Corporate data-gathering has very much been in the news in recent weeks – and not for the best of reasons. But there are important lessons to be learned from the sorry saga of Cambridge Analytica and Facebook.
In short – are you expecting people to hand over their valuable, personal and private data simply for you to make money?
Incidents like this are potentially very harmful to the public’s trust in data and analytics as it is used by large corporations. And that’s bad news for everyone. Because without trust, the supply of data is likely to be greatly restricted.
Incidents, where public data is shown to be misused to encourage governments to more stringently regulate the collection and transfer of information, and it discourages individuals from allowing businesses to access their information in the first place.
Counter-intuitively, from the point of view of someone who encourages businesses to collect, analyse and use personal data, the first of those is not intrinsically a bad thing. Because without rules in place, there can be no trust. And with no trust, there can be no Big Data.
What the issues around Cambridge Analytica make clear, though, is the importance of understanding (and communicating) why you are collecting data in the first place. And if it isn’t to benefit your customers – improve their lives, make them more satisfied, or make it easier for them to do what they want – you’re probably on the wrong path.
Big Data Backlash
Corporate data gathering often makes the headlines for the wrong reasons, usually when there are large-scale breaches. And these breaches are becoming increasingly common. After all, with more and more “personal data” – information which can be directly linked to an identifiable human – being created at an exponential rate, it shouldn’t really be surprising that more and more of it is going missing, or turning up in places where it shouldn’t be.
Hence in recent weeks we have seen one of the largest-scale public backlashes against the corporate practice of large-scale data harvesting, with Facebook bearing the brunt. The #deletefacebook hashtag has been tweeted thousands of times, and company executives have been summoned to explain the social media giant’s activities in front of governments. Their claims that the loopholes which allowed the Cambridge Analytica “exploit” to happen were closed before the breach was even made public have done little to assuage the public’s ire.
Some might consider it to be a dangerous time to be talking about Big Data at all, in fact. But that would be missing the point. Big Data gathering isn’t intrinsically a bad, or good, thing – it all comes down to the intentions of those doing it.
Big Data – Help or Hindrance?
The scale of the backlash does teach us one very valuable lesson though. As far as the public is concerned, if your data strategy doesn’t appear to have any real benefits for the subjects of your data gathering – which will generally be your customers – then why should you have a data strategy at all?
Everyone knows that Facebook, and indeed Google, and a host of other internet giants, can only operate because we are willing to give them data, which they, in turn, can monetize. As the saying goes, “If you’re not paying for the product, then you are the product.”
And it’s clearly a transaction that millions of us are happy with. Google gives us access to basically all of the world’s information, and Facebook puts us a click away from our friends and family, as well as a host of businesses and brands we are happy to interact with. Most of us are by now well aware of how this is all financed and have made the decision that a bit of information on who we are and how we live our lives is a fair price to pay. Just as long as that information is properly looked after, of course.
This is the test that every business needs to apply. Can they genuinely say they are giving their customers fair value for the information they are collecting?
Amazon would say it tracks its customer’s buying habits in order to consistently have the products they want available, at a price they are willing to pay. Businesses like Fitbit, or Hive, take things a step further by making data itself the product. Their devices aggregate data from across their user base and pass it back as insights that those users can put to use, to improve their fitness or more efficiently use energy in their homes.
Engineering and aerospace firms such as GE and Rolls-Royce have been known for their data capture and associated value-added services for many years, but companies such as John Deere have also transformed itself from a manufacturer of agricultural machinery to a supplier of data. Using insights from sensors on its machines, and augmented with third-party data such as weather and satellite imagery, it now provides information which farmers can use to plan every aspect of farm management, from routine maintenance to where to plant their crops to get the best yield.
Royal Bank of Scotland went as far as developing a data strategy which they called “personology” which involved monitoring everything from customer service calls and emails to their own transactional records to work out how to precisely identify what an individual customer’s needs were. Rather than trying to convert customers to fit the needs of the bank – in other words, convince them to buy what they were selling – the aim was to develop services which fit the needs of the customer.
Turning traditional marketing practices on their head like this is a great example of how data and analytics can be used – along with a bit of lateral, customer-focused thinking. To slightly paraphrase JFK – “Think not what Big Data can do for you, but what Big Data can do for your customer”!
As I mentioned previously, the entire Big Data revolution is predicated on trust. Business and industry must gain the trust of their customers otherwise the data-streams will simply stop flowing, and soon dry up.
To avoid this happening, I strongly encourage organizations to think about the examples given above, and to try and understand how the value of data and analytics can be passed back to the public who have entrusted them with their data.
At the core of every aspect of a data strategy should be an answer to the question “How is this improving the lives of my customers?” – and if you can’t answer that question, then maybe that element of your strategy is surplus to requirements.