CDAO Survey: Making the Leap from Strategy to Execution

Data leaders find themselves at the center of a rapidly evolving business environment. Data and analytics must continue to change from a support function to an essential, core business competency. The tumult of 2020 has accelerated and expanded digital expectations of organizations and customers, and these demands cannot be met without data-fueled decision making at speed and scale. 

A recent Gartner survey of CEOs found that 82% have major initiatives underway to make their companies more digital, and 77% plan to increase their investment in digital capabilities. Leaders across the C-suite understand that data will determine the return on that investment; as Piyush Chowhan, Global CIO for Lulu Group International, explains in an episode of The Next Big Question podcast, data is the glue that binds diverse stakeholders together to deliver on digital potential.

Each year, Evanta’s Leadership Perspective Survey takes the pulse of chief data officers from around the world and identifies their top challenges and opportunities. Just past the midway point of 2021, the survey results reflect the demands of the digital paradigm:

Top Challenges

1. Culture change and data literacy
2. D&A governance
3. Improving D&A strategy and operating model
    

These challenges echo conversations data leaders have been having in Evanta Virtual Boardroom sessions and Town Hall gatherings for the last 18 months. CDAOs and their teams have an intimate understanding of data’s strategic potential, but 60% of our data community tell us they struggle with execution. 

Gartner research finds that in part, this disconnect stems from the overall business mindset toward D&A. Many organizations still view data solely as a component piece of successful business initiatives, and they measure efficacy in those terms. Where many CDAOs struggle is measuring the ways data can increase overall business outcomes. In the fifth-annual CDAO survey, only 23% of Gartner’s respondents “defined and tracked metrics to measure the value delivered to stakeholder outcomes.” 

This shift is subtle and difficult to articulate across the business – not all analytics, metrics and data products have a direct, causal impact on business outcomes, so such correlations are hard to communicate and measure with precision. Organizations that succeed along this journey share three key features:

1. An “Outside-In” Value Chain

More than ever before, consumer behaviors and expectations determine business success; outcomes-based D&A has to start with a vivid picture of the customer. Traditional consumer metrics have long informed business decisions, but in the accelerating digital context, the data organization must understand the full customer experience. 

Increasingly, customer journey mapping is done by the data centers of excellence under the leadership of a CDAO. This understanding of customer value is then fed into business units that can now ask the more pointed question, “What is our part in creating that value?” This approach to data strategy organically links stakeholders across the business, rather than focusing solely on the data requirements siloed within given functions. 

The “outside-in” approach is also ideal for organizations undergoing transformation efforts, as the orientation on the customer helps technology leaders focus on new processes and tools while helping business leaders identify new go-to-market opportunities. 

1. A Balance Between Centralization and Distribution 

Organizational structure can be a huge stumbling block for organizations moving from data strategy to execution. Consistency of data, overall data literacy and lack of talent are major contributing factors to this challenge, but these elements point toward an even greater need for the right data operating model. 

The chief data officer as a role is emerging in fits and starts across the business landscape, but centralization under the office of a CDAO is increasingly important. This links business strategy with data strategy and gives the office of the CDAO a clear vantage point to identify gaps and focus on KPIs for business outcomes. A CDAO can also coordinate with a governance board to ensure stakeholder priorities are clearly understood and that use cases are both aligned with those priorities and shared across silos. 

At the same time, data ownership must come from the business groups, and data capability should be embedded in there. Cross-functional teams now include expertise from the business domain, the data science function, engineering/development, and the analytics team.

1. A Constant Focus on Culture

Chris Fitzharris, senior director of data and analytics for C.H. Robinson, uses an interesting analogy to discuss a strong data culture. As he explains, “Our analytics community has been bringing their pail to the stream of water every time they need it, then hauling it and boiling it before they can even use it. We want to say, ‘Go to your faucet and turn on the water.’ And that’s what we’re doing so the business can win.” 

From Fitzharris’ point of view, a winning business views data like a utility – a foundational tool that propels everything else. That mindset articulates the ambient demand for data experienced by all digital businesses. To give that mindset greater rigor and build a stronger data culture takes time. CDAOs tell us that it can be built in any number of ways: detailed listening sessions to better understand business problems; internal marketing of wins along the journey; targeting important business units and power users first to build momentum. 

All these tactics are important, but what makes them work is consistency. Data culture is a powerful accelerant, but it must be built over and over again, every day.