No Absolutes About Value

Taking an overarching view of data governance plans means deciding what approach offers more value

michael-shashoua-waters

Value can mean a lot of things in the realm of data management and operations decisions. Data governance planning can generate value; data itself can have different levels of value; and service providers have their value propositions

I was reminded recently what a rarity it has become for financial services firms to design and build their own data management systems from scratch. So it is striking to see an experienced industry leader, Brian Buzzelli, in his new role with Acadian Asset Management, speak of keeping that approach in place at this firm. Service providers keep growing though, as is evident in our news story on page 5, about client data management software provider Fenergo reaping new backing because of its growth over the past three years.

Another item firms are working on, however, is building governance plans, as recalled in “Calling All Sponsors.” These, by necessity, cannot be farmed out so easily. Firms must understand all the components being put into place and devise a strategy for managing throughout their operations, says Cal Rosen, vice president of enterprise data governance and data quality at TD Bank. His colleague, Paul Childerhose, a data governance director at Scotiabank, says data governance planning shouldn’t only be done by choosing technology providers, but should begin with “planning the journey.”

One area in which firms’ aptitude appears to be maturing is handling evaluated prices, as recounted in “Assessing Evaluations.” Pricing providers, such as Thomson Reuters, are becoming aware of their users’ demands for consistency regardless of asset class, country or region. As Daniel Johnson, head of valuation at Wells Fargo Global Fund Services in London, observes in this story, user demand will drive providers to cover more asset classes, and to derive prices by comparing multiple suppliers’ information—not regulatory requirements about sourcing this data.

Of all the regulations and standards Inside Reference Data encounters, the one that is surprisingly new on our radar is AnaCredit, which is an initiative by the European Central Bank (ECB) to create an analytical credit risk data set that can support the bank’s research, as the body setting monetary policy for the European Union countries as a whole. With the stages for implementation of AnaCredit currently set for 2017, 2019 and 2020, it’s not yet reasonable to expect a lot of data to have flowed in from firms for this credit risk barometer. With the Corep and Finrep reporting guidelines already established, however, firms should not have to start from scratch for their AnaCredit submissions.

Turning to something we do hear about quite often, this month’s “Industry Warehouse” columnist, Robert Iati of Dun & Bradstreet, talks about “big data” in the context of how banks start data projects, warning of insufficient attention being paid to reference data. The increased amount of, and emphasis placed on, pricing information, could be making that more important than reference data information, Iati observes. Big data about customers, counterparties and products could be waning in value, if compared with generation of profits through trading.

The underlying concern in all the data management efforts and decisions reported in all of these stories is the value of data—whether it’s the value to be derived by handling data better (when planning governance), just how valuable the data is (because of the checks and balances of multiple sources, or what it means to a central bank), or the value that a service provider can offer to a user.

 

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