Keeping in contact with your customers is a vital ingredient to ensuring that the hard, expensive work of acquiring customers and then delivering a quality product is translated into brand loyalty, repeat orders and ultimately high lifetime value.
Counteracting this situation, or ensuring you do not slip unwittingly into it, requires a cohesive data-strategy, one that blends technology, psychology, business transformation and data. And it is up to the business management to work in unison with the data team to understand and shape use of data rather than relegating it to a purely technical or IT function.
A recent study carried out at Virgin Atlantic offers a great illustration. In this parable, the airline wanted to increase the efficiency of their aircraft by reducing the cost of fuel used per flight. Airplane modifications and upgrades are of course costly and thus are not the lowest or easiest fruit to start picking. It was decided that the first port of call would be to work with the pilots to see if anything could be done to look for operational efficiency (Gosnell G. K, List J. A and Metcalfe R., http://www.nber.org/papers/w22316). An edict duly came down from on high with instructions asking pilots to optimise where possible and find ways to use a little bit less fuel per flight.
The data team then got involved and an experiment begun. The pilots were split into three groups:
The first group were asked to “save a bit of fuel please“. Fuel use was recorded and analysed in great detail to see if they did save any fuel. However, these pilots were not told about the new fuel use data logging.
The second group were also asked to “save a bit of fuel, please“. This group were told about the new detailed fuel data logging. However, the resulting analytics and data were not shared; but management “are watching!”
The third group, also asked to “save a bit of fuel, please“, were both told about the detailed data logging and, after every flight were given the comprehensive breakdown of their fuel use. Detailed analytics were provided for every flight on fuel used during taxiing, take-off, cruising and landing and was made available to them with comparisons and averages of other pilots, routes and planes. Additional gamification techniques, with the Airline donating money to pilots choice of charity for every £1 of fuel saved, were also used to incentivised the pilots.
It does not take a great leap of the imagination to workout what happened next. The first groups behaviour changed very little and fuel use remained the same. The second did save a little bit of fuel, but not much. But the third group started using a lot less fuel. Taxiing using one engine only, less aggressive take-off and more relaxed engine braking at landing as well as optimising cruising altitude, speed and routes all helped save an estimated 6,828 metric tons of fuel, worth £3.3 million during the course of the study. And an additional and unexpected side benefit was that the group exposed to the charitable donation gamification reported the highest level of job satisfaction.
This data-led feedback loop, known as nudging, helped the pilots execute a key company policy and provide a perfect example of exactly the type of thinking that sits at the core of any business use of data and analytics. Data is not just important; it is *really* important. It should be used to shape and inform the many thousand decisions and actions each of your team make every day. And for this to happen you, the business managers, need to define clearly what you consider to be important; then work with your data team to measure it and share it, so allowing your team to shape their work to push that measure in the right direction.
It’s easy to resent the next generation of tech when it renders all the strife you’ve been through to master the current generation worthless. Until of course you realise that this next wave solves so many of the problems you’ve been wrestling with and, by embracing change, your life becomes much easier and more opportunities open up. I have lost track of the hours I have spent walking round and round the block, trying to grapple with the mechanics of early Hadoop MapReduce code, building up a library of routines and patterns that could be called upon to solve most problems. Then along came Apache Spark and suddenly it all became so much easier. Damn you in-memory resilient-distributed-datasets making my life better and my code run faster!
I’m joking of course. Well, mostly. Progress in Big Data technology has been staggering. A few years ago the challenge with Big Data Projects was often defined by technical practicalities, like how do we store this amount of data and how do we even being to process it? We don’t have to search too far back in time to remember how technically challenging, and thus costly, this was. However, as we reach the end of 2016, the toolsets now available as well a significant focus by cloud platforms, in particular Amazon Web Services and Microsoft Azure to automate much the heavy lifting, mean that there is now a smorgasbord of cost effective architectures that enable companies of all sizes to work with Big Data. Demonstrating this shift, a recent Gartner report stated that, “In 4 years 90% of all data will be on Next generation technology”. From start-ups to multinationals, if you want to work with Big Data, you can.
Many companies have now been through the pain (and cost) of experimenting with Big Data projects. Through these platform iterations we’ve reached a technological level where we can now deploy reasonably cost effective solutions of staggering scale. But so what? We’ve collected this data, we’ve deployed scalable infrastructure, but what’s the benefit?
It’s a fair question. Why do this stuff? Perhaps due to the historic technical difficulties, wide scope and far-reaching possibilities, Big Data projects have tended to be Engineer or Data Scientist led, rather than by Management. Even the famous three Vs of Big Data (Velocity, Volume, Variety) describes the nature of the data, rather than what we are trying to do with it. The three Vs are great, but so what? It’s not exactly something you put in a board level presentation and leave your audience with a sense of direction. Over the years the three Vs have evolved … to the four Vs. We’ve added Veracity! I’ve even seen the slightly tongue in cheek Ten V’s of Big Data as well…
This is not helping. The only sane response from anybody trying to run a business is “OK, we have lots of data. So what?”
We Big Data experts really need to start thinking of a better definition. Something that others can engage with as well as guide us as to what we are trying to achieve. We need to stop the ‘so what’ question from coming back to bite us on our behinds.
“Ah ha!” our audience now says. “I’d quite like to gain a competitive advantage. That sounds advantageous!”
“It is” we reply. “It can be most advantageous indeed”.
“But how, and what sort of advantage would I be looking to gain?”
“What a good looking question”. We respond.
And indeed it is a good question. We can now start discussing the hopes, dreams and worries of the organisation. How can data solutions be put to use to solve each of them in a targeted, costed and most importantly, effective, way. Invariably in business, most hopes, dreams and worries come down to creating more revenue, improving op- erational efficiency, reducing risk or driving business change (in order to create more revenue, improve operational efficiency and reduce risk), but the key is that by starting from a new, better definition all data projects, big or small, will now be driven by a focused need. So what? Well there will be no more “So whats” for a start.
Gartner (again) predicts that by 2020, 75% of large and midsize organisations will compete using advanced analyt- ics and proprietary algorithms. Deploying this type of tech throughout a whole organisation is no small feat; the challenge being both technical and business transformational. Tackling both these aspect of data projects is in- creasingly moving into the domain of Big Data projects. In order for data to drive the enterprise the trend over the next few years has to be the engagement with a more holistic approach, perhaps one that starts with a better defi- nition of Big Data.