The looming possibility of Brexit underscores the need for data clarity
Tactical regulatory firefighting has distracted banks from their long-term strategic shift to an enterprise view of data. Looming issues such as the possible Brexit highlight the urgency of this task, writes Mark Holland
This May’s election result means that the British banking sector is suddenly facing the very real possibility of a British exit from the European Union. Deutsche Bank has publically signalled that it will be reviewing the case for moving chunks of its hefty UK operations to Germany in the case of a Brexit, and has set up a working group to weigh the pros and cons of remaining headquartered in London if Britain was to leave.
This may be more a shot across Westminster’s bow than a genuine undertaking. It is early days yet. It is unclear at this stage what an exit would entail. Adding yet more uncertainty is the fact that the vote will not be on the status quo, but rather Cameron’s reformed deal, for which long negotiations have only just begun.
Nonetheless this is a scenario banks will be forced to consider in earnest sooner or later. And one thing that is certain is that the question will be a deeply complicated and multi-faceted one. It will carry repercussions right across the spectrum, touching on almost all aspects of banks’ operations. There will be tax implications – for instance the UK has the lowest corporation tax in Europe, and departure from the EU could pave the way for an even friendlier regime. There will be regulatory implications – banks currently benefit tremendously from EU passporting provisions, and it is unclear as to what extent a non-EU Britain would retain these. There will also be legal questions regarding, for example, data laws. The list goes on. And all of these factors will interact in a complex way. The very best possible information, combined with a clear and single view of the whole business, will be crucial to any decision.
All of this goes to underscore the need for better data management and joined-up infrastructure within banks. Strategic decisions of this magnitude cannot possibly be made properly without a) creating a logical dataset of all of the organisation’s data and b) having the ability to then glean useful insight from this data. Yet much of the current existing data environment in banks does not allow for either. Historically, the growth of banks has tended to be a sum of the growth of their different business lines – retail, investment banking, insurance, and so on. Most will typically have started as separate, relatively self-contained business units. They will have grown in silos, often at different times and using radically different systems to one another. As a consequence, banks’ data has also grown up organically in these same silos. The only place in which all this data normally comes together is in accounting, in the ledger. And yet the ledger – while providing a consolidated view of sorts – is often only an aggregation of transactions. The underlying detail either gets lost or remains stuck in the various sub-systems.
The push to rectify this and shift towards an enterprise view of data – whereby information from these disparate business units is ‘cleaned’ and brought together into one consolidated layer, providing a single view of the whole organisation – is, of course, a well-worn topic in the industry. Banks have been aware of the need to make a change for some time. Yet very little progress has been made. Since the crisis the issue has fallen down the agenda. Banks have been forced onto the back foot, their time and attention consumed with tactical firefighting in response to onerous new regulatory demands and deadlines.
There is a slight irony here in that many of these new regulatory obligations actually increase the need for a holistic view of data within these organisations. Stress testing, for instance, requires banks to be able to calculate the impact of certain scenarios right across the entire business. And banks are now required to provide regulators with a far more in-depth, detailed picture of business goings-on on a more regular basis in order to better allow the regulator to monitor and minimise systemic risk. So in its own way the regulatory agenda is driving demand for precisely this sort of beefed-up aggregation layer. However the pressured environment means that this transition – if it is happening at all – is happening in a piecemeal, reactive manner, as opposed to the longer-term, wholesale transformation project it needs to be.
Part of the problem with the short-term, reactive approach is that the creation of a proper consolidated aggregation layer often demands long-term planning and investment due to the challenges involved. The abstract notion of collecting all of your data into one place is a simple enough idea. But it is easier said than done. The collection of the data itself is merely step one of an arduous journey. Given the sheer volumes of data involved in the industry (we’re talking datasets with billions of rows here), there is a significant technological challenge in turning this data into something useful in a timely and efficient fashion. This is why we’re seeing such a drive to improve computing times. SAP’s HANA, which uses in-memory computing to process entire columns of datasets in one go, combining high transaction rates and complex query processing on the same platform, is a good example of the innovation we’re seeing on this front.
And this is before even considering the thorny question of ‘cleaning’ the data from the multiple sub-systems to ensure consistency and accuracy. The inescapable fact of the matter for most banks is that a proper move to an enterprise view of data will require substantial time, planning, and cost. It will not, and should not, happen in an uncoordinated fashion as a result of adapting to new regulatory demands.
What the sudden prospect of a Brexit referendum highlights is the importance of this undertaking, and the seriousness with which banks ought to take it. The post-crash environment has (understandably) subordinated the issue to the more immediate demands of regulators. But seven years on there is an argument for resuming a more pro-active, long term approach. Those banks that get this right will put themselves in a far better position to make long-term strategic decisions to their own benefit. The Brexit is just one example, and may come to nothing. But it is symbolic of the newly complex, global and uncertain world banks find themselves in, and highlights the increased need for big-picture thinking, for which an enterprise view of data is a crucial ingredient.