February 19, 2018
February 19, 2018
Contributor: Susan Moore
Advances in artificial intelligence demand that data and analytics leaders have a strategy for harnessing its potential.
It’s hard to read the news or scan social media without seeing examples of artificial intelligence (AI) in action. Some of its most talked about applications include roboadvisors and robotraders in the finance industry, chatbots and personal buying assistants in retail, medical diagnostics, remote patient monitoring and AI tutors for personalized education.
Despite the hype, it’s early days.
“Many organizations are just beginning their AI journey, gathering knowledge and developing strategies for applying it,” says Mike Rollings, research vice president at Gartner.
AI doesn't only offer the potential to radically improve existing business activities, but instead creates the potential for data-driven business strategies. This makes data and analytics a primary driver of strategy, which in turn mandates a more expansive examination of the potential for AI.
If you’re a chief data officer (CDO), now’s the time to define your AI strategy and assess its impact on business models and customer experience, Rollings says.
It’s not enough to look at AI in the same way as we have typically created a data and analytics strategy as a byproduct of other strategy work. Rollings recommends focusing on three areas:
Be mindful of regulatory and ethical considerations. It is possible that the same data with the same analytics may be governed differently based on the use context — one being ethically okay and the other potentially not, and with the same being potentially true for security, privacy, compliance, retention and other once separate questions. As a result, data and analytics leaders will need to raise governance issues as part of normal business discussions.
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