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10 data integration best practices for risk management

Waynette Tubbs, EditorToday, financial services organizations are swamped in data because of regulatory requirements, years of rapid growth, mergers and acquisitions, and Internet-accessible data. This flood has many firms struggling with disparate sources and varying degrees of data quality. There are several reasons your organization might choose to integrate its data, including, business analytics, regulatory compliance and risk management.

For risk managers and chief risk officers, the first step in developing a risk strategy should be to analyze the enterprise data management. Joyce Norris-Montanari writes that companies began using database management systems instead of flat file systems as early as the 1960s. According to Norris-Montanari, this integration is beneficial because your organization can access all of its data from one source using one platform with fewer security issues. What a great idea.

Data integration provides accurate, consistent information to decision makers, analysts and regulators. It enables collaboration and easier reusability across your organization – all with a single point of IT administration. Here are some data integration best practices from TDWI:

  1. When done well, regardless of the data integration (DI) technique used – ETL (extract, transform, and load), data federation, database replication, data synchronization, sorting, and changed data capture – DI adds value to your data by improving its content or creating data structures that wouldn’t exist without DI.
  2. Data integration techniques support a variety of business initiatives and technology implementations including analytic data, operational data and cross-functional data integration (e.g., master data management, customer data integration and product information management).
  3. When defining DI, stay focused on the value proposition seen in transforming and repurposing data; avoid definitions that stress the secondary access, copy and transfer of data.
  4. Traditionally, DI was staffed and managed by larger, related data management teams. Today, many organizations are streamlining with independent teams of DI specialists who perform a wide range of DI work.
  5. Data integration specialists should always raise the bar by looking for ways to add further value to data as they integrate and repurpose it.
  6. Data integration can make data management practices more sustainable by consolidating and collocating redundant databases.
  7. Adopt hub-and-spoke architecture for most DI implementations. The hub reduces the number of interfaces and provides a pattern everyone can understand and be productive with. (Note: Ensure the work is distributed beyond the hub.)
  8. Although an autonomous practice, DI should be a product of collaboration. TDWI Research defines collaborative data integrationas a collection of user best practices and software tool functions that foster collaboration among the growing number of technical and business people involved in DI projects and initiatives.
  9. Adopt enterprise data management (EDM), so that all data management work is aligned to support strategic, data-driven business objectives, including fully informed operational excellence and BI, plus related goals in governance and compliance. (Read Best Practices in Data Management.)
  10. Data governance influences all aspects of data integration and can be extremely restrictive or more open to allow access to data in support of project types that are analytic (feeding a data warehouse), operational (consolidating database instances) or cross-business (sharing data with partners).

David Loshin’s post about duplicate records gives a simple explanation about the need for governance and briefly describes how easy it is to let your data get out of control. It is a risky venture, to be sure.

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