We are endlessly fascinated by the way fashions and fads seem to sweep through all professions – and the change management profession is no different. At the moment we, at P2, are having regular conversations with our clients about the application of predictive analytics to the delivery of transformation. What’s interesting about this is that, in terms of the fundamentals, there’s nothing particularly new about predictive analytics that should make it so current. The logic being applied works on the assumption that what has caused a particular effect in the past is likely to cause a similar effect in the future which (despite good intellectual caution that the past is not necessarily a guide to the future) is often true in the short to medium term. What we think of as experience is essentially an individual who has operated in a particular environment long enough to unconsciously spot patterns in the information that have, in the past, signalled where there is trouble ahead.

Most predictive analytics works on this concept scaled up. Let’s take for example the application of this concept to credit card fraud, as outlined at figure 1 below, and compare this with the challenge of identifying biased status reports in change programmes:

Figure 1 – Machine learning approach to predict credit card fraud

1. We identify a decision point (i.e. a credit card payment) and a validation criteria (i.e. it was valid or fraudulent)

2. We identify the factors that have a relationship to this decision point (where those payments are made from, what time of day, which products, how many etc.)

3. We use a tool or algorithm to look at many past examples of that decision, and work out which factors imply a valid decision, and an invalid decision (i.e. global credit card payments over the last year)

4. We then apply that learning to future decisions (and the factors leading into that decision) to flag where validation of that decision might be at threat (in the example above to flag which payments might be fraudulent and bring it to the attention of the fraud team)

This type of approach has been fairly standard practice in the financial sector for a number of years with extremely positive results. The basic logic of the approach as applied to the decisions taken by large scale permanent organisations should be just as applicable for temporary organisations like change programmes. For example, the logic used to spot a potentially fraudulent credit card payment should be applicable to spot a potentially unrealistic milestone RAG or resource forecast. However this sound theoretical approach has yet to be widely adopted in the transformation space for a number of reasons:

a) The computing power and tools have not been freely available ‘out of the box’ or at the price point most programmes work at.

b) Algorithms developed as far back as the 1980s and 1990s, such as Random Forest, have taken time to achieve widespread application by data scientists.

c) The perennial problem of ensuring the data is standardised and of sufficiently high quality to make accurate decisions.

d) Fast moving changes in the business space and operational context need to be reflected in the data for it to remain relevant

e) Traditionally the data sets have needed to be restrictively large to achieve predictive accuracy.

f) The change community (beyond the mega-project construction industry) simply hasn’t seen it as an available tool and so hasn’t looked to apply it!

Adam Skinner, Director, P2 Consulting
[email protected]
+44 (0) 7803 260508

Richard Rickards, Principal Consultant – Analytics for Transformation Lead, P2 Consulting
[email protected]
+44 (0) 7809 339930