Big Data is setting up camp. But can your organisation feel the warmth from its fire?

Sam Miranda

The financial services industry is blushing; not in the wake of the global financial crisis and accusations of shyster capitalism, but as a result of the sizeable bulge in its pocket. Big data is pitching its tent, roasting marsh-mellows and frying sausages under the cloud.

The arrival of Big Data, a strategic investment

The message from the cynics and sceptics is clear: big data is a buzzword, merely a product of vendor evangelism. I.T. purists may tell you that it’s the computing power that can be fired at data in real-time that’s new and big. Another unofficial classification centres on the ‘3 Vs.’ Data is classified as big depending on its volume, variety and velocity.

In an interview with the Financial Times, IBM’s Rod Smith, VP for emerging technologies, responds honestly to accusations of marketing hysteria: "We tried to call it different things over time – petabyte computing and other things – but the term big data stuck because the customers I was talking to were business people."

In an exclusive PEX Network podcast, Warwick Bailey, Vice President of Business Intelligence at Barclays, calls big data a "boastful term…the solution lies with finding the data that presents commercial value for the organisation."

Big data is becoming less of a computational experiment, and more of a business challenge as firms focus on application - how can a financial organisation use insight and analytics to drive not only its internal, operational processes but also its customer relations?

Before outlining case studies, it’s worth reminding ourselves of big data’s rise to prominence, and how it is both a product of, and a way of benefiting from, an increase in ‘customer consciousness.’ The influx of structured and unstructured customer data is a product of what Cloudera dubs a "digitisation and commoditisation of financial products and services." Consumer engagement and touch-points, or ‘hyperconnectivity,’ has reached unprecedented levels. Mobile and online banking have delivered waves of transactional data, which needs to be carefully captured in order to avoid samples with flimsy assumptions and aggregations.

There has also been a democratisation of finance, with people turning to retail trading, taking care of their own investments and making their own pension provisions. The result is that financial products and services have been commoditised. Ten years ago, selecting a bank was more a question of family heritage than financial flexibility. Now students are scouting for the easiest loan repayment structures, young workers for the best ISAs and retirees for the shrewdest bond investments. Community hubs such as Mint and comparison websites such as Moneysupermarket have got customers making informed decisions. This change in consumer mindset has forced financial institutions to think like retailers: harnessing demographic, geo-spatial and transactional data to develop personalisation and recommendation techniques.

Before the arrival of programs such as Hadoop, big data was an insurmountable package left on the CIO’s doorstep. In a 2011 interview with James Turner, Cloudera CEO Mike Olson offers a summary of the Hadoop revolution: "The Hadoop platform was designed to solve problems where you have a lot of data - perhaps a mixture of complex and structured data - and it doesn’t fit nicely into tables. It’s for situations where you want to run analytics that are deep and computationally extensive, like clustering and targeting."

Programs such as Hadoop and MapReduce offer scalable solutions for portfolio analysis and retrieving actionable consumer insights. In an interview with Forbes, Gary Bhattacharjee, Executive Director of enterprise information management at Morgan Stanley, explains the seamless relationship with I.T.: "Since Hadoop stores everything and it is schema-less, I can carve up a record or an output from whatever combination the business wants."

This is a view affirmed by Bailey, who credits the technical community for shifting data warehouses to new technologies, be that "bespoke internal systems or cloud environments."


Big Data benefits; Barclays, Citigroup and Bloomberg case studies

The more mechanical questions surrounding the role of big data in an organisation - how you re-engineer traditional warehouse systems, break down the data silos, ensure near-real time visualisation and leverage the necessary internal resources - are all very pertinent.

But, for the sake of this article, let’s assume a robust infrastructure is in place and look at tangible business benefits.

Anthony Coln on the Siliconangle blog separates them as follows: "Smarter business, better banking. Smarter customers, better business." The former refers to enterprise benefits – adherence to regulatory and compliance requirements, risk management and predictive credit risk models, fraud prevention and algorithmic trading decision making. The latter refers to consumer enlightenment and increased engagement levels, the subsequent influx of structured and unstructured data, and finally the translation of insight and smart analytics into marketing strategy.

The immediate priority, Bailey explains, is regulation and compliance – something that "needs to happen. It’s not exciting, it doesn’t really grow the business, but we look at compliance value. Over the past 3 or 4 years, we’ve concentrated on Basel I, II and III, and understanding the data behind capital requirements." Basel III, a regulatory standard on bank capital adequacy, stress testing and market liquidity risk, was established on the back of the global financial crisis and solvency problems. Bailey continues: "The projects I’ve been involved in look to marry compliance and growth, so you’ve got something to grab hold of from a business perspective – profitability, credit risk analysis and pulling together the silos for the customers."

So what of these more expansive, business friendly efforts to use data? Given the levels of public scrutiny directed at the financial services industry, it’s unsurprising that many big data initiatives remain clandestine. The harvesting of customer sentiment to predict future purchasing patters and generate personalised offers has somewhat sinister, Orwellian overtones.

But we’re starting to see successful case studies emerge, partly due to conferences, which promote confraternity. Abhishek Mehta, Managing Director for big data and analytics at Bank of America, told Hadoop World: "Bank of America, Walmart…all data companies. You don’t push cash around, it’s moving in bits and bytes. And we realise that we want to be good custodians of it, and increase transparency that we have in the bank…to drive positive change."

It’s interesting that Mehta should mention Bank of America and Walmart in the same breath. In the UK, traditional supermarket retailers such as Tesco and The Co-operative are growing finance arms, leveraging shopper insight to steal a march on established banks. They are free from the shackles of ‘siloization’ – the result of archaic back-end systems which service customer finances and loans. Bailey believes that the "winners will create agile platforms, like those we see in the retail or telecoms sectors. They can action the findings from analytics."

Global banking firm Citigroup may be setting the pace, adopting more customer-centric initiatives with its hiring of IBM computer ‘Watson’, according to a report in The Economist. The aptly named machine mines consumer information to offer personalised products and services such as loans and credit cards – an activity that begins with assessing creditworthiness.

Citigroup’s analytics hub in Singapore are also using card transactions as a vehicle for actionable insight: the customer swipes a credit card, the hub scans his location and purchasing history, and offers a recommendation based on this information. It’s snowing so I go in to shop, use my credit card to pay for a woolly hat, and receive a text message offering me a 20% discount on a scarf. I get a good deal; the bank receives a small cut from the new transaction and generates more insight.

Mutually beneficial opportunism, or Big Brother conditioning? You decide.

Lloyds TSB is running the most altruistic project, forecasting account balances when new bills emerge, and communicating the updates to customers.

And then there’s financial news giant Bloomberg, with perhaps the most innovative big data operation. It has partnered with data provider WiseWindow and consumer sentiment measurement firm Mobi to index social media chatter surrounding major companies, and indicate stock performance. According to the CEO of independent analytics shop Emerald Logic, WiseWindow’s data boosted equity trading returns on companies such as GM, Ford and American Airlines by 30%.

Barclay’s Warwick Bailey: "We need people who get excited by data"

There’s a compelling case for launching big data initiatives and building a team of ‘data artisans’ into your organisation. These employees table business acumen alongside architectural and analytical understanding.

One of the criticisms fired at big data is that it creates a corpocracy, where major firms grow exponentially as they harvest swathes of data and insight. But this isn’t true; today’s eclectic mix of financial services and products gives start-ups such as ZestCash – who provide the niche offering of loans to customers with poor credit ratings – a market edge.

Ron Shevlin, senior analyst at Aite with over 20 years in the banking industry, identifies three veritable challenges. The first lies in understanding big data as a marketing driven technology, and developing the infrastructure to reach customers quickly – what he calls mastering the "sense" and "respond" dynamic. The second is to amassing a team of data scientists for customer data integration when these expert statisticians are in short supply.

Bailey identifies the archetypal pro: "We need people who can discuss with a CIO what needs to happen to the whole suite of systems and understand the life-cycle of data. They also need to know what makes the business tick – assets, return on equities, performance and customers."

The final challenge is data security, not only with internal management, but when reaching out to customers with sensitive information about accounts and investments. This is no space for the slapdash marketing mails that we see in other sectors.

Big Data is setting up camp. Use it safely and strategically to reap the warmth from its fire.

You can hear more from Warwick Bailey in this podcast interview: Barclay's VP Warwick Bailey: Unlocking the commercial value of Big Data