What’s the problem with Investment Data Governance?

Over the past 5 years we have been fortunate enough to help many of APAC’s leading asset owner’s design and implement an investment data strategy with the express intention to maximising the value of investment data to deliver improved investment outcomes and improved operational efficiency.

Whilst the case for improving a firm’s capability for investment data management is clear, the question often posed is what is the ‘right’ level of investment data governance to put in place so that it contributes to the value derived from investment data without imposing unnecessary bureaucracy, rigidity, and control.

The reason for this question is that an investment organisations experience with data governance has often been a mixed one.  In some cases, lack of appropriate governance has meant that there has been lack of accountability over the ownership of data, fragmented and inconsistent decision making and a general lack of trust in the data.  Furthermore, the absence of meta data tools such as data catalogues has prevented the awareness on the availability of investment data in the organisation, resulting in teams fending for themselves in the sourcing and management of useful data sets.

At the other end of the spectrum, some firms may have initiated an extensive program to create an investment data governance model, complete with policies, stewards, forums, and all the ‘ceremonies’ associated with best practice data governance. Often this has simply imposed additional controls and overhead over the management of data with no real demonstrable benefit to the end user, with the outcome that they simply ‘bypass’ the governance model in favour of getting to the investment data they need.

Part of the problem may be how data governance is defined. Let’s take the definition from DAMA (Global association of data management practitioners).

“Data governance is defined as the exercise of authority and control (planning, monitoring and enforcement) over the management of data assets with the Fund.”

This traditional definition treats data as an asset that needs to be controlled, restricted and tightly managed and, if this is used as the basis for determining the data governance model, may be a long way to explain how this traditional approach fails to result in an effective model to unleash the value of investment data to a Firm.

When helping Firms deploy an investment data governance model, we favour an alternate definition that promotes maximising the value of data. The definition we have developed is:

“Data governance is defined as the deployment of accountabilities, processes and tools that maximise the value of data by enabling trust and ease of access.”

By adopting this definition, we recognise that the purpose of governance is simply two fold. Firstly, the users can trust the data to be ‘correct’ for the purpose they intend to use it for. Secondly, they have ‘ease of access’ by knowing what data is available and have the tools, methods, and support to access it.

Using this definition, we believe that an effective investment data governance model should focus on:

  1. Defining quality in terms of the intended purpose of the data – Move away from binary definitions of ‘certified’ and ‘uncertified’ investment data and adopt a more tailored quality framework based against the use cases of investment data and the resultant requirements on data quality.
  2. Ensuring there is complete and transparent data lineage – As data management models become more complex, it is important to understand the relationships between components of investment data and what transformations have been performed. Investing in a capability that controls and describes this is fundamental to maintain trust in investment data.
  3. Promoting availability and access to data – Investing in tools such as data catalogues will ensure there is a complete and consistent understanding on what data is available and its key attributes. It will also go a long way to ensure that data is used for its correct purpose.
  4. Ensuring clear ownership of data and applications – Ownership ensures users know who will be responsible for data decisions, support and overall management of data and enables streamlined and consistent decision making.
  5. Standardising investment language throughout – Inconsistent language leads to inefficient and fragmented data management process and compromises trust. Glossaries, data models, information models help bridge this gap and bring everyone to the same knowledge platform.

Finally, it is important to remember that investment data governance is more than establishing policies and forums. To be effective, the full operating model implications should be considered, and we cannot understate the importance of utilising appropriate data governance technology platforms to support activities and reporting.

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