Recently, we have seen technology industries and social networks iterating rapidly and extract in value from masses of data. It is natural for banks to want to match those abilities – especially when some of those fast-moving upstarts are beginning to compete with banks on certain fronts.

Retail banks have some disadvantages, though, in this race: they are accustomed to an environment where solutions are often developed and maintained in-house, and they have a greater duty to maintain their customers’ privacy.

Start-ups can rapidly scale resources and deliver services using third party solutions. And customers of search engines or social networks will often agree to the use of their data without even reading the terms and conditions.

However, banks also have strengths these new players struggle to match: capital, scale, experience, and a customer base that already provides large amounts of data to work with. The challenge then becomes how to make these advantages count, without disrupting services or raising costs.

Latency and layers

One route to performance is to take a leaf from these more agile competitors’ books, and take latency out of business processes. An important first step down this road is to “chop the problem up” – to break the broader challenges into many smaller tasks, and attack the ones with the greatest benefits first and fast.

Since my recent appointment to SAP’s new Database and Technology division – itself a kind of start-up – I have been identifying particular points of need with banks. Every one has its own priorities, but in many cases the solution lies in cutting down latency and cutting through layers.

Rapid data analysis can use speed to create more time. Decisions can be made, based on solid information, while other institutions are still pulling together their sources.

By focusing on one key area – profit centre analysis – and working with SAP to implement the right solution, one of our customers has cut the time taken to perform next-day analysis from eight hours to twenty minutes. That has huge implications for resource allocation and decision-making.

The amount of data in systems is always increasing, and the windows of opportunity to act most profitably on data analysis always narrowing. By starting with proofs of concept, moving into pilot projects and using that time to identify the key points for improvement, banks can get through those windows – with the right help.

Where do you think banks should focus their attention to make the most of faster, better data analytics? Financial Reporting? Accurate closing? Fraud detection? Or something else entirely?

Please share your thoughts below.