Data Management Best Practice

RIMES is committed to the development of a best practice methodology for the management of index data and benchmarks. We chair regular forums in major global financial centres, where industry experts can discuss issues and challenges surrounding index data and benchmark management. As part of this outreach programme, RIMES also commissioned independent research to quantify these challenges and develop practical methods that can benefit the entire industry.

This is an abstract of a recent white paper by Investit (, which defines a best practice framework for index data and benchmarks. Some of the findings are surprising: business complexity does not seem to be a factor in determining the level of maturity of the benchmark data management capabilities of investment management firms. It also seems that many firms can do more to improve their data governance, tighten control and reduce data management costs.

The management of index data and benchmarks is characteristically different from other data types. Although critical to investment management and other key buy-side functions, benchmarks are growing in volume and complexity and there is no objective mechanism for assessing the effectiveness of data management processes. Consequently there is no industry best practice methodology for benchmark data management. This means that the costs of managing index data and benchmarks are:

  • Consistently much higher than those for managing other data
  • Usually a multiple of the partner licence costs and continually increasing
  • Often hidden and only begin to accrue once the data is on board.

Defining best practice

In 2011 RIMES commissioned Investit to conduct a survey called The Management Of Index Data And Benchmarks In Investment Management Firms. The survey, which consulted 34 major firms, identified the need for investment managers to have an objective mechanism for assessing the maturity of their benchmark data management processes.

RIMES subsequently commissioned Investit to define a best practice framework for benchmark data management. The framework seeks to offer a systematic approach to data management without compromising flexibility or business agility.

The Investit framework measures the efficiency of benchmark management on two dimensions – data governance and data processing.

  • Strong data governance keeps costs down by ensuring that data is only purchased and processed when a firm actually needs that data
  • Strong data processing ensures that the correct data is always delivered in the required format and at the required time.

The model considers 24 individual maturity factors that determine where an investment manager lies on the best practice spectrum. This allows improvements to be made and progress monitored.

A practical approach

The Investit framework has been applied in a study of 18 investment management firms in the US, the UK and Europe. Each of the managers were positioned in the maturity space for index data and benchmark management. The study revealed several interesting findings:

  • Business complexity in not strongly correlated with the position of managers in the maturity space
  • 33% of managers have strong data governance compared with 67% for whom it is weak
  • 72% have strong data processing compared with 28% for whom it is weak.

Investment managers are therefore more likely to have strong data processing than strong data governance. In fact, just under half of the managers who participated in the study showed strong data processing and weak data governance. This demonstrates that market practice for benchmark data management is not aligned with best practice. Most investment managers have focused more attention on developing strong data processing, rather than data governance.

Conclusion – scope for improvement

The Investit model suggests that investment managers have invested successfully in IT systems to support ‘centralised’ activities associated with benchmark data management. They have been less successful at providing effective IT support for ‘distributed’ activities. The distributed nature of the upstream activities arises from the fact that there are multiple sources of index data and benchmarks within most investment management firms. This is increasing costs and producing opportunity costs in terms of missed business opportunities.

Investment managers must focus on data governance if they are to align themselves more closely with best practice. And within data governance, managers are strongest in the area of procurement and weakest in usage management. In practice this means that most managers are proficient at controlling the purchase of new index data and benchmarks, they are exposed to the risk that they are processing data that is no longer required by the business.

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