Acknowledge the risks
Data governance is commonly defined as the policy, framework, processes and controls which collectively describe and enforce the appropriate data management rules. The regulatory reform agenda is accelerating the need to rationalise data flows and improve the quality, consistency and timeliness of data. Identifying data synergies and synchronising planning across the organisation is vital if effective data management is to be achievable.
For many asset managers data governance is still the ‘elephant in the room’. Firms know they have data issues, know they are running risks rather than managing them, know they are exposed to losses and sanctions if things go wrong, and they know they should be doing something about it.
For a firm which doesn’t yet have a data governance strategy, the ‘data champions’ advocating the concept of a data governance framework within their firm have got their work cut out. There are multiple hurdles to overcome, not just with regards to data governance policies and data ownership, but the actual organisational structure and where it sits within a firm.
The Users of data tend to know what the weaknesses are within a particular dataset but are reticent to take ownership of the data, often because the data is used by multiple areas or they are too busy with their daily duties. The executive management may not be aware that there are any data or data governance issues within their firm as either the management information reporting regarding such data issues does not exist, or issues are sanitised or remedied as they are reported up the management chain by those not wanting to deliver bad news.
Scope the issues
If the management of a firm are aware that they have data governance issues then what next? Successful data governance programs rely on more than just processes and frameworks; they are also about changing culture, technology and people. The first practical task is for the executive management to set an initial budget sufficient to hire an individual or consultants to provide a ‘health-check’ on the state of the data and its governance within the firm. Following the health-check, consideration should then be given to developing a permanent structure and a systematic approach to data governance. Typically this will be in the form of a data governance council or steering group, with data stewards, data users, technology experts and executive management sponsors formulating policies and frameworks and monitoring project progress and budgets.
Once the initial resourcing tasks have been addressed, particular attention should be given to roles and responsibilities related to data quality. The status quo is unlikely to be ideal in most firms and now is the time to challenge all data ownership and usage whilst a fresh mandate from senior management exists.
The second phase it to categorise data at a high level and canvas the business functions on their ownership, usage and existing checks on the dataset. It is at this stage that you realise the enormity of the task and the volume of data and datasets. Examples of the high level data categories would be:
- Transaction Data (trades, counterparties)
- Asset Reference Data (credit ratings, FX rates)
- Benchmark Data (returns, constituents, hedged/unhedged)
- People Data (HR files, appraisals, payroll)
- Customer Data (CRM data, legal agreements)
This surveying of the data usage, quality and ownership within the business is crucial as that information once harvested, documented and assessed will form the evidence of where data is consumed and used, and how its quality is measured. By canvassing the business functions in this way you will also identify their priorities and data needs for the wider project. Couple this with the strategic goals of the firm, and from this baseline a roadmap to the data governance framework can be created.
This phase will be time consuming. Whilst some high-level results could be expected within three months (depending on the existence of data management teams and processes already in place), a year is a more realistic timeframe. Detailed research and analyses will be required and the right business partners time available to discuss each data category at length.
Data Governance tasks
The following provides a checklist of undertakings necessary to advance data governance from planning to practice within an organisation.
- Establish a policy which defines the firms approach to data quality management
- Write a directory of data attributes. This must state each elements source, characteristics, control and consumption
- Stipulate and scrutinise activities for the identification, administration, transmission, and storage of data
- Substantiate that data processing from source to end usage is transparent and verifiable
- Specify demonstrable metrics for accuracy, appropriateness and completeness of data
- Embed a process to manage data updates or vendor changes
- Complete data quality assessments, and create a process for detecting and correcting data errors
- Document data quality exceptions including their impact and remedial actions
- If expert judgment has been applied to any data, document the justification
- Facilitate internal and external auditors in examining data quality
Things can only get better…
There comes a turning point where the resources focussed on the data governance program switch from the labour intensive manual clean-up of data to delivering long-term sustainable benefits. A robust data governance framework allows management to be confident that they can identify data issues and correct errors in a timely manner. Yet more importantly, it allows the executive management to focus on making decisions as opposed to questioning the validity of the data in their metrics. Furthermore it allows a firm to manage its risks, rather than have the risks running the firm.
Whilst the return on investment may be difficult to quantify (how much was the fine you didn’t get from the regulator?), there are many constructive but less tangible benefits. Effective data governance programs encourage collaboration and communication between enterprise functions, they also unlock additional value and opportunities in the firm’s data. Developing a framework will require hard work, dedication and persuasion, but then again, as the author Samuel Johnson observed: “Nothing will ever be attempted if all possible objections must first be overcome.”
 Samuel Johnson – The History of Rasselas, Prince of Abissinia
The content provided in these articles is intended solely for general information purposes, and is provided with the understanding that the authors and publishers are not herein engaged in rendering regulatory or other professional advice or services. Consequently, any use of this information should be done only in consultation with qualified legal counsel. The information in these articles was posted with reasonable care and attention. However, it is possible that some information in these articles is incomplete, incorrect, or inapplicable to particular circumstances or conditions. We do not accept liability for direct or indirect losses resulting from using, relying or acting upon information in these articles.
- RIMES partners with AWS to offer its ETF data to the AWS Data Exchange’s millions of users
- Meeting the Ethical Obligations of Data Governance
- Constant Vigilance and Action are Crucial to Deliver on Diversity and Inclusion
- The ETF Market Calls for a Customized Approach to Data Management
- RIMES brings its ETF Data Management solution to Snowflake Data Marketplace amidst global ETF boom