Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part I
The Chief Data Officer (CDO) role is red hot!! If you don’t have one, then you are totally uncool and unprepared to reap the bounty of wealth offered up by Big Data and the Internet of Things (IOT). Gartner predicts that 90 percent of large companies will have a CDO role by the end of 2019 . Hire a CDO, and everything will be good. Or, will it?
Unfortunately, it’s hard to distinguish the charter of the CDO from that of the Chief Information Officer (CIO) or the Chief Technology Officer (CTO). Fortunately, I have a simple fix to this problem; ensure that the CDO charter is to:
Acquire, enrich and monetize the organization’s data (and associated analytics)
Key to the CDO’s success is the ability to determine the economic value of the organization’s data and the resulting analytics, and to use that determination to prioritize and focus data and analytics investments.
So the time is right to introduce the Chief Data Officer (CDO) Toolkit. The CDO Toolkit integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources, and use that information to prioritize the organization’s data and analytics investments. This blog will introduce the CDO Toolkit concept with some examples. Future blogs (or possibly an eBook) will drill provide more details including supporting worksheets.
Determining the Economic Value of Data
Before we launch into the CDO Toolkit discussion, there are some aspects of data (and analytics) that form the foundation of the CDO’s charter.
Most currencies, like money or human currency, have a transactional limitation; that is, a dollar can only be used to buy one item or service at a time. Likewise, a person can only do one job at a time.
But data and analytics are not constrained by these transactional limitations. Data as a currency exhibits a network effect, where the same data can be used simultaneously across multiple business processes or business initiatives thereby increasing its overall economic value to the organization. The same network effect can be said of analytics as well, for what is analytics but “curated” data. This makes data and analytics powerful currencies in which to invest.
Data and analytics, as corporate assets or intellectual capital, exhibit a behavior never seen before in the business world. Most business assets operate under the “rule of depreciation”, where the value of an asset is reduced with the passage of time and/or usage. But data and analytic digital assets operate under the “rule of appreciation” where these digital assets become more valuable as they are 1) used simultaneously across multiple business processes and business initiatives and 2) the more that you use them, the accurate they become. Unlike assets that get worn out or outdated, the more you add to existing data sets the stronger, and more insightful even the old data becomes. Data that would otherwise become throwaway data as standalone becomes more valuable when integrated with other sources of data. This economic phenomenon is a game-changer for organizations looking to drive digital business transformation.
Unfortunately, organizations lack a coordination point around which to align the data and analytic currencies. Fortunately that’s the role of use cases, which we define as clusters of decisions around a common subject area in support of an organization’s key business processes or key business initiatives. Use cases provide an anchor point around which the organization can align its data and analytics currencies, and address what I call the “Rubik’s Cube Challenge,” where you have three dimensions (data, analytics and use cases) that the organization needs to align in order to create economic value (see Figure 1).
The objective of this blog series is to introduce the CDO Toolkit as a framework and associated processes to help the Chief Data Officer to address the questions raised in Figure 1; to provide a toolkit to exploit the unique behaviors of data and analytics as currency to create new sources of organizational intellectual capital and value creation.
Introducing the CDO Toolkit
The CDO Toolkit is designed to help the Chief Data Officer to:
- Monetize the organization’s data by determining its potential economic value
- Identify use cases where the data can drive business outcomes
- Develop a framework for the capture, refinement and sharing of the organization’s data (internal, external, partner, public, syndicated, etc.)
- Develop a methodology for the capture, refinement and sharing of the resulting analytics
Identify Targeted Business Initiative
The starting point for the CDO charter is a solid understanding of the organization’s key business initiatives . For this blog, we will focus on Chipotle’s 2012 “Increase Same Store Sales” business initiative found in Chipotle’s 2012 Annual Report (see Figure 2).
Estimate Financial Value of Business Initiative
Once we have identified the targeted business initiative, we next need to calculate a rough order estimate of the financial value of that business initiative. Using data readily available from the 2012 Chipotle Annual Report, we can determine that the estimated value of the Chipotle “Increase Same Store Sales by 7%” business initiative is roughly $191M annually (see Figure 3).
While this is a fairly rudimentary calculation, it is a sufficient starting point in driving conversations between the CDO and the key business stakeholders in order to gain consensus on the estimated financial value (or range of financial value) of the targeted business initiative.
Identify Use Cases That Support Target Business Initiative
Next, we need to identify the Use Cases  (or clusters of decisions) that support the targeted business initiative. We interview the key business stakeholders to identify the key decisions that they need to make in support of the targeted Business Initiative, and then we group those decisions into common subject areas or use cases (see Figure 4).
Listed below are the use cases that came out of the interviews that support Chipotle’s “increase same store sales” business initiative:
- Increase store traffic via local events marketing
- Increase store traffic via customer loyalty program
- Increase shopping bag revenue
- Increase corporate catering
- Increase non-corporate catering
- Improve new product introduction effectiveness
- Improve promotional effectiveness
This is also the point in the process where the Business and IT leaders need to prioritize the use cases based upon relative financial value and implementation feasibility over the next 9 to 12 months. Figure 5 shows a 2×2 Matrix that is a marvelous tool for driving group consensus regarding prioritization when used in a facilitated workshop situation. Check out the blog “Prioritization Matrix: Aligning Business and IT On The Big Data Journey” for recommendations on how to use the Prioritization Matrix.
This organizational process is critical for the adoption of the analytics. It is important that all key business stakeholders have a voice in determining the relative value and implementation feasibility of each use case. This may be the single most important step in the CDO Toolkit. This step ensures that all parties are in agreement about where and how to start prior to the organization investing significant money and time building out an analytics capability that the business stakeholders may not use or trust.
Estimate Financial Value of Use Cases
We now need to estimate the financial value for each use case. We can employ a simple polling technique to get an estimate on the financial value of each use case from each business stakeholder. Figure 6 captures the polling results from the different business stakeholders for the Chipotle “increase same store sales” business initiative.
The spreadsheet then averages all the estimates to come up with an estimate of the financial value of each use case (highlighted by the red box in Figure 6).
Identify Potential Data Sources
Next, we want to conduct business stakeholder interviews and facilitated brainstorming sessions to identify those data sources that might be useful in support of our target business initiative (see figure 7).
During this part of the process, it might be useful to review the definition of data science:
Data science is about identifying those variables and metrics that might be better predictors of performance
During this data sources exercise, it is important to embrace the power of the word “might” and capture any and all ideas with respect to what data sources might be useful. The data science team will actually determine which data sources are valuable and which ones are not, but in this part of the process all data sources are worthy of consideration!
Estimate Financial Value of the Data
Next we want to map the data sources to the use cases, and determine the relative importance of each data source to each individual use case. Business Stakeholder interviews and facilitated brainstorming sessions can be very useful in identifying the different data sources and creating a relative weighting on the potential value of each data source to each use cases. See Figure 8 shows an assessment of the relative value of each data source with respect to each use case.
When assessing the relative value of each data source, I like to use a scale of 0 to 4 (because I can then turn the results into really cool looking Harvey Balls). However a wider scale (0 to 10, or 0 to 100) might provide more granularity on the data source value estimates.
Estimate Value of Data Sources
We now want to integrate the financial value of each use case determined in Figure 6 with the Relative impact of each data source from Figure 7 to calculate a rough order estimate of the value of each data source across all the use cases (see Figure 9).
The financial calculations are purposely rudimentary. You can make the formula as sophisticated as you want, as long as the business stakeholders can clearly understand the rationale for the formula. 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.
CDO Toolkit Checkpoint: Where Are We Now?
At this point in the CDO Toolkit process, we should be able to answer the following questions:
- Have you identified and estimated the financial value your targeted business initiative?
- Have you brainstormed with the business stakeholders the decisions that they need to make in support of the targeted business initiative?
- Have you clustered the decisions into common subject areas (use cases)?
- Have you created a rough order estimate on the financial value of each of those use cases?
- Have you brainstormed and mapped the potential data sources to each use case?
- Have you created a “rough order estimate” of the value of the data sources for each use case?
- Have you aggregated the value of the data across all the use cases to come up with an estimated value of each data source?
We have accomplished quite a bit in support of the CDO’s data monetization role. If you have gotten this far, then your CDO is well prepared to determine the economic value of the organization’s data, and to use that determination to prioritize and focus the organization’s financial and people investments in data (e.g., data acquisition, data cleansing, data alignment, data enrichment, metadata management, security, data governance). But there is more to do. The CDO must now use these insights to drive digital business transformation.
Original Article: Reposted with permission
 Business Initiative is a cross-functional plan or program that is typically 9 to 12 months in duration, with well-defined financial or business metrics, that supports the organization’s business objectives. For The Disney Company, it might be to “leverage the MagicBand to increase guest satisfaction by 15%” or “leverage the MagicBand to increase visits at Class B attractions by 10%.”
 Use Case is a cluster of actions or decisions, typically defining the interactions between a role (actor) and a system (process) to achieve a specified goal.