Bill Schmarzo

Determining the Economic Value of Data

[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:

Goodwillis 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:

1)   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.

2)   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.

Data Economic Valuation Methodology

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).

Figure1: 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.

Data Economic Valuation Example

Let’s walk through an example to highlight how this process works.  We’re going to start by using the publicly available information for a bank we will call ACME Bank.  From ACME Bank’s annual report, we can determine that the bank is trying to “increase the number of products per household.”  Their 2010 annual report states the following:

“This year, we crossed a major cross-sell threshold. Our banking households in the western U.S. now have an average of 6.14 products with us. For our retail households in the east, it’s 5.11 products and growing. Across all 39 of our Community Banking states and the District of Columbia, we now average 5.70 products per banking household (5.47 a year ago).”

So based upon the above information, ACME Bank wants to grow the number of products held per household from 5.70 to 6.20.  So let’s make that our targeted business initiative:

Increase number of products per household from 5.70 to 6.20 over next 12 months

Step 1:  Determine Financial Value of Targeted Business Initiative

So what is the potential range of value to the bank in increasing the average number of products held per household from 5.70 to 6.20?  The bank’s annual report didn’t spell out the value of the initiative, so we’re going to perform some rough calculations based upon data that is available in the annual report.

Doing some rough calculations using numbers that we were able to glean out of the annual report, we estimate that each product held per household is worth $31.33 annually (see table below).

So if we could increase the number of products held per household from 5.70 to 6.20, it would be roughly worth $1.1B to the bank per year (see table below).

That seems like an incredible number, so let’s cut it by 90% just to be conservative.  That puts the value of this initiative at $110M.

Step 2:  Identify the Decisions That Drive the Targeted Business Initiative

The next step in the process is to identify the high-level decisions that need to be made to drive the targeted business initiative.  Below are some of the decisions that ACME Bank would need to make to support the “Increase number of products per household” business initiative:

·     Improve cross-sell profiling

·     Improve customer segmentation

·     Improve targeting prioritizing

·     Improve offer effectiveness

·     Improve re-targeting effectiveness

·     Improve close effectiveness

·     Reduce time-to-close

·     Improve customer satisfaction

Step 3:  Quantify the Value of Individual Decisions

Next we need to assign a rough order of financial value to each of the decisions that support the targeted business initiative. We can conduct a brainstorming session with the key business stakeholders (i.e., those business users who either impact or are impacted by the targeted business initiative) to assign a rough order of value to each decision in light of the overall targeted business initiative.

We could then discuss the results and allow the different stakeholders to state their case for the rough order of value.  Then everyone could vote one more time.  The result of the brainstorming process would then end up looking like Table 1.

Table 1:  Economic Value of Supporting Decisions

Some notes about Table 1:

  • ACME Bank’s business initiative of “increase number of products held by household from 5.70 to 6.20” equates to a 9% increase in the number of the bank’s products held by household over the next 12 months.  So I have arbitrarily targeted a 10% increase/improvement for each of the decisions to create a baseline for the conversation.  10% may be too aggressive for a 9 to 12 month timeframe and you may want to amp that down to something around 3% to 5%.
  • A facilitated brainstorming session using techniques such as weights, ranks and votes with the key business stakeholders can yield the best case, worst case and most likely scenario numbers.
  • The aggregation of the dollar-valuations for each decision will not sum to the total value of the targeted business initiative.  There is just too much correlation and interplay between the decisions for that to happen.

I have to admit that the process of assigning a “rough order of value” to each decision is not a hardened process.  However, the process does have the benefit of forcing the conversation between IT and the Business

Step 4:  Assess Value of Data Sources to Each Decision

Next, we need to determine a rough order of value for each data source with respect to how important each data source is to supporting the respective decisions.  We will use Harvey Balls (with a value from 0 to 4) to value each of the data sources. I like using the Harvey Balls as it allows even the causal user to visually ascertain which data sources are likely the most important.  If one wanted more precision, then using a scale from 0 to 10, or 0 to 100, might be more advantageous.  However for this exercise, we’ll just stick with the Harvey Balls (see Figure 2).

Figure 2:  Rough Order Data Source Valuation vis-a-vis Key Business Decisions

Key stakeholder interviews (to get the initial value approximations) and then a facilitated workshop driving collaboration across key business stakeholders can yield the Harvey Ball rankings (0 to 4) that appear in Figure 2.

Next, we can create a formula that calculates relative economic value of each data source vis-à-vis the decisions.  One can make the formula as sophisticated as you want, as long as the business stakeholders can clearly understand the rationale for the formula. Below is the formula that I used:



·     D5 is the rank (0 to 4) of the data source (row 5) vis-à-vis decision (column D)

·     D$11 is the sum of the data source rankings for Decision in column D

·     D$4 is the value of Decision in column D

It is a very simple formula.  But if explaining the formula loses the interest of the business leaders, then they will have little confidence in the results of this exercise.  Consequently, err on the side of keeping the formula simple versus making it overly complicated.

Step 5:  Aggregate Economic Value for Each Data Source

Finally, the economic value of each data source can then be summed across the decisions to get a rough order assessment on just how valuable each data source could be (see Figure 3).

Figure 3: Determining Data Source Economic Value


Organizations have an opportunity to use data to improve their decision-making. While that’s something that most companies have been doing with data for the past couple of decades, there is the opportunity to take decision-making to the next level of granularity and actionability.  Access to more-timely, more complete and more accurate data can enable organizations to tease out more significant, material and actionable insights about their customers, products and operations in order to make “better” decisions.

The advantage of this data economic valuation process includes:

  • By starting with a key business initiative, you have established the financial basis for “prudent value” that we can use as the basis for ascertaining the economic value of the supporting data sources
  • You are forced through a process of identifying the different decisions necessary to support the targeted business initiative, and to associate a rough order magnitude of value to improving the effectiveness or outcomes from those decisions
  • Forces the business users to contemplate and rank the perceived value of each data source vis-à-vis the decision that they are trying to optimize
  • Finally, the valuation formula puts you in a position to attach reasonable financial value to the different data sources that can ultimately prioritize data acquisition, cleansing, transformation and enrichment activities

Ideally, one would want to take this exercise to the next level and add a process for determining the cost of acquiring each of the data sources.  The cost would need to consider not only the cost to acquire the data, but also the cost to clean it up, align it, transform it and enrich it.  Maybe that’s a topic for my Big Data MBA class to explore.

Not all data is created equal, that is, some data is more important than other data in supporting the decisions that support the organization’s key business initiative.  Consequently it’s important to have a rough estimate as to what data is most important in order to guide your “data as an asset” management strategy.

By Bill Schmarzo

Bill Schmarzo

CTO, IoT and Analytics at Hitachi Vantara (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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