When was the very first time you answered a survey about data management and whether it was the top priority for your firm that year?
Two years ago? Five years ago? Ten?
Data management and governance has been a long debated issue that is now recognized as a strategic asset for any type of firm.
But in their latest benchmarking study on data management, Cutter Associates found that although most participating investment managers are working toward establishing a formal data governance framework, only one third have achieved that goal.
In 2011, to the question does your firm recognize data as a strategic asset
providing competitive advantage, the unanimous answer was “no.” By 2013, that
number had climbed to 45%. Fast forward four years to 2015, and over two thirds
answer “yes” to the same question.
It is clear that investment managers have invested time and resources to raise awareness on the importance of data quality across the entire organisation. Everyone in an investment management firm relies on good quality, consistent data starting from the investment desk, risk management, and all the way to the performance measurement and client reporting. Once sourced, the data is used in analytic calculations that make the base for many investment decisions. The data underpinning these analytics is more complex and the decisions made are more impactful so those consuming the data must be able to trust it. Consistency across the organisation, from sourcing to calculation, is crucial.
But according to Cutter’s study, most firms still need to establish data management processes that directly support high-level business goals and fewer than half of the firms surveyed have managed to establish a formal Enterprise Data Management (EDM) program.
Among smaller firms, with AuM below US$ 100 bn, just 33% have an EDM program.
Where have things gone wrong?
From Cutter’s survey, it appears that the major roadblock is the expanding volume and variety of data required, as well as the increase in asset complexity, regulatory requirements, and demand for investment information from internal and external clients.
Regulation and the complexity of investments are putting a lot of pressure on organisations to solve the data management problem. On top of that, the costs associated with managing, governing and maintaining that data is skyrocketing, while internal budgets are getting smaller and smaller.
Among the participating investment managers, just 6% have enterprise data management as a standalone budgetary item. For 41% of participants, data management is not funded independently, but rather as a component of IT projects.
Many failed or late projects have data and budgetary issues as their main challenges. So before embarking on those hefty projects, consider whether data governance is really a good solution for data management? Is it going to help you solve for each data related pain point, such as lack of consistency, lack of unique identifiers, limited attribute coverage, versioning, control and the list goes on…
Let’s look at some explanations of governance: The Oxford dictionary talks about rules and control, Investopedia states practices and processes by which a company is directed and managed, Wikipedia even mentions security.
The success for achieving a good data governance really relies on collaboration, and should not be the responsibility of one team. It requires a firm-wide strategy, with rules and processes as defined above, one that includes a representative from each data domain (e.g. market data, client data, account data.) If you try to solve everyone’s problems at once, failure is almost certain. Start small, perhaps looking at reference data as a test drive. Take small steps and ensure quick wins by creating exception reports and managing these exceptions.
Then perhaps move on to client data and portfolio data. Think about the budgeting requirements too. According to Cutter, funding data management through IT makes it harder to get buy-in from the business side. Build your way around it with a real high-level business strategy in mind. Without a detailed, step-by-step plan, it will be hard to make any headway addressing those data challenges.