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.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: http://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Data Management
Saugata Saha pilots S&P’s way through data interoperability, AI
Saha, who was named president of S&P Global Market Intelligence last year, details how the company is looking at enterprise data and the success of its early investments in AI.
Data partnerships, outsourced trading, developer wins, Studio Ghibli, and more
The Waters Cooler: CME and Google Cloud reach second base, Visible Alpha settles in at S&P, and another overnight trading venue is approved in this week’s news round-up.
A new data analytics studio born from a large asset manager hits the market
Amundi Asset Management’s tech arm is commercializing a tool that has 500 users at the buy-side firm.
One year on, S&P makes Visible Alpha more visible
The data giant says its acquisition of Visible Alpha last May is enabling it to bring the smaller vendor’s data to a range of new audiences.
Accelerated clearing and settlement, private markets, the future of LSEG’s AIM market, and more
The Waters Cooler: Fitch touts AWS AI for developer productivity, Nasdaq expands tech deal with South American exchanges, National Australia Bank enlists TransFicc, and more in this week’s news roundup.
‘Barcodes’ for market data and how they’ll revolutionize contract compliance
The IMD Wrap: Several recent initiatives could ease arduous data audit and reporting processes. But they need buy-in from all parties if all parties are to benefit.
‘The opaque juggernaut’: Private credit’s data deficiencies become clear
Investor demand to take advantage of the growing private credit markets is rising, despite limited data, trading mechanisms, and a lack of liquidity.
Fitch claims 20% developer productivity boost using AWS GenAI tools
The vendors have expanded an existing deal to include new Amazon tools that have helped Fitch modernize its infrastructure and applications.