Data transformation is one of the most vital facets of data management. Prior to integrating data sources, conducting analytics, or utilizing data in most operational applications, data must be transformed from its native state to one suitable for the target system—even if it’s just a data mart.
We are at an inflection point in the financial services industry. The evolving and overwhelming demands of regulatory compliance have forced organizations to acknowledge the need for data governance and most are developing their strategy.
Regardless of the ROI of any data-centered solution, upper level management will not support it unless it adheres to governance and security conventions. By definition, data governance formalizes the roles, responsibilities, and rules required for data’s long-term sustainability. Its symbiotic relationship with security ensures that data is protected from the people and practices that negatively affect organizations.
Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.
Many data lake projects achieve their IT objective: cheap storage of all enterprise data in raw form, but fail in their business objective to deliver value from this data. Why? Because making the data accessible and usable for business users is hard.
Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also contain very valuable data that needs to be retained for business or compliance purposes.
Ernst and Young just released their 2015 Federal Reserve regulatory reporting survey looking at how firms are adapting to the new standards. The survey covered 5 key topics but two stood out as areas where new smart data solutions could add immediate value for the banks: Report Preparation and Data &Technology.
Data integration projects can be time consuming, expensive and difficult to manage.Traditional data integration methods require point to point mapping of source and target systems. This effort typically requires a team of both business SMEs and technology professionals. These mappings are time consuming to create and code and errors in the ETL (Extract, Transform, and Load) process require iterative cycles through the process.
Large organizations typically require hundreds of integrations between disparate legacy systems to meet enterprise business requirements. Even with a SOA approach, this complex web of point-to-point integrations can be difficult to govern and manage. The result is a proliferation of overlapping services, poor documentation, limited reuse and a challenging support environment.