The starting block
Asset managers have some way to go in unlocking their full data potential. Data errors and inconsistencies lead to a lack of trust and reliance. This can lead to a fragmentation in their operating model and increased operational risk, for example from fund managers maintaining their positions in their own spreadsheets rather than relying on the authoritative source. Equally, a poorly documented data architecture breeds inappropriate data usage, further exacerbating data issues.
Firms agree that the adoption of a data governance framework is key in order to improve effectiveness through improved data quality, be operationally nimble and lower costs. Unfortunately, implementation is not as easy as it sounds.
A good data governance framework should encompass the following themes:
- accountability and ownership
- integrity and usage
- retention and destruction
- availability
- security
- legal and compliance
This should be underpinned by appropriate measures to facilitate transparent communication and monitoring and to provide a benchmark against which to demonstrate improvement.
One of our recent surveys found that 71% of firms have put in place a formal data governance framework. Of those that haven’t, most are in the process of developing one. But even among the adopting majority, only 6% believe their framework is effective, with 30% doubtful of effectiveness or not yet able to prove it.
The hurdles
Our experience shows that there are two main hurdles in establishing a framework:
- length of deployment, with many teams quoting three to four years to achieve partial implementation; and
- lack of engagement and adoption across the organisation. This can be due to an absence of support from senior management, who lack awareness of the underlying problems and do not realise that operations teams are merely putting plasters on the wounds.
Another issue is a lack of accountability and ownership, alongside insufficient understanding of perceived benefits. Questions persist as to what data should be included in this framework – market data, risk data, other data?
All too often, a data governance framework is the by-product of a large IT project, although some firms can see value in starting from scratch. Either way, the challenge arises in moving such a project into a BAU environment: in particular, who ensures that data governance is sponsored? The view differs among firms, with responsibilities assigned to a variety of roles and teams, ranging from newly appointed Chief Data Officers to existing Operations and Data Management functions. Asset managers also get value from involving Compliance and Operational Risk to prioritise and provide assurance that the framework is adopted.
The finishing line
Asset managers who get close to implementing a successful data governance framework do not underestimate the need to appreciate and formalise the associated benefits, including:
- raising the seniority of the data management role to be on a par with the CTO will in turn help raise the profile of the framework;
- measuring data quality and demonstrating improvements over time is a significant ‘win’ in framework adoption;
- finding ways to automate these measures to showcase business value will facilitate adherence; and
- the use of third-party tools such as Collibra or Solidatus which can help embed and structure data governance while providing the documentation and cataloguing capabilities that underpin the framework.
In our opinion, there is no big-bang approach to data governance, only a long-term effort of organisational discipline. This is more a marathon than sprint – but for those that have persisted, the benefit is being realised.
