David Saul, Chief Scientist at State Street Corp., recently penned a short article putting forth the need for technology-driven data governance in the financial industry. His analysis ties this need to building trust across sometimes-adversarial parties in the industry:
Let’s look at four constituencies that have a shared interest in restoring and improving trust in the operation of the global financial services environment. By enhancing trust in the market we increase investment and raise economic standards for everyone. Financial services organizations derive their revenue from their clients while keeping risks at an acceptable level. Product and services companies innovate and sell. Regulators and supervisors ensure that laws are complied with. Standards organizations follow processes to enable simple and effective communication among the parties. When those four constituencies treat one another as adversaries, the financial services marketplace is less efficient leading to loss of overall trust. When the four work together in pairs or as a group they all gain value.
I propose that we move to a new model in which those four groups collaborate to their mutual benefit. To accomplish this goal I posit that we need better data governance built on semantic data standards. When data moves along with its meaning based on standardized definitions it enables transparency. Transparency is at the heart of trust that benefits everyone.
I think that David has hit the nail on the head with one of the key values of semantic data in cross-organizational contexts: shared meaning. Furthermore, semantic data has the characteristic that in addition to enabling mutual trust and data exchange, it also makes data more repurposable for internal uses. One of the drivers for adoption of semantic technologies that we see within both finance and pharma is this dual motivation: on one hand, many industry standards groups are moving towards semantic data to foster trust and increase the ability to do ad-hoc data exchange; on the other hand, many organizations are internally adopting semantics to ease integration, analytics, and decision support challenges when dealing with diverse sources of data. When two motivations meet, we see the greatest acceleration of semantic technology adoption.