Data governance: when will you be ready?
Trusted, quality data is the DNA for unlocking the potential of financial organisations
Data governance has been around since the last millennia. Yet change management specialists and industry experts find limited evidence of wholesale success in data governance adoption within investment management and financial services organisations.
Before the onset of increased regulatory oversight and transparency after the fallout from the financial crisis in 2007 / 2008, reliable data was considered the Holy Grail for COOs and CIOs in investment management. Thus far, even organisations who are considered mature in their approach to data governance acknowledge that their governance practices could be better.
This article explores the types of issues that can hold organisations back from implementing a successful data governance model, why initiatives can fail, and the criteria for maximising success.
Beyond data control
The value of business insights from a well-structured data governance model is well-documented; from increasing revenue and value, managing cost, risk and compliance to ensuring the business reaps the benefits from trusted, quality data. Yet many misconceptions abound on what data governance means.
Data governance goes beyond controlling data in a central data store, defining ownership and meta data. A complete data governance model ensures that data in an organisation can be trusted, is accurate and brings value to the business.
|‘Data governance is the overall management of the availability, usability, integrity and security of data across an organisation. It determines who can take what actions, with what information, when, under what circumstances and using which methods.’|
5 steps to unblocking the path for data governance
So what organisational traits and characteristics can enable effective data governance?
1. Recognising data as a corporate asset
Introducing a data governance programme in an organisation can present a challenge if senior stakeholders don’t recognise data as a priority. Where data quality is not deemed a risk to business success, it is often not monitored. Along with the cost of managing data, governance represents an unknown quantity. Where data issues do get raised at C-level, a common rebuttal is ‘do we even have a problem?’ Any initiative to address poor data quality or management can be considered a threat and adoption inhibited. Yet this perceived threat can be mitigated by evangelism from the top down.
2. Establishing ownership and accountability
A common issue with data governance initiatives is the reluctance of C-level executives to lead through sponsorship and promotion. This may be due to misconceptions about risk, or that data is not regarded as warranting time and resource. A disregard for data risk can also be reflected across different functions within an organisation, where lines of ownership and accountability are not clear. It’s not uncommon for the business to attribute data responsibility to Information Technology (IT) teams or investment operations, whereas IT and operations may attribute poor data quality to end-users and poor system usage.
Denial that data quality is an issue can permeate all levels of an organisation
Data is the Chief Data Officer’s problem.
We have a data governance practice; don’t they take care of it?
We’re judged on our investment performance not on how good our data is.
Isn’t the Client Reporting team responsible for client data?
Acknowledging the presence of poor and unreliable data can be blocked by fear of reprisal.
Where senior management do acknowledge the risks posed to their organisation by unreliable data, a few obstacles can remain.
3. Defining a business case
Introducing a data governance programme is no mean feat. Conflicting priorities may exist or other initiatives that appear easier to quantify and realise ROI. Often, a lack of ambition can unsettle a governance initiative due to the size of the task; and deciding where to start can prove contentious. Difficulties in substantiating a business case can result either in no budget being made available or, where funds are allocated, these may be woefully inadequate.
To ensure success, there needs to be a shared vision and strategy for data at senior level and across disciplines, as well as agreement over what good data and governance infrastructure means.
4. A cohesive approach across all business lines
Modern investment management organisations are often vast and multifaceted. Requirements for data may conflict across different business lines, front- and back-office, and different regions. Business lines may operate in silos or be unable or unwilling politically to collaborate on cross-organisational initiatives. Functional and regional silos can fuel resistance to a data governance programme, whether centralised or dispersed. Without a cohesive approach, division can arise that can become a barrier to effective implementation.
Functional silos can act as barriers to implementing a cohesive data governance model
5. Establishing a culture for change
Data governance is an evolutionary process rather than a one-time effort. A lack of standardised, mature change process model can represent a significant obstacle when it comes to implementing data governance. The scale and uniqueness of a data governance initiative means that standardised project management methodologies need to be adapted to facilitate implementation. An organisation’s culture must embrace change to make headway.
How to assess readiness for data governance
Before embarking on a data governance initiative, time needs to be spent in identifying, understanding and addressing any inhibiting factors. While this can be an uncomfortable process, this is vital to determine the most appropriate data governance model. Here are some fundamental considerations.
If you can answer a definitive yes to these questions, you’re on the road to embarking on a data governance programme. Even if you’re not ready, exploring and addressing these factors is the first step to embarking on a data governance initiative.
How ISC can help
ISC provides data management consultancy services to the investment management community. We are experienced in implementing data governance initiatives and can help assess, plan, advise and implement data governance.
Investment Solutions Consultants (ISC) provides trusted advice and expertise to the investment management community. ISC's goal is to provide practical solutions to the challenges facing investment managers. Our consultants understand the end-to-end operating model and vendor landscape that supports the industry. With a strong blue-chip client base, ISC helps its clients to maximise the efficiency and effectiveness of their operations and technologies.