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Data Management

04:52 PM
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Does a Managed EDM Service Preserve A Firm's DNA?

Bloomberg PolarLake and Markit EDM announced a non-exclusive agreement to distribute each others' reference data.

Bloomberg's PolarLake, a managed service for Enterprise Data Management (EDM) and Markit’s EDM has announced a non-exclusive agreement to distribute each others pricing and reference data. Bloomberg PolarLake customers now have access to Markit’s data, and vice versa.

PolarLake was acquired by Bloomberg in May of last year. Prior to that it served as a technology and consultant business in the data management, distribution and integration space. Since the acquisition it has launched options for clients to take on data management as a managed service, enabling clients to manage and distribute large volumes of financial data, get out of legacy infrastructure and corresponding chores like updates and maintenance.

In the press release, John Randles, CEO of Bloomberg PolarLake, said: "In today's demanding regulatory environment, financial institutions want a data governance process that reduces costs, mitigates risk exposure, and enhances data quality. This agreement enables Bloomberg PolarLake to deliver a more comprehensive data management solution as an installed or hosted EDM service, in order to better meet the needs of our financial services clients."

An important note for vendors, data is kept separate from Bloomberg and their data business, as Bloomberg PolarLake is a wholly own subsidiary with separate data centers, operations, and staff.

According to Randles, the firm has a "great critical mass" of vendors across multiple specializations on board, and they are on track to move 10 clients to the managed EDM model by the end of the year.

Industry Trends

"We see today's announcement as a significant milestone for the financial data industry," says Randles. Twenty years ago, he explains, it was typical to over-simplify and over-commoditize data. "They didn't bring the amount of technology to bear that they need to do managed service versus utility… Everyone tried to address challenges using a relationship data model, which was fine for a small number of classes and feeds, but what we face today is the classic big data challenge; a huge number of feeds and volume of data, in different shapes and with different cardinalities."

"But," he continues, "the biggest challenge is the constant change in that data." If you have multiple feeds, products, asset classes, they likely experience changes every day because vendors are making changes, impacting internal data and internal cross reference data, classification and feeds. Because of this treadmill of updating the software, firms get stubbed with enterprise data management, spending too much time on modeling, instead of using the data or concentrating on its business value.

"The vendors need to bring it together," he adds. "We take a different approach to enterprise data management that improves data speed and usability." Using their managed service and on boarding PolarLake is helping to generate efficiency and processing across clients and vendors. To Randles, the greatest benefit to clients is their ability to retain specific business rules and data formats, as well as preserve intellectual property – maintaining key decision-making power as they migrate to adopting managed services. "Deploying EDM as a multi-vendor solution is a fundamentally different way of approaching the data management challenge that almost requires that we rebuild the system from scratch. If all vendors deliver data according to the same set of requirements, then the product and the service is pure utility. Once you get into the details, it's easy to see that the entire DNA of a firm is defined by how they use data." Becca Lipman is Senior Editor for Wall Street & Technology. She writes in-depth news articles with a focus on big data and compliance in the capital markets. She regularly meets with information technology leaders and innovators and writes about cloud computing, datacenters, ... View Full Bio

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