What does it take to deliver Data Governance excellence? This was one of the central themes discussed during our last Signals forum for senior asset management and wealth management executives.
A best practice data governance model is made up of three organisational functions:
- Data governance oversight – develops the data strategy, policies, and standards; co-ordinates all data governance activities and provides guidance, tools and training. This is typically led by a chief data officer or head of data.
- Data forum or council – accountable for the organisation’s data management and governance. This body sets the strategic direction and priorities, reviews progress and resourcing requirements, and resolves or escalates issues. Membership typically comprises senior functional executives across the organisation.
- Data domain leadership – owns and manages the data for each domain; defines data quality standards; understands and meets the needs of data consumers, and ensures compliance with organisation policies, standards, regulations, and licensing requirements.
“77% of our participants believed that the above federated organisational model was the right structure for governing data; 6% felt that a centralised single function should be responsible for all aspects of data governance, while a further 6% felt that the domain owners should be exclusively responsible for data governance.”
Davies consider the federated organisational model as foundational to drive data governance. However, on its own, this is not sufficient to deliver excellence.
Several additional factors should be addressed to ensure data governance delivers value and meets the needs of the organisation:
- Ensure executive buy in – failure typically results from the absence of strong executive sponsorship. Without visible support from senior leadership, data governance initiatives often lack the necessary resources, authority, and direction to succeed. Metrics to track progress are also essential to ensure attention and continued support of senior management.
- Develop a pragmatic and business centric framework – a clear and comprehensive governance framework should be established with policy and procedure development done in close communication with implementing teams and integrated with daily operational procedures. Governance requirements are not always understood and can alienate data workers, especially when accompanied by cumbersome procedures, with staff having to perform extra tasks with no discernible value add.
- Prioritise data requirements – data assets should be prioritised by domains and by sub-domains and attributes within each domain. Critical data normally comprises up to 20% of all data, and the focus should initially be on these priority items only. Within these, deployment should be further prioritised, focusing on those that result in maximum benefit and support existing use cases. This narrows the scope of governance efforts and ensures focus on the most important data.
- Apply the ‘right level’ of governance – employ an approach based on the requirements of the organisation, adopting a level of sophistication appropriate to the organisation.
- Implement iteratively and incrementally – governance priorities should be adapted to the domain, and an iterative process should be employed, adjusting quickly based on the experience obtained. Priorities should be further adjusted, focussing on any backlog of known data quality issues, and reprioritised regularly to maximise benefit to the business. Priority use cases should be supported quickly, even if the solution is not perfect.
- Ensure consistent focus across business, technology and data change programmes – linking governance to existing transformation efforts will help senior executive buy in and ensure data governance is embedded in the culture and your developing ecosystem.
- Create a “Data as an Asset” Culture – instil an organisational ethos where data is accorded the status of a key asset. This cultural transformation will help motivate staff but can be the most challenging aspect of the programme and may require a combination of interventions to drive the right behaviours. As part of this, establish a continuous feedback mechanism and use the feedback to guide changes in policy and direction; acknowledge successful implementations at team and individual levels to help drive greater adoption and effectiveness.
In my next article, I will explore another critical data question: “Can new technology and vendor solutions help propel data solutions?”
How can Davies Consulting help?
Davies is a global advisory and delivery firm with deep expertise within the investment management industry and has extensive experience in supporting all aspects of your journey to become a data driven organisation. Davies is SME led and focuses on delivering pragmatic solutions to add value rapidly. Please get in touch if you would like to have a conversation to find out how Davies can help you.
