Maximizing Metadata

Unlike the "telephony metadata" at the center of the US National Security Agency (NSA) surveillance controversy of recent weeks, the use of financial operations metadata should not reap global criticism.

Metadata can commonly mean a set of data comprised of attributes for each piece of data. In phone records, as was emphasized in the coverage of the NSA story, this means items such as length of calls, time of day and frequency of calls between the same parties. But for financial operations data, as discussed by attendees of last week's Sifma Tech Expo, this can be data about the parties to a transaction whose price is the starting, original data element, or other descriptive data about those transactions.

For instance, metadata can mean attributes created by an outside service provider to better enrich and calculate financial transaction data, as Eagle Investment Systems would define it, according to Jeremy Skaling, head of product management at the data technology and services provider. The company also sees metadata as a commodity that can be collected at a central point or utility, such as its Metadata Center service within its data management product.

Metadata may also be thought of as a categorization of firms' customer data to be available for linking to transaction data and other types of data, as Bob Molloy, associate partner, strategy and transformation, IBM Global Business Services, stated during the Sifma conference.

"For almost all our clients, when they put in compliance systems, they do it for that one system—with point-to-point linkage," he says. "All of a sudden, that won't work anymore. You must have flexible infrastructure. Being able to tie in metadata is becoming more important because you have to be able to link these records together effectively to be able to find all of them."

Capturing metadata has also become an important part of using the Data Management Maturity (DMM) model now taking hold at firms in the industry. Bank of America chief data officer John Bottega included the capture of metadata as a key element when building a new data governance program built on the DMM model last year.

The DMM model, released last year after three years in development, defines the parts, processes and capabilities necessary for effective data management. The model provides criteria for evaluating data management goals. Organizations are deriving value from the model itself, but have to think about metadata traits on top of the DMM model to really achieve the goals that the model's developers are aiming for—better data management to avoid the risks that caused damage to the industry in 2008.