Why 2019 needs to be the year we slow down with data - a CDO perspective
- Summary:
- Caroline Carruthers explains why business needs to go back to basics with data before considering tech like AI and machine learning
We’re all well aware of the mammoth amounts of data being processed, managed and stored. There are plenty of common examples - 90% of all today’s data was created in the previous two years; every minute of every day, 2.7 million gigabytes of data is generated in the US alone; YouTube users are watching over four million videos; the Weather Channel receives 18 million forecast requests; and 103 million spam emails are sent.
And with the latest developments in Big Data, machine learning and Artificial Intelligence (AI), the potential for getting more value out of all this data is expanding.
But seasoned data expert Caroline Carruthers believes 2019 needs to be the year where organisations go back to basics with their data strategies, rather than rushing ahead to try and exploit new technologies. She explains:
This year is almost the bit where we need to slow down to speed up a bit. It's fantastic that people are talking about machine learning and AI and all these really fascinating subjects that have incredible potential for us as a human race. However, if we haven't got the basic discipline of looking after our data right in the first place, we're just going to be building houses of cards.
It's not that you can put a technology like that in place and just see what happens. It doesn't work like that. I would always advocate that companies have their basics right. By the basics, I would talk about having a data strategy, a team in place and then pick a problem the organization is working on and deal with it at that problem level.
Once a company has taken these basic steps – ensuring that their data is governed, is trustworthy, and that there is a team in place to look after it – then the time is right to use technology to solve that specific problem:
And then when you can demonstrate a minimum viable product, then what's the next problem along that conveyor belt that you then solve.
Carruthers was formerly Chief Data Officer at Network Rail as well as Group Director of Data Management at financial services firm Lowell. She's now a director at data consultancy Carruthers and Jackson and co-author of the 2016 Chief Data Officer's Playbook.
In her forthcoming book, Data Driven Business Transformation, Carruthers delves deeper into the importance of purpose in data management:
The point that we try and strike in the book is that you're never going to get it perfect so you need to get on with it. And I would always start with purpose. So if you're collecting data for the sake of data, why. You need to understand what your purpose, direction, strategy is, and then get on with it.
Don't try and get all your data right, and then do your engineering and then do your analytics. It’s this whole idea of a vertical strike, so you focus on the business problem and do what you need to solve that problem and then move on to the next.
Fear of data
One of Carruthers ambitions is to get people to rethink their fear of data, a fear that's rife following the Facebook and Cambridge Analytica scandals of recent times:
Cambridge Analytica is a typical example that everybody uses, it's such an easy one to use. There's this general big fear about data - 'oh my God, look what happens with data in the wrong hands!' - coupled with the whole idea that AI is coming to take over and we’re now creating The Terminator who’s going to kill us all and take all our jobs first before all that happens.
Part of the problem here is with some of the terminology the technology industry is using, according to Carruthers:
I was talking to Jeremy Waite, the Chief Strategy Officer at IBM Watson, recently and we were talking about terms like AI. I've talked about something for a while called Augmented Intelligence, and IBM talks about Intelligent Assistant. Those terms are much more meaningful than Artificial Intelligence, because it's not.
The general sense of unease over data privacy and the rise of the machine is a symptom of the fact that people aren’t quite as data savvy or literate as they should be, leading to a fear of the unknown. To help tackle this problem, Carruthers and Jackson is working with training provider QA Consulting to offer data literacy courses aimed at all employees, so everyone can have basic data skills.
Carruthers is also targeting a much earlier stage in the education journey. She carries out school visits to get the message across to younger audiences that “your data is worth something that's worth something to you”, and explaining the fundamentals of data transactions, that we all have a choice over what information we share in order to get different services.
She is also in talks with Camden Council in London to look at rejigging the curriculum:
To see whether we could drip feed some of these useful things into the curriculum, that's not overloading already what’s quite a busting set of expectations for our children.
Cross-learning
Cross-learning across sectors is also crucial to getting the most out of data. Carruthers believes that every sector has at least one good habit, which should be shared and fostered with other industries:
The banking and financial sector have got governance really down pat, but they're not really driving the value through with their data. Whereas if you look at some of the more entrepreneurial companies, they’re fantastic at driving the value, but they need to catch up on the governance side.
There needs to be a lot more sharing, and I really try and drive forward the idea of having a data community. Everybody's got a different piece of the jigsaw puzzle and we haven't put it together yet.
To this end, Carruthers and Jackson is hosting a series of data events around the world, aimed at building up a community of experts who will collaborate on these core challenges:
There's a lot happening in the data space right now and I want the data people to pull together into this community so we can actually solve these problems. It's not good enough to have all these jigsaw pieces in separate places anymore. We need to pull together.