The other big trend in 2015 has been the noticeable move of banks away from Big Data to Data Analytics. Some say it’s all about the Small Data, but it’s more about the Context of Data.
Contextual Data is what PayPal brought into when they acquired Modest and it’s detail. This year, I’ve been surprised by how many banks I’ve met with Heads of Data and when you talk to them, you find that they’re heading up enterprise data change programs. Cleansing data, creating a single enterprise data store, leveraging data analytics, propensity modelling and trying to use internal data supplemented with external data from social media and other stores has been a big focal point and mantra throughout 2015. This will also continue into 2016, and a key will be managing the detail.
No one has yet fully worked out the right permissions model of data and date usage, but that is going to be the model of focus for the coming year(s), as we try to target consumers with far more intelligence than ever before. This is not just having the data, but knowing how to use it. That’s the big difference in 2016.
This is again stuff I’ve talked about a lot in the past but it’s really hotting up now as the data wars begin. Data wars is all about knowing more about how data could and should be used. It’s why so many firms from IBM to Facebook are investing in AI. It’s why we’ve just seen the launch of the OpenAI project LiNK which, like Open APIs, is all about opening the data for intelligence.
What is the intelligent use of data?
It’s sensing where I am, what I am doing, when I need something and how I need that something; and then targeting me with the right message, at the right time, about the right thing. That’s a tough call as most banks have only just got to the stage of training tellers to ask would you like a mortgage with that cheque deposit? which doesn’t quite cut the ice. Neither does Target targeting a teenage girl with a pregnancy kit before her father knew she was pregnant.
Intelligent, contextual data analytics is more what we see Google, Facebook and Amazon doing, where they can take 1+1 and equal 2. But even they get it wrong on many occasions. The fact that I googled about blood pressure once does not give google the right to sell that to third parties and inundate me with ads about blood pressure tablets, pads and systems for months on end (true story).
This is where permission is so important. I might give away my privacy by sharing on Facebook, Twitter and other media, but it does not give you the right to invade my privacy. What I choose to give is mine to choose to give; it’s not for you to take and abuse, as seems to be so often the case today.
However, this is purely because it’s early days and I can already see permissioned, intelligent, contextual data analytics starting to arise. Possibly one of the best examples I encountered in this space this year was the story of the new digital bank in Brazil, Banco Original. Bank leader Guga Stocco told me about his plans to offer concierge services that are far more intelligent than anything seen today, from KYC via your LinkedIn and Facebook profiles, to crowdsourcing ideas for bank products through your preferences.
I now use this story in every presentation, particularly the idea that the bank could proactively realise your Likes and respond accordingly. In Banco Original’s case, the idea is that they would crowdsource the purchase of 200 or more new BMW 5-series and offer these at 20% discount, two weeks before launch with a 5-year term loan bundled into the offer, on the basis that they know there are 120,000 bank customers who Like the BMW 5-series on Facebook. That’s intelligent, permissioned thinking.
Another example is the early days of Bank of America and Cardlytics making contextual offers based upon your GPS, purchasing patterns and preferences, such that you get the offer for a $50 discount on a USB hard drive 100 metres before you walk past the electronics store on the main street.
I loved another example from a few years ago where Commonwealth Bank of Australia produced an app that allowed you to check out the street you walked down, see which houses are up for sale in a visualised real-time augmented app and, if you liked on of the properties, immediately show you whether you could afford it and the mortgage offers from the bank.
These are great early examples of augmented, artificial intelligence based upon permissioned, contextual and intelligent data analytics. A space that can only develop further over the next few years .