A Big Future for Big Data

Go back

Big data is big news. When one of the most successful of the fund-raising unicorns, ride-hailing service Uber, launched its new app in early November, the promotional puff said users would now be able to link their Uber app to their calendars, track the locations of their friends, and find out information, reviews and other news about their destination. It was clear to all that, as one report suggested, Uber wasn’t so much a transport app as a data company.

The company’s aim of leveraging all the data and information that is gathered around their users’ habits to subsequently persuade them to buy into further services is a classic big-data cross-sell. The aim it to place Uber at the centre of people’s live – and profit from it.

But the big daddy of big data is, of course, the internet giant Facebook. In the same week as Uber was announcing its intention to further leverage data about its users, UK insurance company Admiral issued a press release saying it would be launching a new insurance package that would see premiums for its customers determined by their own social-media posts.

The news caused something of a ripple of dissent among privacy campaigners, and indeed, Facebook was quick to pull the plug on the idea entirely citing the self-same issue. But as Aidan Fitzpatrick, chief executive and founder of app data company Reincubate, said on his blog after the news about Facebook’s action broke, this wasn’t really an argument about privacy. Rather, it is about how much the data gathered by Facebook can be used by third parties and how jealously the internet giant guards its own – or more precisely its users’ – data sets.

“Users have had their information stealthily and incrementally collected by Facebook over the years, often in ways that weren’t clear,” wrote Fitzpatrick. “’Trust us’ founder Mark Zuckerberg famously suggested. In this circumstance, a third-party is trying to innovate, and is asking users if they will happily share the data that Facebook has collected on them. Facebook’s position isn't protecting user privacy, but instead stifling innovation and ensuring that they are the only firm to use an individual’s data for service provision.”

It’s all about the leverage
What’s interesting about the Admiral example isn’t the fact that Facebook has essentially asserted it sown proprietorial interest in the data it collects about its customers – instructive though this is. Both Uber and the Admiral/Facebook instances are examples of how companies are moving to utilise big data to enhance the prospects of their own businesses by leveraging what the data tells them about the behaviour of consumers.

“I don’t think that there are any dangers of heading down the big data path,” says Marc Wood, founder and chief executive at Gambler Analytics. “The biggest danger is not heading down it, as others, probably with their own tech capability will. Along with BlockChain and cryptocurrencies, it’s another game-changing new tech that will change the dynamics of the gaming industry, eventually.”

The gambling industry isn’t alone in being somewhat slow to pick up on the importance of big data to the future of the business. A recent paper from Helen Mayhew and Tamim Saleh, both partners at McKinsey in London, and Simon Williams, co-founder and director at big data consultancy QuantumBlack, pointed out that the transformation of business via the utilisation of big data is yet to become the norm.

“The complexity of the methodologies, the increasing importance of machine learning, and the sheer scale of the data sets make it tempting for senior leaders to ‘leave it to the experts’,” they wrote in October this year. “But that’s also a mistake. Advanced data analytics is a quintessential business matter.”

The problem is often one of articulation of purpose and aim at the highest levels, but Mark Robinson, chief executive at real-time data analytics company DeltaDNA says that in the gambling industry this is slowly changing. “I think this is now a boardroom conversation across the industry,” he says. “There is a focus on this and a mandate to get this right. Newer businesses have perhaps an easier time because they have less legacy systems. The older, more established businesses sometimes find it more difficult.”

As Robinson says, knowing where to start is intimidating. The McKinsey team point out that clarity is vital and that organisations need to “put the question horse before the data collection cart”. “The impact of ‘big data’ analytics is often manifested by thousands – or more – of incrementally small improvements,” they wrote.

It’s a point which is echoed by Robinson. “It’s about doing simple and effective things well,” says Robinson. “We talk a lot to clients about their acquisition strategy and bonusing and making the onboarding interventions relevant and responsive to the players and responsive to the players.”

Team players
It’s a piecemeal approach, taking small steps such as focusing on in-game behaviour first before adding in other data elements such as deposit information, CRM statistics and other lobby data. Once this data is added into the mix, it has the potential to be very powerful, says Wood from Gambler Analytics: “There are two elements to this,” he says. “Firstly, I’ll deal with player retention which I believe where the biggest bang per buck will come from. Being able to mine vast amounts of historical and real-time data and developing propensity models will, in my view, enable the traditional industry metrics of player lifetime and value to be radically improved.

“Secondly in terms of acquisition, being able to access third-party real-time data and target specific socio-demographic types, at the right time and the right place with the right offer is the holy grail.”

As one commentator put it, analysing the digital crumbs that consumers leave behind them can add up to “great insight and great fortunes.” The gambling industry remains somewhat in the slow lane, in part due to customer acquisition being so much easier in the past but also because of the fragmentary nature of the data sets to hand. “The suppliers, the platform providers, have had in-game data, the operators have had out-of-game data,” says Robinson. “But now the industry is starting to address getting this data all together and in one place to get a single view of the player.”

The McKinsey paper identifies that organisations need to draw on expertise from across the whole business to fully exploit the potential from big data. “Analytics is a team sport,” they wrote. “Decisions about which analyses to employ, what data sources to mine, and how to present the findings are matters of human judgment. Assembling a great team is a bit like creating a gourmet delight—you need a mix of fine ingredients and a dash of passion.”

The McKinsey ideal team sheet is reproduced below, but the point is echoed by Robinson who points out that “there are different flavours” of analytics across most organisations, from the business intelligence (BI) team to the marketing and CRM departments. “The BI team could be all about dashboards, and charts and KPIs, and that is an absolutely vital part. But you need to know about the players. The BI doesn’t tell you much about the players. You have to do the segmentation.”

Big data doesn’t offer all the answers. Indeed, curiously, it is somewhat debatable whether the gambling industry actually generates big data in the first place, certainly at a company-by-company level. But what is certainly true is that the march of data analytics in all our lives will have an impact on this industry as much as any other. Privacy is one side of this debate, but expectation is the other. We may not complain is companies use our previous online histories to improve our present and future experience. But the onus is one companies to achieve that improvement. At the end of the day, consumers will expect nothing less.

The McKinsey breakdown on the perfect data team

Key team members include:

•Data scientists, who help develop and apply complex analytical methods
•Engineers with skills in areas such as microservices, data integration, and distributed computing
•Cloud and data architects to provide technical and systemwide insights
•User-interface developers and creative designers to ensure that products are visually beautiful and intuitively useful
•Translators —the people who connect the disciplines of IT and data analytics with business decisions and management.