The Chief Data Officer (CDO) role is in the hot. If you don’t have one, then you are totally unprepared to reap the bounty of wealth offered up by Data Science and the Internet of Things (IOT). Gartner predicts that 90 percent of large companies will have a CDO role by the end of 2019.
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.
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 Unlike assets that get worn out or outdated, the more you add to existing data sets the stronger, and more insightful even the old data becomes.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. Here you have three dimensions (data, analytics and use cases) that the organization needs to align in order to create economic value.
Identify Targeted Business Initiative
The starting point for the CDO charter is a solid understanding of the organization’s key business initiatives 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. It is a 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
Identify Potential Data Sources
Next, a CDO 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.
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
Estimate Value of Data Sources
We a CDO would want to integrate the financial value of each use case determined with the Relative impact of each data source to calculate a rough order estimate of the value of each data source across all the use cases. 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.
Making it Real: Chief Data Officer Challenges
Chief Data executives have been hired in many companies and given the authority they need to drive data management and data science initiatives forward.
Many firms is now standing at the precipice of turning that “data management commitment” into “the art of the possible” action. Many firms are in the midst of re-writing internal policies and standards needed to embed data management into the fabric of their organizational operations.
Challenge 1: Foundational levels of governance are in the process of being established
- Data owners (i.e. chief data officers, or CDOs) have been hired and tasked with addressing the gaps and challenges associated with data management
- The “Office of Data Management” has become an official control function with defined processes and has been provided with both the executive air cover and authority required to integrate data management into the organizational environment
- Seed funding is in place to get data management programs underway
- The political challenge is to scale these pilot initiatives across the organization
Challenge 2: Changing organizational behavior is difficult
- Data policies and standards are in the midst of being created but are undergoing rigorous scrutiny because the adoption of policy mandates compliance (and many firms would not be in compliance with their adopted policies)
- Data stewardship and accountability are defined and in the process of being integrated into the operational processes of the organization
- Business buy-in is still tentative because many firms are benefiting from seed funding to get their data management programs underway
Challenge 3: Implementation of the data management infrastructure remains a priority
- Critical data elements (CDEs) and critical data attributes have been defined but not fully inventoried or aligned with compounding processes
- Unraveling lineage, mapping complex data flows, separating data attributes from calculation processes and building data inventories are huge tasks that require time and cross functional collaboration
- Adopting unique identifiers and harmonization of content to precise contractual meaning across hundreds of repositories is in process but remains a daunting challenge
The more challenging news stems from the fact that the scope of the task is significant. Large enterprises are complex organizations who are forced to deal with the intricacies of multiple LoBs, the need to unwind technical legacy and the pressures of volatile global business environments.
Most firms are working to untie the “Gordian knot” and dedicating resources to data science models, new platforms like Spark, unraveling lineage, inventorying content, identifying critical data, adopting standards and mapping systems. Harmonization of meaning across thousands of repositories and implementing control processes needed to ensure trust in data resources remain as daunting challenges.