Viewpoint | Data management | Stephen Engdahl
II requires much more granular data, in a broader set of attributes, than was needed in the past – touching on all aspects of data from position to instrument to counterparty. The strategic reason for a firm to invest in
data management capabilities should always be to improve quality and improve its own decision making and in turn its own investment performance. However, regulation is providing an additional kick to get things right, today.
Making order out of chaos Prompted both by regulatory requirements and market events, risk managers, portfolio managers, and other consumers of information within the firm are now asking more questions about the lineage of the data they use. In addition to demanding better data quality, the business is demanding a view into the data supply chain. This is particularly important in areas such as valuations, where the business needs to show why a particular valuation was applied to a thinly traded instrument, and what other sources and models for valuation were provided. Transparency requires data management operations to run on systems with full audit trails, entitlements control, and easy access points for end users, rather than spreadsheets. It also requires the data lifecycle within an organisation (and the governance processes which surround and support it) to be clearly defined and documented. Every set of data attributes has a data steward somewhere within the organisation who knows the most about how that data behaves, and the business needs a simple way of finding that person when it has questions about the attributes.
The path forward The risk discipline is facing new scrutiny as firms steel themselves for the present and ready themselves to compete in the future. And integral to good risk management is sound data management policy, practices, and systems. Given the current state of data management in many firms, it may seem like a daunting task to get to the end state of clean, consolidated, complete data to drive effective risk analysis.
Best Execution | Summer 2013 Luckily, that does not have to be the case.
Leading institutions that have made strides in this area share the following three characteristics:
Define success criteria around sets of data consumers Data management initiatives achieve their best and quickest results when they focus on delivering value to a particular region, a department, an application, or a business function. This ensures that each project phase provides ROI, and sets the stage to iteratively increase ROI in subsequent phases. It’s important to have a solid end-state vision, but equally important to achieve it in incremental steps.
Don’t forget the data generated within the firm Data governance and quality processes apply to all data assets which need to be aggregated and leveraged across the enterprise. Many data projects only consider the security master data, which is procured from external vendors. However, that is only part of the picture. A solid end-state vision must address counterparties, accounts, and associated hierarchies. It must also include positions and transactions. The quality and standardisation of such information is critically important to risk, regulation, and gaining a 360-degree view of the business.
Be selfish – identify strategic benefits from regulatory projects Regulation is often viewed as something to do “because we have to.” But data aggregation, standardisation, quality management, and transparency required by many regulatory initiatives can also be used for competitive gain. Leading institutions derive strategic gains from their regulatory initiatives, such as finding ways to get to know their customers better, operate more efficiently, create new financial products more quickly, and improve their investment performance. Data management has reached mass-
adoption. Now, given market uncertainty, regulatory requirements, and the drive for business transparency, it’s a perfect time to create your own data management roadmap. n
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