Is data analytics a differentiating skill?
Top image, Climbing Up the Tree of Life.
At TeamFit, we regularly survey different industries on skill trends and look at our own platform to see what skills are getting the most use. One of the skills that have been trending over the past five years is pretty much anything associated with data: big data, data visualization, data analytics, predictive analytics and related skills like machine learning, deep learning and neural networks.
These are powerful skills to have in one’s portfolio and almost all companies need access to them one way or another. They are also showing up a lot in our surveys of skills that will be important in the future and in many company’s claims around differentiating skills.
Are data skills differentiating? The answer depends on whether an individual or company is asking this question and what industry we are talking about.
Before we go on, let’s make sure we share a definition of ‘differentiating skill.’ TeamFit defines a differentiating skill as follows.
Differentiating Skills: The core skills that provide a person, team or organization with its unique perspective. Differentiating skills are always relative to an alternative. If two people each have the role of Project Manager, they will generally share a large number of core skills but will have certain skills that distinguish them from each other and that determine how they approach project management.
The idea of differentiating skills comes from value-based pricing and the work of Tom Nagle. There are two key things to consider when thinking about differentiation. (i) Differentiation is always for a specific customer or market for organizations, or for a role or project for individuals. (ii) Differentiation is always relative to an alternative.
Studying trends in our skill surveys, we see a number of organizations across many different industries claiming the ‘data analytics’ cluster as a differentiating skill. (We will be publishing the results of our major 2016 survey “What skills drive success in professional services?” later this month). This is a contradiction. The more people who claim a skill the less likely it is to be a differentiating skill. This is true both for individuals and organizations. It would seem that over the past five years the data analytics skill cluster has moved from being a differentiating skill to a core skill.
There will be exceptions of course. In some laggard industries or disciplines, data analytics will still be differentiating. Companies that serve these industries can differentiate themselves by adding expertise in data analytics. Companies with deep data analytics expertise can try to use this to enter new segments where they have a competitive advantage. It is not clear to me what those industries are though. Everything from precision agriculture to HR has become data-driven.
A more promising approach is to marry data analytics with domain knowledge and access to data. With data analytics technologies becoming increasingly commoditized and well supported by off the shelf software, the differentiator will generally not be the analytics themselves but access to data. Domain knowledge plus access to data will be the secret to differentiation over the next few years. Eventually though, the data will become more and more widely available from multiple sources, data commoditization so to speak, and we will have to search out new areas of differentiation. But for the next few years, companies, especially professional services companies, will differentiate themselves by (i) gathering data and (ii) developing the domain expertise to interpret it. They will still need to build core skills in data analysis to support this. But the differentiation will come from access to data and specialization.
Look at your business and ask yourself, how can I build up a relevant data store and what new insights can I get from this data?
