From our vantage point, the industry is awash with data. Broadridge processes more than US$5 trillion in fixed income and equity trades per day, serve 6 of the top 10 global banks, every mutual fund company, and provide investment servicing for 70% of the North American market. That’s a LOT of data. We spend a lot of time and effort around data management, and we maintain a great dialog with our clients — and the capital markets industry as a whole — about managing, optimizing, and capitalizing on their data. What’s surprising though is that firms still need to leverage the potential of all this data.
According to new CEB research, almost 70% of capital markets firms either don’t have a data management strategy, or haven’t invested in one. That’s a staggering and unacceptable figure for an industry so dependent on data quality. It’s not that executives don’t understand the importance of data. Most do, but they just can’t agree on what their strategy should be. Sixty-three percent of surveyed firms have only partial alignment between IT and business lines on data management strategy. As CEB’s research points out, misalignment has a documented 20% negative impact on the insights that investment staff can draw from data. It also causes firms to react to data, instead of planning for it.
Leading firms recognize the pitfalls of a reactive approach to data management, and are right now building internal consensus and agreement. To move strategy beyond the item or technology level, CEB recommends grounding strategy efforts in four target states for data management:
A common component of these target states is the need for a cohesive, firm-wide approach. Over time, many product lines have sprouted cottage industry functions to manage data and analytics. It is not only inadequate from an efficiency standpoint – it also leads to inconsistent and low-quality data. This holds true at the industry level. Managed services for data and processes can mutualize costs across firms, lead to more consistent data, and improve the scalability of the data process model.
Filter Insights by: