Guest blogger Tony Boobier replied to my question by approaching it from a different perspective. Tony has shared his different take on familiar topics before. He shared these views with us on goals, data viz and training. Regular readers will recall that Tony is an author & advisor focusing on AI & Financial Services.
In this post, he usefully prompts us to think about two types of data leaders and the different levels of understanding of the technology stack that each need. What matters most to each? How realistic is it for any leader to understand each part? Over to Tony to help us think that through…
Directions for a tourist
There’s a well-known joke about a tourist who asks one of the locals for directions to his hotel. The local replies: ‘Well sir, if I were you,’ he says ‘I wouldn’t start from here’. You may have heard it before, as this joke has multiple iterations about any city and any nationality in the World. The philosophical argument which aligns to the joke is that we are usually more concerned with where we are going to, than with where we are at the current time.
In that spirit, I wanted to spend a few moments thinking not about the technology stack, but rather the use of the expression ‘data leader’. What does it actually mean? Is a ‘data leader’ the same as a ‘data-driven leader’? Or are the two expressions synonymous?
Technology always struggles with definitions
Technology constantly and increasingly struggles with meaning and definitions. Where does predictive analytics stop and prescriptive analytics start? What is the boundary between ‘hard’ and ‘soft’ AI, for example? Doesn’t technology often hide behind jargon, which also extends to job titles?
In that spirit, let’s ‘unpack’ what we mean by ‘data leader’, and compare that to what we might describe as a ‘data-driven leader’.
What does a data leader need to know?
A data leader might be described as someone ‘in charge of the data’, with responsibilities for all parts of the data ‘stack’ such as storage, cleansing, management, security and analysis. That individual is likely by nature to be a technologist such as a CTO or CIO.
Whilst they might have ultimate responsibility for the ‘technology stack’, they’re unlikely to know everything about everything and at best will only have a general awareness. Almost certainly they will have particular strengths and interests. The reality is that each part of the technology stack has now attained a degree of complexity that it’s almost impossible to be aware of everything that is happening. That knowledge is not only relative to the ‘status quo’ but especially in terms of development and innovation.
The data leader is likely to have a strong awareness of what is happening throughout the entire stack. But, he or she is likely to rely on experts with particular skills to provide more detailed assistance. For example, they are unlikely to have the statistical knowledge to undertake the detailed calculations that experts operating in predictive analytics carry out on a day-to-day basis.
The exception is perhaps in smaller organisations where the burden falls more heavily on one individual or a small team. Dangers can exist in such a situation. It is all too easy for that individual or small team to be either opinionated, to remain within their comfort zone, or simply to struggle with issues of personal work bandwidth.
It’s all too easy for individuals or small teams to be lured by, or cynical about claims made by vendors. Claims regarding new technological products and services. In what is a rapidly developing sector, both temptation and cynicism have to be avoided. The best response needs to be one of active, open and objective awareness.
Data leaders need to consider these aspects
So to summarise, the focus of a ‘data leader’ is principally someone who focusses on the data stack. But it’s more than just technical competence. Choosing the right technology or vendor isn’t an easy task, and key elements of decisions and recommendations can be both multiple and complex. Data leaders need to be concerned with broader technology vendor issues such as:
- Vendor Organisation: How a business is structured and supported operationally
- Vendor staffing: number of employees and staff churn
- Revenues and financial status: Growth, profitability,
- Customer care regime: including 24hr support
- Quality systems and procedures: such as ISO 9002
- Quality of technology solution
- Cost of integration into existing systems
- Degree of dependency on key clients
- Regulatory Compliance
- Scalability of any system
- Switching costs
- External analysis: Forester, Gartner
- Cultural fit of the vendor with the client
The point is that choosing and managing technology suppliers is much more complex than choosing the ‘right’ technology.
Managing some or all of these elements will naturally fall to supplier managers and procurers. They also need to have the specialist technical expertise to allow the most appropriate recommendations to be made.
What does a data-driven leader need to know?
The Data-Driven Leader is quite a different role and function. Their particular emphasis is on using data to make sound, data-driven decisions. This means a move away from solely intuitive or experiential decisions, to business decisions which are made using data-driven insight. Analytics are the mechanism by which the ‘value’ is released from data, allowing the data-driven leader to make actionable decisions which affect operations, customer-driven activities or the management of risk.
A ‘data-driven leader’ may not necessarily worry about what is happening in the technology stack as far as data storage, cleaning and distribution are concerned. As individuals, they might have a particular preference in terms of how that information is ultimately provided to them. For example, the type of visualisations adopted (as this is the part that they are most exposed to). Other than that, their focus is on the application and impact data on business decisions. On how this affects the business outcome.
In other words, the emphasis of the ‘data-driven leader’ is on the application of data to business decisions and processes, not on the data stack itself.
Where should a data-driven leader focus?
By way of an analogy, think of it a little like driving a car. The Data Leader is the mechanic, who ensures that the vehicle operates as expected. If need be, this ‘data mechanic‘ might rely on other experts with appropriate specialisations, but is seldom concerned with the journey ahead. Their priority is to ensure that the mechanics are suitable and fit for purpose.
On the other hand, the ‘Data-Driven Leader’ seldom worries about what actually happens under the bonnet. Their concern is to drive the car and reach the destination, without necessarily knowing the pros and cons of particular engine components. For them, no single component in the stack is more important than another. They are concerned with the satisfactory operation of the ‘whole’
That is not to say that the ‘Data-Driven Leader’ should not have awareness of the technical expressions used and that for example a ‘data cloud’ is not based somewhere in the sky. The reality is that today’s leaders, and especially tomorrow’s leaders, are increasingly gaining hybrid skills. They often find it helps to increase their understanding. So the two job functions and titles are, to some degree, already converging.
Getting back to Paul’s technology stack question
But I have, perhaps eloquently, avoided the question. Which is that of which part of ‘the stack’ is most important?
Fortunately, the answer is probably the same, for both job functions. If there is one element of the technology stack that both the data leaders and the data-driven leader need to be most concerned with, it is that of the management of data security.
Cyber-crime threatens to undermine systems, it attracts massive regulatory penalties and undermines customer confidence. Without effective cyber precautions, organisations are affected both at a technological and operational level.
In a technology world subject to both jargon and occasional ambiguity, isn’t ‘cybersecurity’ an area where there’s no room for doubt as to its meaning?
What is your view?
Thanks, Tony for that, somewhat circuitous answer. Some useful questions posed along the way as ever.
Do you agree? Both with Tony’s case for the data security parts of the technology stack & the lens of those two data leader roles?
If you have a different perspective to share on my original question, please use the Comments box below. Until then, keep safe.