The new CxO gang: data, AI, and robotics
Hiring new figures to lead the data revolution (Part 1/3)
It has been said that this new wave of exponential technologies will threaten a lot of jobs, both blue and white-collar ones. But if from one hand many roles will disappear, from the other hand in the very short-term we are observing new people coming out from the crowd to lead this revolution and set the pace.
These are the people who really understand both the technicalities of the problems as well as have a clear view of the business implications of the new technologies and can easily plan how to embed those new capabilities in enterprise contexts.
Hence, I am going to briefly present three of them, i.e., the Chief Data Officer (CDO), the Chief Artificial Intelligence Officer (CAIO) and the Chief Robotics Officer (CRO). Sad to be said, I never heard about a ‘Chief of Data Science’, but for some strange reasons, the role is usually called either ‘Head of Data Science’ or ‘Chief Analytics Officer’ (as if data scientist won’t deserve someone at C-level to lead their efforts).
Let’s see then who they are and what they would be useful for.
The Chief Data Officer (CDO)
Apparently, it is a new role born in a lighter form straight after the financial crisis springing from the need to have a central figure to deal with technology, regulation and reporting.
Therefore, the CDO is basically the guy who acts as a liaison between the CTO (tech guy) and the CAO/Head of Data Science (data guy) and takes care of data quality and data management.
Actually, its final goal is to guarantee that everyone can get access to the right data in virtually no time.
In that sense, a CDO is the guy in charge of ‘democratizing data’ within the company.
It is not a static role, and it evolved from simply being a facilitator to being a data governor, with the tasks of defining data management policies and business priorities, shaping not only the data strategy, but also the frameworks, procedures, and tools. In other words, he is a kind of ‘Chief of Data Engineers’ (if we agree on the distinctions between data scientists, who actually deal with modeling, and data engineers, who deal with data preparation and data flow).
“The difference between a CIO and CDO (apart from the words data and information…) is best described using the bucket and water analogy. The CIO is responsible for the bucket, ensuring that it is complete without any holes in it, the bucket is the right size with just a little bit of spare room but not too much and its all in a safe place.
The CDO is responsible for the liquid you put in the bucket, ensuring that it is the right liquid, the right amount and that’s not contaminated. The CDO is also responsible for what happens to the liquid, and making the clean vital liquid is available for the business to slake its thirst.” (Caroline Carruthers, Chief Data Officer Network Rail, and Peter Jackson, Head of Data Southern Water)
I don’t want to get into what a Chief Information Officer does and how he differs from a CDO, a CTO, a CRO, or any other roles, but if you want to know more I highly recommend Julie Steele’s free ebook (see here).
Interestingly enough, the role of the CDO as we described it is both vertical and horizontal. It spans indeed across the entire organization even though the CDO still needs to report to someone else in the organizational chart. Who the CDO reports to will be largely determined by the organization he is operating in. Furthermore, it is also relevant to highlight that a CDO can be found more likely in larger organizations rather than small startups. The latter type is indeed usually set up to be data-driven (with a forward-looking approach) and therefore the CDO function is already embedded in the role who designs the technological infrastructure/data pipeline.
It is also true that not every company has a CDO, so how do you decide to eventually get one? Well, simply out of internal necessity, strict incoming regulation, and because all your business intelligence projects are failing because of data issues. If you have any of these problems, you might need someone who pushes the “fail-fast” principle as the data approach to be adopted throughout the entire organization, who considers data as a company asset and wants to set the fundamentals to allow fast trial and error experimentations. And above all, someone who is centrally liable and accountable for anything about data.
A CDO is then the end-to-end data workflow responsible and it oversees the entire data value chain.
Finally, if the CDO will do his job in a proper way, you’ll be able to see two different outcomes: first of all, the board will stop asking for quality data and will have clear in mind what every team is doing. Second, and most important, a good CDO aims to create an organization where a CDO has no reasons to exist.
It is counterintuitive, but basically, a CDO will do a great job when the company won’t need a CDO anymore because every line of business will be responsible and liable for their own data.
A good CDO aims to create an organization where a CDO has no reasons to exist.
In order to reach his final goal, he needs to prove from the beginning that not investing in higher data quality and frictionless data transfer might be a source of inefficiency in business operations, resulting in non-optimized IT operations and making compliance as well as analytics much less effective.
Note: the above is an adapted excerpt from my book “Introduction to Data” (Springer, 2019).