Data Management


Date: July 2007
Author: John Robertshaw

The increased focus on operational risk exposure, stringent regulatory requirements and the growing need for management information have amplified the need for comprehensive control structures to ensure that data is managed to the right level of quality. Furthermore, external clients are becoming more sophisticated in their reporting requirements demanding ever-higher standards of data quality from their investment management firms. Inadequate approaches to managing data will expose a firm to data quality issues across the board.

This report examines the current level of maturity of data management within investment management firms. The focus has been from a business oriented viewpoint rather than a purely technical one. Our objective is to provide an informed and current insight into the ways in which investment management firms and their third party administrators (TPAs) are approaching the challenge of data management and the models that they are applying. We wish to continue to raise the profile of data management beyond the technology and operations departments into the executive domain.

In particular we have examined:

  • Industry and corporate level awareness of the importance of data management.
  • Governance and ownership models and the development of business responsibilities for data management.
  • Where the drivers are coming from, who is sponsoring data management and who is responsible for delivering initially the programme and subsequently the business as usual?
  • What organisational structures we see developing for managing data?
  • What high level architectural challenges are being faced and the whether the golden copy principle for a single trusted source of data is being applied?
  • What the investment management firms expect their TPAs to manage and what the TPAs believe they can manage.
  • Whether progress is being made in measuring the costs/benefits of data quality.


Table of contents

Management Summary   1
1.0 Introduction   5
1.1     Paper structure   6
1.2 Methodology   7
1.3 Glossary of terms   8
2.0 The Importance of Data Management   9
2.1 Data management - industry awareness   10
2.2 Data management - corporate awareness   12
2.3 Making the business case   13
2.4 Measuring success   15
3.0 A Framework for Data Management   17
3.1 The data management model   18
3.2 Organisation and governance of data   22
3.3 Principles and policies   23
3.4 Processes and procedures   24
3.5 Technology and systems   25
3.6 Data management roles   26
4.0 The Data Management Landscape in Practice   29
4.1 The profile of data management   30
4.2 Data management in practice   32
4.3 Who is responsible for data management?   34
4.4 What are the obstacles to data management?   37
4.5 Data management functional roles   38
4.6 Perspective from the United States   40
4.7 What do firms expect to achieve from their data management programmes?   41
5.0 Technology Challenges for Data Management   43
5.1 The quest for the 'golden copy'   44
5.2 Architectural challenges   46
5.3 Technical solutions – buy or build?   47
6.0 Investment Management Firms and TPAs - Data Responsibilities   49
6.1 What data should the TPA be responsible for?   50
6.2 Choice of data vendor   52
6.3 Partnering, cooperation and opportunities for rationalisation   53
7.0 Data Management Metrics   55
7.1 Measuring different types of data cost   56
7.2 External data services costs   57
7.3 Measuring data quality   59
8.0 Conclusions   61


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