Hopefully the title got you to click on the blog, although the title is a stretch to fit the content and I probably missed the words “adventure” and “pirates”, but the change in attitudes towards data analysts is something that has been playing on my mind for a while.

Brands and businesses need to make decisions based on information available to them – nothing new here. Eliciting these decisions from data, and the various forms it takes, both structured and unstructured text, numeric, and code has become a vast task and in a bid to keep up the analysts have all been rebranded as scientists overnight to try to compete for, advance in and defend, market position. Essentially recognised for what they have known all along – the data finds the problems and the answers. This march of repositioning and re-branding is not going away and the engine is only just warming up….

This creates challenges.

  • There aren’t enough good analysts or mix in skill sets, and although the education system seems to be addressing this quickly, education does not replace experience– Although a close colleague of mine swears that math “isn’t taught the way it used to be……”


  • Many analysts rebranded to “scientists” have suddenly been told to “start thinking outside of the box” (as though they haven’t been doing this all along!), work in labs and hubs and start collegiately working, predict the future and put us ahead of the game…..but who will complete the work they were doing before? what about transition…?


  • Analysis as Usual needs to exist so how do you deal with this…..? Do you either burn out the scientists to whom you are giving this freedom, by just increasing work load? or do you take a risk on giving them freedom with unknown results, and risk again by increasing headcount with potentially inexperienced heads?

Technology has stepped up to the plate to solve some of this. “I hear your challenge” say Google, IBM, Microsoft, SAS and many others…we shall solve this for you using cognitive computing, automated decisioning…we will rebadge the old, mix with the new and deliver packaged solutions to help free your shackled scientists for the labs…to some extent this has helped and if deployed and utilised correctly can fundamentally free up time for innovation. Working for a data and marketing technology implementer I would say this!

So is data science the emperor’s new clothes?  If I am a CEO, and the CMO, CIO and COO are all saying we need innovation, but for every good hypothesis proven out there are ten that go nowhere,how do I make sure I haven’t just created a whole lot of cost? And how do I encourage a culture of free thinking when there are commercial pressures…how do I limit (note the word limit) the rabbit warrens of cost this could create?

Having worked with, and to all intent for, some excruciatingly good data analysts over the years, I believe I now have some exposure to what works well:

  • Collective ownership and understanding of the objectives of the area of the business – however open this may be – create revenue through the use of data, improve service through use of data, improve product through the use of data, reduce costs through use of data and so on.


  • Mr Myagi and Yoda play a hugely significant role. A good teacher/tutor, someone who can instill the scientific empirical methods alongside the “free thinking” but bring the team back from the brink of chasing its tail to an empty result for the business and brand.


  • Let the robots be the analysts and the analysts be the scientists. If it can be mechanised to good effect, let it be mechanised. However, this means this department needs to keep more than just an eye on the technology. It needs to be understood, in particular its limitations and where it could go wrong and where it could connect the wrong dots. So when done well the customer doesn’t suspect it is a robot, but instead a person who cares about them., Done badly the customer sees it as cost cutting, and the brand can’t even be bothered to invest in them or their experience.


  • Keep the commercial heads in the loop particularly the “entrepreneurs”. They may see value where none can be seen. The non-result may actually provide value where it has not been seen before.

Written by Daniel Telling 


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