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Data Operating Model

Data - Operating Model

Data is a fundamental commodity for any investment management organisation. Managing data efficiently and effectively is one of the biggest challenges facing companies. Often each department has their own version of each type of data. The inefficiency of duplicated effort is not the only issue. Each department having different versions of the truth leads to numerous issues including poor regulatory reporting.

It is common for security data, benchmarks, corporate actions, portfolios and portfolio structures, products and client data to be managed multiple times throughout a company. Transitioning to an organisation where data is truly 'managed' is difficult and requires a number of common challenges to be overcome. A successful data-operating model is critical in today's investment industry.

ISC has helped numerous organisations move to an efficient and effective data-operating model.

What does bad look like?

A typical scenario is where different departments in different locations with multiple people update data at different times, applying different standards and processes.

Data operating model diagram

Multiple data management centres are likely to create inconsistent data with subsequent issues including additional effort for validation and the potential for poor decision-making. Each team's rules for data standards and data quality may diverge and there may be duplicate requests made for data from external data vendors.

For a cost-effective operation it is necessary to remove duplication and inefficiency in processes. Operational inefficiencies will hit the investment manager's bottom line.

Common Issues

A poor data-operating model results in various issues. The most common include:

  • Duplication of data and effort to maintain it
  • Updates being made at different times
  • Reporting using different values for the same data
  • Reconciliations issues
  • Additional operational efforts and costs
  • Stale and out-of-date data
  • Inaccurate validation
  • Trade breaks
  • Incorrect client reporting
  • Incorrect regulatory reporting
  • Risks assessed with inconsistent data
  • Incorrect compliance monitoring
  • Poor investment decisions
  • False performance reporting
  • Unnecessary market data costs
  • Inability to quickly respond to RFPs