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| Version 8 of EDM Suite Released Tailored Solution for Managing Risk |
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| Friday, 03 October 2008 | ||
| Reference Data Review | ||
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In the current economic environment, the focus of most current data management projects is understandably on enabling and protecting business practices, but this necessarily entails understanding data workflow and business stakeholders’ data requirements. And it is precisely these downstream data impacts that are being used as a barometer of success for data management projects, according to recent research by A-Team Group and commissioned by EDM vendor GoldenSource. The white paper, entitled "Enterprise Data Management Process Crosses Boundaries", examines the evolution of data management workflow and the practical aspects of firm-wide data models. The current climate is demanding that financial institutions jointly enable and protect business practices through streamlined data management processes, the report contends. "Astute awareness that business has to come first is guiding technology decisions. Extreme volatility and heightened risk underscores business needs," explains Maryann Houglet, senior vice president of strategic consulting at A-Team Group and author of the report. "Meeting the needs of the business stakeholders has become both a barometer for success and a catalyst for change in data management workflow." Firms need an understanding of what business stakeholders need to know and how they will use the information. However, this is not an easy task, especially when the very definition of 'workflow' in a data management context is unclear. According to A-Team Group, the industry tends to fall into two distinct camps when defining data management workflow: one sees it as exception handling, while the other views it as a holistic process. "Data managers and IT support that focus on implementing central reference data solutions and warehouses defined data workflow as handling data exceptions," says Houglet. "Whereas data technologists and data support within business areas look at workflow management as a holistic process and a necessary backbone for EDM automation." Nevertheless, she reckons the importance of linking counterparty data with instrument and reference data could have the power to bring these two camps closer in the future. The key concept behind better meeting downstream requirements is cooperation and harmonisation across business functions. "Data management reengineering initiatives in financial services firms are now turning to firm-wide data models as projects span boundaries. Integrated workflow orchestration tools will help take these projects to the next level of automation," explains Houglet. The fundamental challenge for these projects is that stakeholders have unique, ingrained data practices and frequently speak different languages, the report stresses. Dealing with the data management problem involves a lot more than just putting data on a server and requires the engagement of all parties, it continues. As one senior management engaged in data management projects tells A-Team, many institutions have set up steering committees to get everyone "talking the same language and buying into the process". The power of the data model is paramount in this endeavour, says the report. To simplify complexity, the industry is turning to data models that reflect centralised standards on data and are governed by a central group. Data models, in particular those that are used firm-wide, act as catalysts in advancing automation and ensuring enterprise consistency. Data managers are realising the power that data models offer both in cross data platforms and in reflecting enterprise standards, the report states. "Automation, data centralisation, stringent risk controls, plus the need to connect disparate data sets are forcing financial services firms to attack enterprise data management from all angles," adds Houglet. Accordingly, A-Team is hearing that there are six clear steps to achieving EDM goals, she explains. This includes leveraging cross-functional steering committees, identifying data workflow connections and incorporating relationships into a firm-wide data model. It also involves selecting a flexible, scalable data management infrastructure solution and establishing firm-wide data management governance and standards, says Houglet. Firms must conduct reviews to leverage data management workflow across the firm and implement management reports, enabled by workflow tools.
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