Financial Sector Should Commercialize Data Management Knowledge to Advance Other Industries

Data management within the capital markets wasn’t always an established and respected practice. It took many years of evangelism for data professionals to make their case for acceptance and a seat at the table, and to convince management of the importance of investment in data quality, and the sector has not one but two industry associations—FISD and the EDM Council—representing it.

Other industries outside finance are subject to similar data challenges, such as social media and messaging platforms handling millions of update messages, streaming media tracking subscribers’ watching habits and recommending new content, self-driving cars, courier services optimizing shipping routes, or how online retailers group items in their warehouses to automate and speed up packing and shipping. But in these industries, data management is not such a formal or standardized process, even though many of these depend on data to run their operations, provide analytics to improve their business processes, and give an understanding of how different datasets work together—and need to be integrated—to obtain beneficial outcomes, such as being able to harness the Internet of Things.

The EDM Council is currently expanding its membership and areas of coverage to participants and issues faced by other industries beyond finance, while the recently-established Enterprise Knowledge Graph Foundation piloted by former EDM Council managing director Mike Atkin is specifically cross-industry. Enterprise knowledge graphs define and link data using web standards, and a standard ontology, rather than traditional tabular databases, enabling users to incorporate a broader range of data and make more use of it, and have applications well beyond financial services.

And despite the claims of many companies, it’s hard to genuinely be data-driven without handling data properly in the first place. How data-driven are companies in reality? According to research from Alation, a provider of data governance, security, and analytics solutions, there’s still a substantial disconnect between how firms profess to use data and how they actually use it. For example, according to Alation’s first quarterly State of Data Culture Report, produced by Wakefield Research, despite 86% of the top 2,000 global companies having a c-level data officer, and 78% having company-wide initiatives to be more data-driven, two-thirds of CEOs still make decisions based on “gut feel” rather than metrics.

Alation created a Data Culture Index, which measures how well a company is positioned to support data-driven decision making, and scores companies based on three disciplines: data search and discovery, data literacy (the ability to analyze and draw conclusions from data), and data governance. However, while data governance was firms’ second-highest priority, only 12% of companies scored an A grade for their data culture, with almost two-thirds only achieving grades C through F.

So, most of the companies that make up the economies on which the financial markets place their bets don’t take data as seriously as the companies that invest in them. Who can help accelerate their education? How about financial firms?

We’ve all heard how banks have been commercializing internal datasets as alternative data sources for buy-side clients. But another area ripe for exploitation is the expertise that financial firms and their industry bodies have acquired over many years around data management. There’s a huge opportunity for associations like the EDM Council and FISD (perhaps through its non-finance-specific parent organization the Software & Information Industry Association) to commercialize that collective knowledge in a way that benefits other sectors, as well as their members among capital markets firms, who are often the ultimate consumers of the exhaust data generated by those other industries. That opportunity could be monetary (through training, standards, licensing, representation to a broader membership, and certification) or a change to shape and define the standards for non-financial datasets that they care about, to save integration and processing headaches further down the line.

All I ask is that when we arrive at standards that make it easier for non-financial companies to optimize, someone shares them with Domino’s, so they can finally figure out how to drive my pizza just 12 blocks in less time than it takes me to write this column.