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Getting analytics teams to feed the innovation pipeline

Wednesday, February 21, 2018

 

I am not sure what your data analytics teams are like, but most that I have run across are so stretched doing recurring and ad-hoc queries that they don’t see the light of day.  They are also locked away with various layers of “protection” from the business.  These folks are like gold dust and what they do is in such demand that they almost hidden from the business.  I was at a conference the other day where a data analyst was talking about how they were “feeding” ideas to innovation teams based on observations they made from real historical data. WOW, this sounded like an early Christmas gift for many Innovation teams for various reasons.

 

Finding real opportunities: Innovation teams are normally not short on ideas of what to fix in the organisation.  The thing they are short of is a prioritised list of ideas based on real facts.  There are plenty of the normal suspects; reducing costs, finding new revenue sources and even customer experience improvements, but the ones you are really after are the ones that do all three and may even improve your employee’s lives. What you want are these “super ideas” that are backed up by real data.  Bring in the analysts.

 

Building the business case:  As we all know, there is nothing more soul destroying then finding a great solution and then getting caught in a death loop trying to substantiate the business case.  The fact that the data is all there should make this process much easier.

 

Getting data analytics onside: Further to the business case issue, gut feel decisions usually run into a data analyst down the road who actually has the data.  If this data refutes your idea then the Proof of Concepts and pilots can be stopped dead in their tracks.  The fact that these guys actually found the opportunity puts them on your team right out of the gates.

 

Finding “Black Swans”: I don’t care how much industry experience or gut feeling that you have, overlaying this with proper analytics that can churn through reams of data you will give you better results.  Also, unless you are very lucky, you will not see anomalies that are showing up in a process. These so called ‘Black Swan” events are sometimes the jewels that we look for to make the “10X Innovation” change happen.

 

Finding the “Bleating Obvious”: The opposite of the Black Swan is the Bleating Obvious.  Many teams I have worked with using design thinking will go for the most complex and difficult case to fix and walk by the bleating obvious.  Again, the use of data analytics and visualisation will bring these to life.

 

I guess the point is if you able to pry these teams out of the dungeon and get them helping you in the front end of the Innovation pipeline you are likely to find some amazing, data substantiated ideas to work on.  Bring them on, I will buy pizza and beer!

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