With rogue trading again making headlines, data quality can’t be ignored. In an investment bank or any asset management firm, your controls are only as good as your data.

UBS hit the press after a trader, allegedly had lost them $2.3 billion. Similarly, in January 2008, a trader purportedly lost SocieteGenerale $7.1 billion, virtually cleaning out the 2007 profits at France's second-largest bank. Barings Bank in 1995 was hit with losses that totaled more than $1.3 billion, almost the entire bank’s assets.

Access to data that could indicate suspicious activities, ability to consolidate such information, and continuous updates and cleanup preventing outdated data being used for fraud, are imperative, to stem such abuse.

Also, it’s clear that management of data in financial services organizations needs to fundamentally improve in order to facilitate stable and controlled business growth in the enterprise. Bad data costs money as it replicates itself throughout an organization’s information systems; a problem that is exasperated by deeply rooted process poverty.

Simply put, if the system is a body, the data is the blood, and if you have bad blood in the system, you can't have a healthy body.

Data management systems and processes must be tailored for the organization’s specific needs. For this, data management as a function needs to transform from being a mere cost center to a critical competitive advantage. The recent emergence of roles like Chief Data Officer in financial services organizations is confirmation of this need.

To date, one of the challenges with data management has been the ardent view that the pain points come from the data itself, rather than the processes that create, use, maintain and delete it.

Data follows a time-honored lifecycle of origination, normalization, storage, maintenance, and usage before final deletion. All the steps in the lifecycle involve numerous and diverse stakeholders, each with their own constraints and requirements imposed on the data.

As application of data becomes more real-time and interactive, the process of managing it is becoming as important as the data itself, if not more so.

Here’s what you should worry about.

Originators of data either feed instrument and counterparty data into an organization or create it through discovery from a wide array of sources. Counterparty related information bears little resemblance to instrument data and hence banks and firms need the following to ensure maximum value is derived for the organization out of any originated data:

You need to create:

  • Robust and discrete processes – with controls to identify missing or incorrect data across data inflow streams
  • Strong resolution workflows – to make necessary corrections before data is utilized downstream
  • Data review processes and interface parsers – that drive automated and semi-automated filters over the data feeds

Organizations typically also have data held in multiple formats across several different systems that require mapping, clean up, and amalgamation before it can be stored, used or distributed downstream.
An equally important function within storage is identifying, archiving and disabling/deleting redundant information. This is a largely ignored process as dead or unused data in the system is usually considered harmless or the littlest of all data evils.

Internal and external users will not expect reference data to be presented in raw formats for further processing by them. Instead, they require the data to be offered in bespoke formats and in line with standards they prescribe, in order to be able to process the data efficiently. Raising the quality of data is not costly. Not getting it right is.

Data itself is not the end product; it is the fuel that powers the systems that deliver the revenue-generating ideas. Or whack your bottom line, like at UBS, if you can’t see what’s going on and act on it.

This column originally appeared on Securities Technology Monitor.

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