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A-Team Insight Blogs

Operational Risk Could be Data’s Best Friend

By Daniel Simpson, CEO of Cadis

The story is one we’ve heard before – we’ve slowly come to recognise the importance of an issue, some of us have even started to do something about it but mostly it’s much discussed and remains on the ‘to do’ pile. So what is the tale this time? That of data management finally finding a voice that might just compete with – and benefit – front office initiatives.

It remains unlikely that working with data will ever be described as glamorous, what is revolutionary though is the realisation of just how important it is to manage that data. This is largely due to the charge of operational risk to the top of the agenda. This has in turn placed central importance on data management.

Data management is at the core of operational processes. As such the impact it has on any firms ability to identify and manage operational risk is enormous. It is therefore worrying that data rarely has champions within an organisation and often there is doubt as to who actually has ownership of the data – IT or the business for example. Different users place different value on the importance of data, so how can we realistically expect it to be managed and valued the same across the business?

Good operational risk management means collecting, organising, categorising and analysing data. An informed, intelligent decision relies on good data. In fact data should be seen as an asset. An asset in any other form would attract investment, resource and champions. The time is ripe for key investment in data and data quality.

Let’s get back to the crux of the matter. Talking about the importance of data management is great. What would be even better is action. Unconsolidated data and duplicate data cause inefficient operations, and can therefore aggravate operational risk issues.

Years of underinvestment, mergers and acquisitions, legacy systems, siloed information, inefficient data mapping, duplicate and inefficient data stores all contribute to a typical scenario where an organisation is making strategic decisions based on poor data. Intelligent decisions need intelligent data. Poor data means poor decisions.

Furthermore, in a time when eyes are firmly on costs it makes no sense to be spending money on getting the same piece of data multiple times – firms are often found to be paying three to 20 times for the same data within the front and back offices. Nor does it make sense to be spending precious funds on siloed, redundant and inconsistent data stores. Poor data management in financial firms should not be taken on board as an expected operational risk. Rather, senior management should be championing investment in data management as both a cost saving exercise and best practice for operational risk management.

The front office is awash with data and back office systems just don’t have the capability to consume some of the more complex data, especially around OTC products. Firms therefore need to look at the cost of owning the back office function. New instrument types continue to crop up and consolidation of firms through mergers and acquisitions means firms are increasingly considering outsourcing their back office.

Outsourcing of back office functions has been going on for ten years now, so it is not a new phenomenon, it is just that there is increased confidence in outsourcing as an option. The decision management need to make is whether to outsource the back office completely or just certain elements. This decision is key and depends on the structure and culture of the company. It is also a strategic decision as firms need to keep control over trading, risk and compliance systems and are therefore reliant on the outsourced service provider to supply data in a correct and timely manner to support these critical processes. The back office should be considered as a provider of data and data management projects as a way of empowering the business.

While operational risk has brought data management to the fore there is some resistance that champions will need to overcome. Data management projects, by and large, have a poor reputation for ROI and pace of implementation. Whether the decision is made to build in-house or to bring in third party vendors the same questions need to be asked. What is the implementation time? Have you achieved this before? Can I get ROI in six months or less? And can you integrate the disparate sources of data across the front, middle and back office into a single data management platform?

Data management is a central challenge to managing operational risk. The back office is a data provider for the front office and good decisions need cleansed data. Creating a single security, price and position master that forms part of a central data hub is a cornerstone to the art of successful operational risk management.

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