Data quality management

Data quality management for non financial institutions

If the reliability and quality of data is inconsistent it can be potentially misleading and faulty, resulting in sub-optimal decision-making.

Common weaknesses in data quality management include:

  • A lack of institutionalised data strategies and governance frameworks;
  • An absence of a vision for data management change;
  • A lack of performance targets or allocation of resources;
  • Data quality enhancement frameworks and policies that are compartmentalised and fail to reach across the functions or the various levels within an enterprise.

Reply typically work on data quality management at two levels – as part of an enterprise effort to support enhanced risk adjusted decision making, and, in support of enhanced risk modeling.

Risk Reporting in Support of Risk Adjusted Decision Taking

Reply services in this respect focus on identifying and delivering data in support of the risk metrics with which operations are monitored and reported. Reply adopt a business focus to assess priorities and adopt a cost/benefit approach to sourcing supporting data from internal and external sources.

Risk Modeling

Much of the data needed for building and testing risk models is held in multiple (and often legacy) systems and in different formats. In addition, there is a need for an auditable trail (in an area where manual intervention in preparing information remains prevalent), a tighter regulatory environment (regarding the use and retention of data) and security considerations (including data protection, contingency and recovery). It is easy to understand why data quality management is now a core organisational requirement and a source of competitive advantage. Chief Financial Officers must understand the origin of the data upon which they rely, which means taking an active role in defining the processes from which data is derived.

Reply can help you improve data quality. Reply offerings, which are supported by a proprietary methodology, include:

  • Establishing a data management governance structure by defining roles and responsibilities, and developing and implementing data quality management strategy;
  • Developing, documenting and rolling-out data quality policies and standards;
  • Designing a common data architecture across the enterprise, developing and promoting data quality awareness and communication plans, and defining data quality requirements and business rules (for data transformation);
  • Designing, implementing and monitoring operational data quality management procedures, testing and validating data quality requirements;
  • Analysing, profiling, measuring and monitoring data quality, setting data quality service levels, certifying and auditing data quality, identifying, escalating and resolving data quality issues;
  • Planning and conducting data cleansing/clean up programmes.



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