Tuesday, 10 May 2016

#DataLondon16 #LondonDataFest - How to Change Technology & Culture in a Big Data World




More from the London Data Festival where I am attending sessions from the HR & Workforce Analytics Innovation Summit, as well as the other conferences - including the Big Data Innovation Summit and a session from Peter Sueref - Head of Data Science at British Gas speaking on Changing Technology & Culture in a Big Data World (and Why Both are Equally Important).

There’s a perception that big data can do everything (with reporting often suggesting there is an evil intent too).  But reality is that the world is inherently messy and unpredictable, not clean or anodyne.  Maybe 1 time out of 20 we’ll get it right but that’s often a result of pure chance.

So data science is dirty and difficult.  We need access to technology but people need time to go and explore these to.    People need open minds so BG tries to recruit for this.  And it’s useful to see data science as R&D vs a service, and to find alternative funding models, not projects.  BG deliver data science ideas (not projects) in collaboration with the business.  They keep these ideas on KanBan boards and team members decide which ideas from the backlog they want to work on, creating a pull vs push approach.

The first principle they follow is to try to demystify data science - it’s not something which needs to be left to phD power users but others who are fluent in data can get involved as well.  BG finds lots of existing SAS users want to get involved.  One thing which has worked is Bring Your Own Data sessions where teams sit down with a data scientist for half a day and discuss the use of clustering, time analysis or neural networks etc in connection to the users’ data.

The second principle is to show your working through open show and tells, explanation of models - showing bad results as well as good.

Thirdly, creating joint ownership of problems - helping create hypotheses (not just go and explore smart meters because we don’t know what’s happening), crowdsourcing metadata and working collaboratively, not just ‘Data Science as a Service’.

Big data is incredible - the possibilities are endless, but so are the possibilities of data drudgery.  Achieving the first and avoiding the second involves collaboration and democratisation of data science.

It's another reason why HR needs to care about this, and not just the workforce analytics being discussed within that session - HR needs to get involved with IT, Data Science and Analytics functions / professionals to extend the type of thinking Peter described across our organisations.  This may include recruiting people with open minds and I'd hope it might involve more pull based organisation structures and organising mechanisms too.


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