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Facing the reality of big data

In recent months, I have been speaking with several banks about how to employ big data technologies to solve real world business problems. Then, last month, I attended and presented at the Big Data & Analytics in Financial Services conference in London where I was able to speak with a wide variety of banks, product

  • Karl Rieder
  • August 22, 2014
  • 5 minutes

In recent months, I have been speaking with several banks about how to employ big data technologies to solve real world business problems. Then, last month, I attended and presented at the Big Data & Analytics in Financial Services conference in London where I was able to speak with a wide variety of banks, product vendors, and service providers.

A Mixed Bag

From all these conversations, I can attest that the state of big data in financial services is a very mixed bag. Banks are embracing these technologies using a wide variety of strategies and are at different stages of development. For example, one large investment bank two years ago kicked-off a series of IT-led projects which employ these new technologies to improve operational procedures, but with little sharing of information between them; another has worked to centrally collect data from throughout the bank and to establish a data lake upon which to conduct analysis and business intelligence; a third, following a year of investigation, is setting up a centre of excellence and is now consolidating infrastructure upon which it will begin to build business applications. The first could be considered an IT and operations approach, the second a business analytics approach, and the third a strategic, organisational one.  Three very different approaches in three very different states.

Lessons Learnt

It is still early days and there is a long way to go for each to fully develop their big data strategy. It isn’t too early, however, to take a view of what has worked so far and what hasn’t, and to set down some lessons learned from our experience so far. GFT has gained this experience through a number of programs of work using big data technologies in corporate, retail and investment banking, working both on the functional as well as the technical aspects of these projects.

Seven Steps to Success

We see 7 key factors that need to be taken in consideration when defining a new big data program:

Business – involve business from the beginning to understand the use cases and short, mid, and long-term goals.
Data – identify best sources of data and kick-off a data quality initiative to ensure that the processes and organisation are in place to guarantee a continued provision of high-quality, business-validated data.
Technology – choose the right technology platform given the current and future needs of the enterprise; consider that technologies are particularly dynamic in this area and that you need to have flexibility to adapt down the road.
Infrastructure – involve experts to get your infrastructure set up correctly – a well-tuned Hadoop cluster performs far better than one which is not configured well; expect that the infrastructure will need to continue to grow and provide agility to meet variable demand.
Projects – choose first projects wisely to ensure that they will be successful, demonstrate the capability of the technology, and have the ability to grow into a larger, more ambitions scope of work; work iteratively.
Team – have the right people on your team, whether that includes in-house capability, or external consultants, or 3rd party vendors; consider setting up a centre of excellence to ensure that the knowledge gained by this team is available to the whole organisation and that energies are focused.
Operations – ensure that the whole organisation, business, technology, and operations are well trained with the new tools and that the new technology brings value to the organisation without burdening it with additional obligations.

At the end of the day, big data should be an enabler for the bank to have greater insight into its business. Retail banks should understand better their customer behaviour and be better equipped to establish a more personal connection with them; investment banks should have greater visibility of their trading activity and be able to more quickly and effectively deploy new strategies; corporate banks should be able to more proactively work with their clients, foreseeing their needs across all products and business lines.

Business drives technology strategy

To achieve this, technology alone will not be enough. A broad strategic vision is required from the company’s management: the Chief Data Officer to define policies which will ensure data quality and take the most advantage of the bank’s data; the Chief Operations Officer to drive the business use cases including operational improvement, regulatory reporting, and analytics / business intelligence; the Chief Technology Officer to implement these use cases in the IT infrastructure and systems; and the Chief Experience Officer (an uncommon, but ever more important role) to ensure engaging applications for both customers and employees alike.

Common Challenges

What we have seen is that biggest challenges are, in fact, not technological, but rather organisational. For a bank to break away from its siloed mind set is in itself a huge challenge. Big data, however, can provide incredible value for the banks which are able to break down the walls between its business lines, bring its data together, and start to extract all the insight which is hidden within.

There is still long way to go, no doubt.

 

By Karl Rieder, Executive Consultant, GFT UK Limited