Asset Managers Attack Data Silos with Governance

Data governance isn’t just for megabanks anymore. Asset managers are gradually joining the crowd of believers, creating rulebooks for how data quality is ensured enterprisewide with business lines at the helm.

That was the consensus of panelists and attendees at a TSAM North America fund management operations event last month where data management took center stage as a priority in 2017. Of the dozen operations managers from fund management shops attending the gathering in New York who spoke with FinOps Report, five say that their firms have already implemented a new data governance model while three will be doing so next year. The remaining firms are in the “discussion phase” between business units, technology managers and C-level executives.

Historically, asset management firms haven’t enjoyed a holistic view of their data. “Data management fell into the domain of the IT group who didn’t understand how it was really used,” says Paul McInnis, head of enterprise data management for BNY Mellon subsidiary Eagle Investment Systems in Boston. “Each business line had its own IT budget, so there was little alignment across the broader organization. The business lines adhered to their own rules which led to different ontology standards and permissions of who could access and oversee the data within the various units.” The result: inconsistent or duplicative data.

“The journey toward data governance for asset management firms is just beginning,” asserts Kenneth Lamar, principal partner at Lamar Associates in New York. “C-level management at the big banks have already tapped chief data officers in charge of enterprisewide programs.”  Their reason: the financial crisis and subsequent bankruptcy of Lehman Brothers showed their vulnerability in identifying market, credit and counterparty risk for many of the asset-backed and mortgage-backed debt instruments as well as the swap contracts they traded.

“Chief executives are realizing that the tsunami of regulatory requirements has increased the need for accuracy and transparency; therefore, policies and procedures for who manages data quality and how it is managed need to be developed” says Richard Lane, senior enterprise data management and technology project manager for buy-side technology consultancy InvestTech Systems Consulting based in Boston. Incorrect or stale information can lead to incorrect regulatory reports on holdings, valuations and risk metrics. Most of the attendees at the TSAM event,who spoke with FinOps, cited the European Market Infrastructure Regulation (EMIR), Markets in Financial Instruments Directive (MiFID), Alternative Investment Fund Managers Directive (AIFMD), and Solvency II as the most data-intensive measures that were keeping them awake at night.

Of course, regulators aren’t the only ones concerned about data. If they are given the wrong information it stands to reason that clients will as well. If the securities watchdogs don’t catch the mistakes, rest assured the investors will. Nothing will damage an asset manager’s reputation faster than a disgruntled customer.

Error Triggers Action

“One of our customers picked up a discrepancy in our reports which was ultimately traced back to an error in reference data,” one fund management operations manager attending the TSAM event tells FinOps. “We were then forced to do a top-to-bottom review of all of our data and discovered inconsistencies between business lines.” Ultimately, the asset management shop decided it was time for change and came up with a data governance program comprising six business lines. He declined to elaborate further.

Yet more reasons for a data governance plan: trading new financial instruments and staying competitive. “A good data governance program will make it far easier to come to market with new products, increase performance results, and even reduce operating costs related to fixing errors,” says Lamar, who was previously head of the statistics function at the Federal Reserve Bank of New York.

Although none of the panelists or attendees at the TSAM gathering were willing to divulge too much information about their data governance programs, the few high-level details they did share indicated some common ingredients. Data users from each business line will create the business glossary and data dictionary for each asset class. Each business line is represented on a steering committee which reports to a data governance director or chief data officer, who reports to the chief executive. Separate data stewards will ensure that the business users adhere to common data definitions, formats and identifiers so that the data is consistent and error-free. In some cases, the data stewards are technologists, while in others they are part of the same business units as the data users.

“The business users really know the data and understand contextually all of the various applications,” says McInnis.”They can also better articulate to senior executives just how important clean and consistent data is to their day-to-day functions, which makes it easier to garner the senior-level buy-in needed to make data management a priority.”

Of course, there will be times when various groups use different versions of the same data. “There could easily be differences depending on the function of the business unit or the particular region where the data is being used,” says McInnis. “If a quant team is deriving its own data for a specific application that data set should remain isolated to that specific group. However, standard reference and transactional data should be centralized and processed before it is distributed to the rest of the organization.” Why? If it isn’t, the firm will run into all of the inconsistency issues and inefficiencies that come with operating out of data silos.

Sanjay Bery, managing director of data governance and data stewardship at fund management firm TIAA in New York, confirms that his firm’s institutional business client services group has assigned dedicated data users and data stewarts within all of the business units it services. Those units are the customer contact center, middle office, and back- office operations for the firm’s institutional retirement businesses. “We determined a need for a formal data governance structure to support our customer experience and to continue to improve our operational efficiency,” says Bery, who joined TIAA last year. He would not specify which data sets were the areas of focus.

Bery’s data governance program is overseen by a steering committee which works with the fund management firm’s business data stewards, the organization’s chief data officer team, corporate data architecture and IT organizations. Their common goal: to discover and define data and develop and deploy data controls. Bery says that his firm is developing metrics and measures that will evaluate the program’s progress.

It is only a matter of time before asset managers catch up to their banking peers. “Data governance isn’t an IT project that happens overnight,” explains Bery. “Close collaboration, a long-term focus, key executive sponsorship and buy-in from the businesses are keys to success.”