What’s in a Name? (Part 4) — Data Analytics Leadership
Background
I’ve now written 3 articles in my sub-series (here and here and here) that questions both the need and the impact of the proliferation of a great many roles and sub-practices in data analytics. For example, we’ve recently created new roles such as Decision Scientist, AI Engineer, Data Connector. The same proliferation of Data Analytics leadership roles has also occured, albeit at a much slower pace. Now, good Data Analytics leadership has always been in short supply and unfortunately, the positions have also been riddled with imposters. And so, in part 4 (and last) of this sub-series, I unpack the implication of role (and sub-practice) proliferation on the evolution of data analytics leadership.
(I write a weekly article on bad thinking and bad practices in data analytics / data science which you can find here.)
C-Suite Misnomer
Many large organisations segment their data analytics workforce into a variety of roles and responsibilities, regardless whether they pursue a centralised or federated organisation design. I’ve argued here before that having a variety of roles does not equate to the need for having a variety of sub-practice domains. Nonetheless, what would you call the head of your Centre of Excellence? What would you call the head of your Data Scientists team? What would you call the head of your Data Visualisation team? What would you call the head of the entire data analytics group?
Let’s start with the big ones. I asked Google Bard, “What are the key differences in responsibilities of the Chief Analytics Officer, Chief Data Officer, Chief Information Officer?” Google Bard very nicely summarised the key differences as follows:
- Chief Data Officer’s (CDO) role is to be the custodian of the organisation’s data assets. In layman’s terms, this would be data quality, data maintenance, data access, data usage, data protection.
- Chief Analytics Officer’s (CAO) role is the lead and build organisational capability to extract actionable insights from the data and develop them into business solutions. In layman’s terms, this would be data mining, predictive modelling, AI solutioning.
- Chief Information Officer’s (CIO) role is to plan, implement and manage IT infrastructure that is aligned to business objectives. In layman’s terms, this would be system architecture design, IT budgeting and roadmapping, system availability, data security.
I’ve always felt that the CIO title is a misnomer. There is a specific definition to what constitutes information in data analytics and the broader knowledge economy. The CIO role seems a semantic upgrade of the classic Chief Technology Officer (CTO) to make it more business-oriented. IT = information technology. But it’s used as a short hand for all computer technology systems in the organisation; the information part doesn’t have any meaning and isn’t the driver of strategic thinking for the ‘CIO’.
The CDO role is another problematic one. The CDO’s charter is to oversea data as a strategic asset. But they don’t have executional responsibilities which makes the role uninformed. Too often, the CDO ends up playing a governance role instead, and becomes a hindrance to the organisation rather than an enabler. Shouldn’t it be the responsibility of the CDO to drive frameworks and methods to measure the economic value of data? How can the CDO direct the appropriate resources to manage and enhance the organisation’s strategic data assets if they can’t value it? Also, it should be the CDO’s responsibility to define and shape the organisation’s data collection strategy, but to do this requires the CDO to have a deep understanding of the information signal gaps and distortions in the existing dataset. This is only achieved through having executional responsibilities.
WTF Roles
I’m also calling bullshit on these data-related roles — Chief AI Officer (CAIO), Chief Digital Officer (CDgO), Chief Innovation Officer (CINO). I can hear the rancorous disagreement already from my friends!
Let’s start with the Chief AI Officer. There has been a significant proliferation of this role driven by the sharp increase in AI adoption interest. The CAIO is responsible for articulating the organisation’s AI roadmap in terms of opportunities and deployments, cultivating an AI culture within the organisation, and managing the legal and ethical implications of AI usage. Organisations without a CAO are suddenly keen to appoint a CAIO; this is ridiculous. AI is data science, albeit there’s a lot more deep integration required to make AI solutions successful, but that can be achieved through a strong partnership with the CTO. Like the CDO role, the CAIO has no executional responsibilities / capabilities, and likely no decisioning rights. It’s an influence role. It’s a dud at the get-go. Organisations should enfold the ‘CAIO responsibilities’ into the CAO. If you don’t have a CAO, then properly appoint one. There is no short-cutting this.
Next up is the Chief Digital Officer role; another popular one. And another dud. The CDgO is responsible for articulating a digital strategy for the organisation, overseeing enterprise-wide adoption of digital technologies for business process improvements, improving customer experience (CX) through the optimisation of digital interfaces and channels. It’s an alignment role. I’ve written here before about the intimate link between digitalisation efforts and maturity in data strategy. The more you digitalise, the more cognition you lose, and the more you need to supplement that loss with thoughtful data instrumentation and data analytics. Most CDgO are not ‘data certified’. It’s one of the reasons CDgO have one of the shortest tenure in the C-suite. Digital is just another form of computer technology systems. Responsibility should sit under the CTO in partnership with a process re-engineering office that is set up specifically to measure improvements in efficiency and effectiveness through digitalisation.
This segues to the adjacent role of the Chief Innovation Officer. The CINO is responsible for fostering a culture of innovation and creativity in the organisation (whatever the heck that means!) often with a charter to run an innovation lab (puh-leeze!), piloting new business models and products (isn’t that a line-of-business role?!), and disrupting the status quo and aligning innovation with overall business strategy (I’m gagging!) Increasingly, the CINO looks more like the CAIO because, by general consensus, AI is regarded as the greatest innovation game in town. Most CINOs arrive from having done successful work in business strategy. Innovation isn’t a step-change exercise but an incremental process, and requires deep knowledge on technology and business processes. As business processes digitalise, the CINO must naturally be proficient in data analytics. Almost none are. Similar to the CDgO, the CINO’s corporate tenure is extremely short, typically inside of 24 months. The outcome speaks for itself — a dud role.
Back to Basics (Conclusion)
We need a serious re-think of the C-suite roles that have sprung up around the data analytics practice. Some are claiming association through very stretched arguments like the CIO role. Information Theory should inform how we go about constructing these (data analytics related) leadership roles. Information Theory tells us that we need to put data through a ‘sieve’ to extract first information, and then knowledge (information that drives decisioning). I argue that all you really need is the CAO and CTO roles. The former defines the effective nature of what we are extracting based on deep intimacy with the source material, while the latter provides the means to operate that ‘sieve’ efficiently. Add on new responsibilities to these two roles as we go along without the need for role speciation — e.g. the CAIO responsibilities clearly sits with the CAO. The CDgO responsibilities should sit with the CTO. This desire for role speciation does nothing but confuse the non-complex agenda of achieving integrated intelligence for the organisation.
