Michael Shashoua: Simplicity and Security
The substance and emphasis of data management operations have evolved over the past few years. As the industry heads into the fourth quarter of the year, aiming for greater simplicity in data collection and production of a “golden copy” is at the top of executives’ minds, even as the data becomes ever more complex, and requires greater effort to keep it secure.
A host of data issues, including those relating to the golden copy, all come back to the data supply chain. Any consideration of whether federated or consolidated data models are better for a firm is affected by how data is sourced. Where it used to be possible to obtain golden copy data from a single, reliable source, now golden copy is typically an amalgamation of multiple sources.
So, again, the data supply chain must be considered. The term “data supply chain” itself is often thrown around casually, without clear definition, so it may mean one thing to a professional using it and another to the colleague who hears it. The term evokes ideas of several data suppliers being used, but that isn’t really it. As John Bottega, the ex-chief data officer who is now a senior advisor and consultant at the EDM Council, says, the steps in the data supply chain really are acquisition, process cleansing, maintenance, distribution and consumption.
In thinking about the data supply chain this way, the processing steps that data undergoes will affect how it can be handled in a federated or a consolidated fashion, as well as how multiple sources of data can be tied together and distributed accurately, most likely internally. Differences in data processing or sourcing from inconsistent supply chains can produce discrepancies right from the start, while putting flawed data into the cloud to feed big data resources can undermine such aspirational solutions.
Governance Considerations
Similarly, when considering data governance strategies, firms must consider how data is handled, whether by service providers or by centralizing data according to a data governance strategy. At Inside Reference Data’s European Financial Information Summit last month, Jacob Gertel, SIX Financial Information’s senior project manager for legal and compliance, considered the importance of data governance in the current regulatory climate.
Considering how to make data available to users becomes more complicated in the wake of new rules aimed at increasing security.
To comply with US Fatca tax withholding and reporting law and work with the Common Reporting Standard used for Fatca reporting, the data that financial intermediaries deliver must be based on data files from their customers, Gertel said. As a result, this data, with its relevance for regulatory compliance, has greater value than it might otherwise have. And, Gertel said, SIX seeks ways to make the data available to users without having to set up new management and distribution structures.
Complications
Considering how to make data available to users becomes more complicated in the wake of new rules aimed at increasing security, such as the European Union’s Cybersecurity Strategy and the European Commission’s Directive on Network and Information Security. Along with these, European regulators want firms to demonstrate that they have appropriate systems and structures in place for effective data protection.
The directives also require firms to know what their IT partners or vendors are doing about data security. “We all understand that there are speed-to-market and speed-of-compliance challenges from regulators, and global regulations are becoming increasingly strict,” explains Dan Crisp, managing director, EMEA, information risk management at BNY Mellon.
The data supply chain, especially its processing component, is a layer that underpins data governance strategies. When firms draft a governance plan to handle data, that must be done via data supply chains. Data format standards like the CRS for Fatca, and security directives for protecting data, like the EU Cybersecurity Strategy, will in turn shape these governance plans. No single part of data management changes by itself in a vacuum.
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