Regular readers may recall my March 2017 article ^[1] which started by exploring failure rates of Big Data implementations. In this, amongst other facts, we learnt that between a half and two-thirds of a range of major business transformations fail to deliver lasting value ^[2]. After recently reading a pair of Harvard Business Review articles from back in 2016 ^[3], I can also add Analytics. Here is a salient quote from the second article:

Only a little more than one in three of the three-dozen companies that we studied met the objectives of their analytics initiatives over the long term. Clearly, driving major innovations with analytics was harder than many executives expected.

Once more we see what appears to be a fundamental constant emerge, around 60% of most major business endeavours cannot be classified as unqualified successes. I feel that we should come up with a name for this figure and ideally use a Greek letter to denote it, maybe φ which is as close to “F” for failure as the Greek alphabet gets ^[4].

The authors based their study on a 20 years of research spanning 36 client companies. The drew a surprising conclusion:

Efforts to adopt analytics upset the balance of power in the C-suite, and this shift often had a negative impact on analytics initiatives.

As ever (and as indeed I concluded in my previous article) reasons for failure have little to do with technology and everything to do with humans and how they interact with each other. This is one of the reasons I get incensed by Analytics teams saying things like “the business didn’t know what they wanted” or “adoption wasn’t strong enough” when their programmes fail.

For a start, Analytics is a business discipline and the Analytics team should view themselves as a business team. Second, to me it is pretty clear that a core activity for such teams is working with stakeholders to form an appreciation of their products or services, their competitive landscape, the markets they operate in, their day-to-day challenges and, on top of all this, what they want from data; even if this requires some teasing out (e.g. spending time shadowing people or using mock-ups or prototypes to show the art of the possible). Also Analytics teams must take accountability for driving adoption themselves, rather than assuming that someone else will deal with this, or worse, that “if we build it, they will come” ^[5].

The C-suite aspect is tougher, but in my own work I try to spend time with Executives to understand their world views and to make sure I align what I am doing with their priorities. Building relationships here can help to reduce the likelihood of Executive strife impacting on an Analytics programme. However, I do also agree with the authors that the CEO has a key role to play here in ensuring that his or her team embrace becoming a data-driven organisation, even if this means changes in roles and responsibilities for some.

I’d encourage readers to take a look at the original HBR material, it contains a number of other pertinent observations above and beyond the ones I have highlighted here. When either looking to prevent issues from arising, or trying to mitigating them once they do, my article, 20 Risks that Beset Data Programmes, can also be a useful reference.

Beyond this, my simplest advice is to always remember the human angle in any Analytics programme. This is more likely to determine success or failure than technical excellence, or embracing the latest and greatest Data Visualisation or Analysis tools ^[6].
 

 
Notes

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

Follow @peterjthomas