order of magnitude value for each data source with respect to how important each data source is to supporting the respective decisions.
- Step 5: Aggregate Economical Value for Each Data Source. The final step aggregates the financial value of each data sources across all the different business decisions to come up with a rough order of magnitude value for each data source. While this may not be a hard and fast number, it will provide the basis against which to make data acquisition, enhancement and enrichment decisions.
Note: this process will not deal with exactness, but instead be preferred to deal with ranges of values and confidence levels.
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