CDOs, Let's Get Out of Our Comfort Zone

There is an ongoing assumption that a data and analytics team is mostly technically oriented. This assumption is reflected in two ways:  in a company’s organizational structure, with the team usually reporting to the CIO or the CTO; and in the composition of the team, frequently formed only by code-writing roles, from extracting and processing data to building sophisticated predictive models. But restricting the scope of Chief Data Officers to technical responsibilities highly limits their ability to bring value for the business, especially if they are also in charge of applying analytics.

First of all, I believe that a data and analytics team formed exclusively by technical roles lacks some of the key skills to successfully transform data into value. Without specialists in solution design and change management, technical developments are very likely to never make it to production, which happens more often than we would like. Secondly, an approach like this tends to limit the diversity of the team, minimizing the chances to innovate and think out of the box.

However, most training programs related to data and analytics (including the few aimed at CDOs) seem to follow the initial approach, reinforcing hard skills like data governance, data management, MLOps (Machine Learning Operations) in professionals who already have a technical background. Could this be one of the causes of CDOs having such a short average tenure, as highlighted by Randy Bean in a 2019 Harvard Business Review article and again in a recent CDO Magazine article following the 2022 MIT CDOIQ Symposium?

Don’t get me wrong. I truly believe that a data and analytics leader, as well as anyone in charge of other technical or functional areas, must bring experience-based judgment to the role. As leaders, we are expected to anticipate the risks implicit to our responsibilities and surround ourselves with highly-skilled professionals to cope with the technical challenges we have to face. Being able to guide our teams to find the best solutions, even from a technical perspective, is part of our job. It undoubtedly requires a frequent knowledge update from our side.  But that is not enough.

Generally speaking, we could summarize a leader’s responsibility as maximizing the value delivered by the team in a sustainable way. However, I would like to slightly modify that definition, introducing something I consider especially relevant for data and analytics teams. Value generation is just one step of the journey; what is actually important is that the value is acknowledged by all stakeholders. This has two implications. First of all, we need to measure the direct and indirect value delivered from the use of data. Second, we need to make that value visible for everyone in the organization. Let’s be honest, we are usually so busy executing a tightly-scheduled roadmap that we do not even have time to measure the impact of what we are doing.

Establishing operating models which optimize the relationships and dynamics inside and outside the team is a complex process in many dimensions. Adapting agile ways of working to the reality of data and analytics projects for production and scale-demanding environments is a huge challenge itself. Ensuring the adoption of analytical products by business areas is a key success factor for generating value, and it normally requires changes in processes, in behaviors, or both. Actively communicating the direct and indirect value generated by the team is at least as relevant, if not more, than all the effort applied to achieve it.

As passionate as we are about the new opportunities brought by AI and its fast evolution, we are very likely to spend a disproportionate amount of our time trying to catch up with the latest technical approaches. This is not unique to us; it happens to everyone that has been lucky enough to make a living from their passion. The difference is that there is no way we can be successful by just developing predictive models, no matter how sophisticated they are. 

It has been common to hear lately that the role of the CDO is the most complex among the C-suite executives, due in part to the lack of clarity of its responsibilities and the inflated expectations created around it, especially in companies hiring a CDO for the first time. Navigating these uncertain (and sometimes unwelcoming) waters requires courage, resilience and a handful of skills that, in many cases, are not part of our professional background:

• Promoting a culture which fosters collaboration inside and outside the team. 

• Developing new behaviors at scale from a deep understanding of motivations, fears and needs. 

• Deploying a communication strategy capable of spreading out the quantitative and qualitative impact achieved by the team to each organizational level. 

What percentage of our time are we spending developing and applying these skills?

Finding the right balance is always hard, especially when we have to get out of our comfort zone. But we must not forget that, as data and analytics leaders, we are expected to be change agents. That change begins with us.

About the Author

Iván Herrero Bartolomécurrently serves as the Chief Data Officer of Intercorp, a Peruvian corporation operating in retail, entertainment, finance, education and healthcare, leading the data and analytics strategy group wide.

Before joining Intercorp, Bartolomé was the Head of Data & Analytics at NTT Data Colombia and worked at Deloitte in Spain and Colombia as Senior Manager of Digital Strategy.

Bartolomé is a telecommunications engineer and holds a master’s degree in Business Intelligence and Big Data, as well as a specialization in Data Visualization.

In 2020, Bartolomé was elected President of CDO LATAM, a not-for-profit organization aimed at fostering the strategic use of data and analytics across Latin America. He also serves the community as the Country CDO Ambassador for Peru and a Global Editorial Board Member of CDO Magazine.