Opening Cross: The Convergent Series: Data Science Friction

The separation that took place over the last decade was not without good cause: until just over a decade ago, reference data was largely managed within a firm’s market data function, and didn’t get the recognition and support from senior management that it deserved. Poor reference data was a major cause of failed trades—a cost that has been eliminated to a large degree as a result of vendors like Markit creating products such as the Reference Entity Database, and firms getting their houses in order, creating internal “golden copies” of securities master data, and critically, translating those failed trades into a P&L argument that alarmed senior managers enough to set aside separate budgets for reference data projects, processes and staff. 

However, the challenges associated with the sheer volumes and complexity of data now being captured, processed and monitored by financial firms mean that data is a much bigger challenge than in previous years, and therefore accounts for a larger share of budget, has inherent in it higher levels of risk, and has higher potential for monetizing it—by which people typically mean getting more value out of the data, rather than finding ways to productize and sell it. To address the reality that trading firms increasingly have more in common with data processing firms, banks and asset managers alike are appointing chief data officers to oversee all aspects of a firm’s data management—from market data to reference data, from data held in internal documents to confidential client data. To perform these roles successfully, CDOs must work hand-in-hand with other departments, from operations to trading functions. And, of course, they have direct oversight of the most data-intensive areas of all—their market data and reference data departments.

As a result, market data and reference data functions are—if not unifying into a single unit—converging under a common structure. This became more evident than ever at last week’s European Financial Information Summit in London, hosted by Inside Market Data and stablemate Inside Reference Data, where market data professionals were as concerned about provenance as about prices, and about LEIs as much as latency, and where reference data experts were as concerned about real-time changes to information as they were about traditional static data.

Aside from the commercial opportunities created by this new data frontier, another reason for this change is the increasingly strict regulatory environment. This is not to say that firms believe bringing these groups closer will directly save money, but rather that by creating closer ties between all of its data assets—and the people, systems and groups that govern them, they will be better placed to obtain a single and more accurate view of their data, and will also therefore be better placed to respond quickly and accurately to regulatory reporting demands from regulators, minimizing both fines and the cost of providing this function.

Another benefit is being able to respond more quickly to IT migrations or adopting new technologies, such as machine learning. When building its Synapse data management platform, consultancy Sapient Global Markets surveyed senior IT execs, and found that data issues account for a large percentage of challenges relating to IT change projects. While tools like Synapse are designed to provide the kind of governance layer that helps mitigate these challenges, another way to achieve this is to align all of a firm’s data management functions more closely together to deliver not just better data management and higher data quality, but also faster time to market.

How are we at Inside Market Data responding to this trend towards convergence? Watch this space.