In a world awash with data, being conversant in big data methods and terminology is now a must-have skill for managers. This understanding is vital for any organisation that hopes to consistently outpace its rivals, says Ray Eitel-Porter, managing director and analytics lead for the UK & Ireland at management consultancy firm Accenture.
“Increasingly, the success of a business can be measured by the ability of its executives to dream up questions that competitors haven’t even thought of asking yet,” he says.
The good news is that big data approaches not only make it possible to ask these questions, but also easier to get better answers, giving business leaders rich, multi-dimensional views of a given situation, by combining vast volumes of structured and unstructured data from a wide variety of internal and external sources.
The bad news is that traditional methods and tools for data analysis, with which non-technical business executives might at least enjoy a passing familiarity, aren’t up to the job. New ones are often needed to handle big data, taking them into uncharted territory.
They’ll need to navigate an alien landscape, populated by Hadoop and NoSQL platforms for data analysis, rather than traditional databases. They’ll hear developers enthusing about tools with weird names like Pig, Hive, Flume, Squoop and Oozie. They’ll need to consider incorporating third-party datasets and external data feeds into analyses, rather than relying simply on the operational data captured and stored in their company’s internal back-end systems.
And the results of analysis will need to be presented to users within the business in new ways, says Axel Goris, a business intelligence expert at management consultancy PA Consulting. “Organisations can only really hope to get true value from big data when end-users can explore data and play around with the results of analysis, to test new hypotheses about markets, products and customers,” he says.
If your company is way out in front, the job is to extend its lead – and big data is a good way to do that
On the plus side, in the age of cloud technologies, there are many ways to access big data tools and expertise on an on-demand basis. There is a thriving marketplace for big data skills and analysis, where specialist consultancies jostle to organise and probe big data on behalf of clients. The hardware resources needed to store and process big data can be easily procured from cloud-based infrastructure-as-a-service (IaaS) providers, such as Amazon Web Services and Microsoft with its Azure platform.
Where companies want to keep big data in-house, for reasons of customer data privacy, for example, a platform such as Hadoop is based on ostensibly free-of-charge, open source software and runs on low-cost commodity servers, unlike expensive proprietary data warehouses. All these aspects need to be factored in when building the business case for big data.
Meanwhile, for companies that choose to take a wait-and-see approach to big data, the danger is that their competitors won’t, says Mr Eitel-Porter at Accenture. “Once a competitor steals a march on you, you’re in the unfortunate position of having to play catch-up. It’s very hard from that point on to regain lost ground.”
If your company is way out in front, by contrast, the job is to extend its lead – and big data is a good way to do that.
Take, for example, British online gaming company King.com: this year, it overtook San Francisco-based rival Zynga as the world’s largest social gaming company.
The company is using big data to preserve that market advantage, using Cloudera’s Hadoop-based platform to collect data on millions of gamers and the games they play. That helps the company better understand game usage, customer preferences and advertising revenue associated with each game, in turn helping it to produce better games and sell more advertising space.
For an example from a more traditional industry, there’s British Airways’ Know Me programme, based on data collected through its Executive Club loyalty scheme and its website.
BA has recruited big data consultancy Opera Solutions to help it analyse that data, and get more insight into the personal preferences and buying patterns of its most loyal frequent fliers: whether they need a vegetarian meal, for example, or prefer a bulkhead seat so they can stretch their legs.
It’s BA’s plan to provide a more personalised service to its high-value customers than its competitors can, at a time when it is being squeezed from below by low-cost carriers like easyJet and from above by high-end airlines like Emirates.
In fact, big data may provide an answer to helping traditional industries identify entirely new business models that will differentiate them from their competitors, says Tony Lovick, a telematics pricing actuary at consultancy firm Towers Watson. He’s helping the company’s insurance industry clients to explore usage-based pricing for automotive policies, based on data collected from telematic devices in customers’ cars that report on speed, distance, routes, harsh braking, airbag deployment and so on.
“We’ve quickly seen how more complete data sets and more finely tuned data collection models can help automotive insurance providers better understand individual drivers and their habits,” he says.
“What that means is a potential win-win situation: it will benefit customers, who’ll be quoted rates that fairly reflect their driving habits, and it will benefit insurance providers, who will be able to attract those customers based on a better understanding of the risk they represent.”
In the insurance industry and elsewhere, bigger data means better insight, but it’s turning insight into action that creates competitive advantage. Certainly, powerful big data technologies and skilled data scientists are necessary. However, it’s data-savvy senior leaders, delivering on that necessary final step from insight to action, who are likely to drive the most impressive business results.