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  • Nick Bonney

Putting the Direction into Data

Of all the catchphrases which have sprung up since I've been working in the insight arena, none seems to have caught on in the boardroom with quite such enthusiasm as Big Data.


This was a new dawn for business. Data trumps opinions. Data is the new oil etc etc. However, for those of us who have 'been around a while' and particularly for those who have worked in data rich sectors, this was evolution not revolution.


I'm sure I was not alone then in delighting at Dan Ariely's quote when it first popped up:-

The danger with 'Big Data' as a phrase is that it focuses on what it is and not what it does. As a result, two worrying trends have emerged.


Firstly, the debate has swung towards technology over commercial decision making. Too much time and money focused on investing in the kit and not enough time focusing on the experiences this data will create for consumers. Secondly, a worrying lack of discipline can enter into these discussions - if storage costs are no longer at a premium, why waste time on a data strategy or working out what the data is going to be used for when you can simply throw it all into the 'data lake'


This is where Dan Ariely's quote really hits the nail on the head. As business leaders we are convinced that someone else has found something magical whereas in reality they're all trying to figure it out as well.


The reality therefore is that for all the hype, some tried and tested principles remain.


1. Start with a strategy


OK, so this may sound obvious but it's surprising how often this doesn't happen.


Data is an enabler, it's not an end point. There needs to be a clear purpose for collecting the data (either for the business, the consumer or ideally both. Be clear on what data you need but also at what granularity and what frequency you need to use it. What data do you need at a granular customer level but also how does it need to be aggregated to help decision makers make sense of it.


2. Join up the what and the why


Inevitably the focus is often on the 'hard data'; the systems and processes which come with a large capital investment. However, this data can be meaningless unless you have a way to understand why those patterns are emerging. Qualitative techniques may seem 'old hat' in this technology-driven world but often it's the contradiction between the observed behaviours and the claimed motivations behind them which reveal the most useful insights.


3. Don't ignore the culture


A lot has been written about the 'golden triangle' of people, process and technology and nowhere is it more applicable than the world of 'Big Data'. Sure, you can invest in the latest tools and technology but has enough thought gone into the business processes which need to evolve to make best use of it. Even more importantly, what are the softer cultural issues which need to change alongside the technology? Who are the stakeholders who need to be influenced? Which teams are central to success? What skills need to be brought in? Dare I say it but what are the cultural symbols/ totems which might be placed under threat (this can be as simple as changing the dynamic in a long standing meeting)


4. Test as you go


If someone presents to you a 'data transformation project' which presents a nirvana at the end, run a mile. The reality is that getting this right will involve an ongoing process of optimisation and testing. The process will be iterative and you'll need to course correct along the way. In some ways this is nothing to be scared of - it allows for some early 'runs on the board' and to prove the value as you go rather than expecting a big payback at the end of a big project deliver. Think agile not waterfall.


5. Don't be a d*ck with Data


OK, this is shameless stolen from Andrew Day, CDO at Sainsbury's but it was too good not to borrow! Just because you can do something with data, doesn't mean you should. For all those 're-consent' e-mails that have come with GDPR, the great thing about the new legislation is that it has confirmed some common sense principles. Think about what data you hold, how you need to hold it and what you want to do with it.




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