Asset management

Data quality management for asset management

Data management is a core organisational requirement and, more importantly, a source of competitive leverage. If the reliability and quality of data (e.g. static data such as the securities master file or operational risk-related data) is inconsistent it can be potentially misleading and faulty, resulting in harmful conclusions.

Problems in data management often arise from poor definition of roles and responsibilities, and lack of senior management and/or steering committee sponsorship and buy in. Although data quality is not purely an IT issue, it also relies heavily on strong partnerships between business and technology functions.

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 developed in silos and not spanning across the functions or the various levels within an organisation.

Reply  works with organisations to 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 organisation, 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 programmes.

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