Going Beyond Compliance in Toronto

Looking over the agenda for our Toronto Financial Information Summit on Tuesday, June 23, I was struck by the lighter presence of regulatory compliance topics on the program compared to years past.

This is not to say compliance isn't important or there will not be any discussion of it whatsoever in Toronto, but it's just pointing out that data governance, leveraging data for analytics and efforts to raise data quality are now considered important enough to merit their own panels and their own time during the day.

Does this signal that financial industry operations professionals are about to emerge from having to focus extensively on regulatory compliance, crowding out their ability to address work that can more readily add value to data management, such as governance plans, analytics and raising quality?

Some may say that even with a slight recovery in the global economy, higher level management at financial firms is still insistent on cost control, although others may argue such a directive actually supports doing more work on data governance planning. Yet, as Giles Arbuthnott, a data services manager at index data provider Rimes Technologies said recently, the firms that will profit most are those "who are agile and able to adopt new strategies."

The data governance discussion promises to explore this conflict between cost concerns and the pressing need to keep data management up to date. Those who seek to build a data governance model have to consider whether changes are needed in their firm's culture, business or IT infrastructure – or all three.

As Giles Arbuthnott of Rimes Technologies said recently, the firms that will profit most are those 'who are agile and able to adopt new strategies'

Certainly, leveraging analytics and data – including big data, both historical and real-time data, and even social media data – can be more easily pitched as an effort that will bring a lot of value for minimal cost, comparatively. Yet, the understanding of how to achieve this may be elusive and our planned discussion should shed light on this.

Data quality can also be an indefinite goal or trait. What should a high data-quality standard look like? Is an industry-wide standard needed? Another panel promises to share ways in which firms can take steps to raise quality, whatever the bar.

Beyond compliance, these are the considerations most likely to make a difference for data operations and management.