6 Vital Workstreams in a CDAO’s Playbook and How to Bring Them Together

With the recent excitement around AI (the more mature stage of analytics), specifically Generative AI, how should CDAOs respond to questions and pressure from CEOs and CFOs?

The Data and Analytics space has been rapidly evolving, so how do you keep up?

My recommendation: Master the basics. Go back to the fundamentals. There's no point in chasing every new hype. After meeting many CDAOs in the past decade, I have found out that our challenges are similar and we have our own playbook at work, with a similar framework. Below is a quick summary of my playbook, as a practitioner's example.

If you mislabel the role as only “Chief Data Officer” (CDO), you may have unintentionally diminished the role to be more like a taskmaster and a technical leader, to be narrowly focused on data as a resource or raw material, and to be a part of the technology function.

This is why I highly recommend we use the “Chief Data and Analytics Officer” (CDAO) title, so we can accurately define the role as a strategic leadership role with a focus on alliance building and organizational transformation.

There are six workstreams in the CDAO framework:

1. Strategy

2. Value delivery

3. Data

4. Technology and techniques

5. Processes

6. People

1. Strategy

In the context of a specific industry and organization — We formulate our mid-term and long-term strategic plans and priorities when facing various forces in the marketplace. One important driver is to develop data and analytics AI capabilities. CDAOs need to translate these strategic priorities into short-term and mid-term objectives and metrics.

We ask ourselves the following questions:

• Where are we currently on data and analytics capabilities?

• How do we describe the future state we want to be in 3-5 years?

• What are the gaps?

• What are the tasks and milestones in our action plan to narrow the gaps?

• What resources do we need to acquire or develop (leadership, technical talent, budget, technological infrastructure and tools, and data resources)?

• What is the appropriate level of investment budget? Treating this as an innovation initiative, what is the timeframe for us to measure the outcome. (similar to the exit strategy for a startup)?

• What is the best organizational structure and how do we plan to continue to adjust it annually?

• What governance structure will we use (such as steering committee or advisory body)? How do we review progress and guide the process?

2. Value delivery

This workstream answers the question from the top stakeholders and influencers: “What have you done recently for me?” and “What is in it for me?”

While building an alliance with CFOs and COOs, we need to translate strategic priorities into specific business use cases, scope out the projects with needed resources and skill sets, and deliver the expected outcome.

A portfolio of several critical projects for each quarter would help CDAOs focus on action and outcome. Biweekly or monthly status updates and feedback collection should be scheduled. Quarterly review with the executive leadership team under the CEO (and potentially with the board of directors) helps promote strategic importance and visibility.

3. Data

This workstream is owned by a data resource management leader reporting to a CDAO. The focus is on data quality, definition, catalog, and process standards, making sure the data resources are relevant, accessible, and trusted. An enterprise-wide data fluency program (also known as data literacy, data governance) is also included in this workstream.

Data resource monetization and commercialization are the end goals. The bottleneck for value delivery is effectiveness and user-friendly / use-case-ready data management. The recent progress and acceleration in AI (particularly in Generative AI) is a great sign for more breakthroughs in the next few years.

This workstream has been covered in detail by our professional circles.

4. Technology and techniques

The Tech workstream is not easy but is probably the easier part of the entire framework, simply because we have spent the most time and the most money in the past few decades.

This workstream includes the following major components: Infrastructure, network, security, access, DataOps workflow (operational data storage, through extracting, loading, and transforming, into data warehouses and specific-purpose-built data marts), operational and compliance reports, data visualization interactive dashboards, data catalog interface, low-code and/or no-code data science solutions, machine learning operations (MLOps), data science techniques, and Generative AI infrastructure and mechanics.

5. Processes

This workstream has three components:

• Agile project management: Agile methodology is not the magic wand, but we need to combine agility with the traditional linear waterfall approach in the CDAO team’s daily activities.

• Change management: How to document the changes, properly communicate the upcoming changes, and proactively resolve any anticipated issues.

• Internal communication: How to proactively summarize what we have done recently, tell the stories of why our work matters to the various stakeholders and how to measure the impact.

6. People

This workstream has the following elements:

• Focusing on talent pool with acquisition, retention, development, coaching and mentoring, growth, career mapping, productivity, belonging, rotation, lateral movement, and sustainable engagement. We need to work closely with human resources teams on upskilling training, career portfolio design, and functional job families.

• Building relationships and trust with stakeholders including sponsors, allies, early adopters, advocates, and beneficiaries.

• Working with various external partners including service vendors, colleges (for talent pipeline), government agencies (for compliance and collaboration), and non-profit organizations (social impact).

Bringing all 6 workstreams together

After identifying the above 6 workstreams, we can put them in the context of organizational capability maturity levels.

CDAOs can conduct their own simple assessments or leverage external resources (such as IIA or Gartner) to gather information to create a more comprehensive assessment, with a detailed description of gaps and action recommendations.

Note: The article was first published on the author’s LinkedIn blog. It has been republished with consent.

About the Author:

"Mr. Ge” Gary Cao advises CEOs and board of directors on analytics and AI strategy and serves as a fractional Chief Data and Analytics Officer (CDAO) or Chief AI Officer (CAIO). With 20 years of experience as a CDAO and serial founder of internal analytics startups, Cao has had a strong track record at 8 companies with revenue between US$40 million and US$120 billion.

Cao’s journey spans industries including healthcare (provider and payor), distribution, retail and ecommerce, financial services, banking, marketing, and credit/insurance risk. He is an expert advisor at the International Institute for Analytics and Rev1 Ventures startup studio and has been a speaker or panelist on various events and podcasts.