On May 20th, 2020, the CRTA hosted the third session of our Spring Webinar Series entitled: Instilling Trust Through Sound Data Governances Practices
With data at the nexus of digital transformation efforts that are taking place across a wide spectrum of public and private sector organizations, a scalable and sustainable approach to data governance is crucial for success. The diverse composition of backgrounds and experiences from our subject matter experts made for a thought-provoking and highly informative conversation from which I’ll highlight two key themes that emerged:
Standards Development in Canada
Mr. Jansa shared how the CIO Strategy Council has taken a very active role in helping to influence and transform the information and technology sector in Canada through the work of cross industry technical committees that are developing a series of standards for data governance
At the same time, he cautioned that as a nation we are a little late to the table when it comes to standards-setting relative to the European Union, the U.S., and China noting:
“If Canada is left to adapt technologies, platforms, and practices that are based on standards rules that have been set by other countries, that would put us in a position where maybe we aren't able to export or drive our own productivity as a country.”
The discussion veered on to the topic of whether the adoption of standards can be achieved on a voluntary basis through the ecosystems and supply chains – an approach that sounded like it has merit especially when you consider that the pace of technology and innovation will always outpace regulatory and compliance legislation enacted for sectors like banking and telecommunications.
Mr. Kulkarni commented that he was proud to be a member of the Canadian Data Governance Standardization Collaborative (“the Collaborative”) ...
“It's been fantastic to watch this organic collaboration approach come together where people are saying we're going to do this not because allies are prescribing it, but because it’s the correct way. Canada's organic approach that leads to a bottom-up meets top-down in the middle – I think that approach will win out over the long term.”
Note: the Collaborative was established in May 2019 as a cross-sector coordinating body to accelerate the development of industry-wide data-governance standardization strategies. It is led by co-chairs Anil Arora, Canada’s Chief Statistician, and Philip Dawson, the Public Policy Lead at Element AI.
Quantifying Data Governance
Our moderator, Mr. Bhanot, transitioned the conversation onto a topic that would resonate with many, that being, how can organizations that have established a formal approach to data governance, with lots of written policies, standards, and procedures, show a quantifiable impact on the top line or bottom line?
Ms. Paszti noted that firms need to be able to recognize and measure the risks that are associated with managing data.
“Whether it's regulatory risk or reputational risk, you have to ensure that whatever outcomes you thought you were going to get from collecting that data are achievable. And if you're unable to do that, then you have to go back to see whether your framework has to be tweaked.”
From Mr. Kulkarni’s experience, he shared that by applying a ‘benefits-driven lens’ you can achieve better collaboration amongst stakeholders in the organization.
“Rather than looking at data standards as an exercise, we look for simple examples of how much time is being saved in terms of different departments exchanging different data sets ... that's a very clear and objective measure”
There was unanimous consensus on the importance for organizations to define key performance indicators (KPIs) and to establish quality indicators to measure the effectiveness of data governance.
Mr Jansa also referenced the recent standards developed and published by the CIO Strategy Council for the ‘Ethical Design and Use of Automated Decision Systems,’ which contain a specific requirement for ensuring that the test results inherently sought are being realized.
Assessing the effectiveness of data governance controls in this use-case (artificial intelligence (AI), Automated Decision Systems) will be of paramount importance. Being able to proactively assess potential data risks up front will require a well-defined framework with standard operating procedures in place that can be measured frequently for their effectiveness, and this is definitely a move in the right direction.
Prepared by Paul Childerhose, Strategic Advisor to the CRTA