Canadian CIOs are still trying to find their way through the hype to get the real value out of artificial intelligence (AI) and machine learning.
“There is a lack of understanding,” said a CIO from the financial sector at a recent CanadianCIO virtual roundtable. “We need more substance on what AI can do for us and how it ties to our business objectives.”
Many organizations are not sure where to start with AI, acknowledged Filip Draskovic, Information Architecture Executive, IBM Canada. “It should start with a self-assessment to evaluate where they are and then plan small steps forward to move up the analytics and AI maturity curve.” Draskovic suggested that businesses look for the “low hanging fruit improvements” that will produce quick results.
The consensus among participants was that, at the end of the day, success with analytics and AI will depend on the data.
You can’t have AI without this
There is no quick fix to improve data quality, said Draskovic. “It’s not a one-time thing. It’s an ongoing process and two-thirds of it is people and process.”
The quality of the data is vital to succeed with AI, but it’s just as important to improve the information architecture. “We have a saying that there is no AI without the IA,” Draskovic said. Information silos have to be eliminated. Organizations must make sure that users can access the data from one place and safely share it.
If employees have to copy information to share it or if ongoing improvements to the data aren’t tracked, you’re losing time and productivity, Draskovic said. Making copies also poses a security risk and increases storage costs.
This approach is not the same as moving data to a data warehouse. Rather, data virtualization provides an access layer to an inventory of data, wherever it resides. “It’s a one-stop-shop for any data in the organization,” said Draskovic. It solves the biggest problem when a new project is launched, which is knowing where to find the data. It also simplifies data governance.
Another advantage is that centralized access can put the data in the hands of line-of-business experts who can become “citizen data scientists,” noted a public sector CIO. They are in a good position to identify use cases that support business objectives.
Are we ready to let machines take over?
Many of the participants acknowledged that their organizations aren’t ready to turn decision-making over to machines. “There is a resistance from business leaders due to a concern over privacy and access to data,” said one IT leader. “There is strong pushback on moving on this front too quickly.”
Organizations must also monitor machine learning to make sure the models they’re using don’t become biased. This will soon be required by legislation, said Draskovic. One IT leader added that she sees risks in allowing machines to make sensitive decisions that impact people’s lives.
“The bottom line is whether you trust the data and the governance,” said Draskovic. “And that all comes down to the strength of the architecture.”