A CDO Playbook for Driving Marketing Impact


The Chief Data Officer is a relatively recent addition to the classic C-suite roles. C-level’s in  larger organizations are focused on enterprise-level strategy and asset management. The presence of the CDO among that cohort represents the growing realization that “Data” has become a true strategic asset that requires highly specialized management and dedicated resources.

Central to the CDO value proposition are the specialized skills and resources to administer data— and effectively manage data sources, transformation, usage, storage, etc. In addition, the CDO is responsible for activating the data they manage on behalf of the various functional areas of the organization. This activation work comes in the form of easier access to higher quality data as input to (broadly) improving decision making, leading to revenue generation and lower costs. By making the data work as hard as possible on the most strategic opportunities, the CDO can maximize and optimize the business impact of the organizations’ data assets.

Why Prioritize Marketing?

When the CDO is focused on activating data to drive revenue, the single best path to that goal  leads directly to the Marketing function and a natural partnership with the Chief Marketing Officer (CMO).

The CMO and the marketing organization are laser focused on generating revenue, and often  are spending huge budgets to do so. The CMO knows there is an opportunity to increase revenue through better decision making, and better decision making is dependent on better data (Columbus, L; Venturebeat.com). The overall business rationale for prioritizing the CMO and the marketing organization is compelling when large volumes of data, heavy analytics and complex data handling are foundational to revenue generation. The CDO has expertise and resources to manage complex data, which can serve as an accelerator to the CMO’s financial objectives.

Thus, the CDO and CMO have joint interests in optimizing data and decision making to drive revenue. This sets up a shared objectives scenario, ideally a partnership, where the CMO gains additional resources to better operate their complex data environment, and the CDO is more directly aligned with revenue generation.

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An observation specifically for CMO’s and marketing leaders:

The level of skills and resources that a marketing organization can direct against “data” is obviously limited. There has been and will continue to be huge growth in volume, sources, and complexity of marketing data. Customer data in particular has been an ongoing management challenge due to its various structures and rate of accuracy decay. Plus, there are sophisticated marketing analytics applications, such as real-time predictive engines (and more) that need to ingest high-quality data to yield important customer-level decision making. This scenario resembles the relationship between the CMO and the CTO (or CIO), in terms of providing services to operate MarTech systems such as CRM and Email platforms (EY.com; Baylis, J; Moore, B). If it is not already happening, these are now the business circumstances to engage and partner with the CDO to optimizing marketing data, and in turn directly improve marketing decision making.

The CMO and Their Operating Environment

It is very helpful to have a deeper understanding of the CMO’s operating environment in order to understand how the CDO and their data value proposition benefits marketing.

The primary responsibility of the CMO is “demand generation.” That means creating a pool of potential customers, with needs that can be fulfilled by the organizations’ products or services, and who have the ability to actually make a purchase. Because it takes time to build the pool of prospects with these qualifications, marketing is usually focused on future sales revenue months or quarters out. There are exceptions by product and industry but marketing is usually not tasked with very short-term revenue.

Building the demand pool means marketing must operate a rather complex infrastructure designed to identify many prospects and convert them to the best customers. Customer data as a broad data type is a particular challenge, yet fundamental part of revenue generation. Thus, marketing has very complex customer data requirements and is very sensitive to the quality and timing of numerous data streams that are ingested into their customer demand infrastructure. While the CMO and the marketing organization manage many types of data, the focus here is on customer data which is directly associated with revenue, thus one of the most valuable types of marketing data. Illustrated here are a few of the current state sources and types of data that marketing requires to build and manage pools of prospects and customers.

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To get a sense of complexity and volume it is worth noting that customer data is sourced from a number of different internal systems/sources, as well as many external 3rd party sources, all of which have different data structures, time frames and processing requirements. Depending on the type of organization, there can be millions (or more) customers. Also, paid digital media campaign performance data volumes can easily run into the millions of rows daily from multiple publishers. This data is ingested by marketing and used to plan and optimize advertising spend.

In addition to data management, marketing is driving the use of leading-edge analytics techniques (Vehkatesan, Farris, Wilcox; pp 101-109). These capabilities are part of the CMO’s tool box and used in a variety of tasks including:

  • predictive modeling of prospects and their propensity to convert to customers
  • customer lifetime value (LTV) and net present value (NPV)
  • media mix modeling,
  • advertising spend and channel allocation

These tasks are springboards into even more sophisticated analytics applications, involving true big data and machine learning. Even with this growing capability, the CMO must engage on important data management initiatives in order to feed the best quality of data to the decision platforms that are a part of everyday business.

How the CDO Can Get Involved

To build a pipeline of prospects and future customers efficiently and effectively, marketing requires leading-edge analytics which in turn is dependent upon sophisticated and large-scale data management, ETL, complex structures and integration across internal and external sources. This opens the door for the CDO to provide data expertise and resources that are likely not core competencies within the marketing organization.

Revealing further opportunities for the CDO to add value, recent survey data from over 600 senior corporate executives found that CMO’s are not heavily involved in setting customer data strategy or managing customer data (HBR.org; EY/Financial Times; Baylis, J).

The percentage of CMO’s responsible for:  
Maintaining compliance with data privacy and protection regulations 5%
Getting, storing and protecting customer data 12%
Quality of customer data 17%
Approving investment in digital engagement technologies 6%

While the other responsibilities are certainly important, only about 1 in 6 CMO’s (17%) are perceived to be responsible for customer data quality. Obviously this is an area that can use more attention, and specialized skills in DA to drive data value.

Taking a consultative approach, seeking to understand the questions the CMO needs answered and why they are important, is usually the best way to get involved. In addition, an assessment of the current state of one (or a few) data sources, following standard quality processes will almost certainly yield examples of issues with data that is ingested by decision processes. These tend to be less controversial, as they are less subjective and possibly are already known to the marketing teams. These steps will set up a “proposal” phase where documentation of the current state, identified improvements that can be made in processing, transformation, timing etc., and the expected mitigation/resolution outcomes can be measured.

Examples of Potential Areas of Exploration

It is very likely the CMO and the broader marketing organization already know of specific issues with data, but have not been able to prioritize them or have been unable address them successfully on their own. The consultative approach above is helpful in identifying these types of known problems and bringing CDO-level skills and resources to bear.

A parallel approach (or a next phase) is to choose a goal and objective in an area where there is high potential for improvement. For example:

Scenario 1

  • Goal: Improve customer data quality to directly impact marketing decision making
  • Objective: Assure the level of accuracy of customer profiles (within the CRM) is at least 98%
  • Rationale: Customer profiles are ingested by the customer valuation analytics process which is used to forecast repeat purchases and potential recurring revenue for the next fiscal year. Duplicate customers, missing customers, inaccurate contact information, incomplete purchase transaction history, etc. results in a higher level of uncertainty in the forecast for recurring revenue. Resolving these data issues will resolve some of the uncertainty and lower the risk of faulty decisions.

Scenario 2

  • Goal: Accelerate the timing of purchase data collection and availability
  • Objective: Confirm add-to-cart and purchases, and update customer profiles within 3 min from the time of purchase (24/7).
  • Rationale: Delivery of accurate near real-time purchase data is a growing requirement.  Recency and type of purchase are highly correlated factors for internal modeling of Next-Best-Action to incentivize follow-on purchases as soon as possible.

Scenario 3

  • Goal: Accurately match and integrate 3rd Party data with internal prospective customer data
  • Objective: Ensure that licensed 3P demographic data is matched correctly for at least 99% of all attempts     
  • Rationale: Prospective customers must meet an appropriate age and income level to be eligible for certain services. When the prospect is qualified for the service, an online advertising campaign is instantly triggered to reach and engage that person.


Today’s CDO has the opportunity to drive and improve the internal value of data and the visibility of DA specialization. Much of the value can be captured by applying skills and resources against improving data that is directly related to the organizations ability to generate revenue. The CMO and the marketing organization are at the intersection of revenue generation and complex data management requirements. By seeking to understand Marketing requirements and resolve or mitigate customer data issues that affect decision making, the CDO role will be closely associated with enablement of revenue generation. In so doing, the CDO will be aligned and actively engaged with the highest priority of the entire C-suite.


“Is Poor Data Quality Undermining Your Marketing AI?”; Columbus, L; VentureBeat.com; 2021

“Why the CIO and CMO Must Collaborate to Drive Business Transformation”; Baylis, J; Moore, B; EY.com; 2021

“Marketing Analytics: Essential Tools for Data Driven Decisions”; Venkatesan, R; Farris, P; Wilcox, R; Darden Business Publishing, 2021

“Who’s Driving Corporate Data Strategy?”; Janet Baylis; Harvard Business Review/HBR.org; EY/Financial Times Survey; 2022

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Jeff Godish

Jeff Godish

Jeff Godish is currently the Managing Director at DeltaStrata LLC, a strategic marketing acceleration platform designing new solutions for demand generation and revenue capture. Previously, Jeff held senior/executive roles at several F500 companies including GlaxoSmithKline plc., Kraft Heinz Company and Mylan Inc., where he led digital marketing and media initiatives. He also led strategy and solution design at Merkle/dentsu group, Inc., a global advertising, customer experience, digital marketing, and analytics network of companies. In addition, Jeff is an active Adjunct Faculty member at the Katz Graduate School of Business at the University of Pittsburgh, specializing in digital and social media strategy and marketing analytics. He also holds MBA and MS Management Information Systems degrees from Duquesne University, as well as a BS in Computer Science. Jeff can be reached at jeff@deltastrata.com.

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