Trust Issues Have Potential to Slow Adoption of Analytics, Report Says

While HCM technology vendors trumpet their advancements in artificial intelligence and analytics, it appears their customers’ executives are increasingly nervous about the possible repercussions of relying on flawed data or analytics.

Red FlagAccording to KPMG’s “Guardians of Trust” report, just 35 percent of executives have “a high trust level” when it comes to the way their organizations use data and analytics. Nearly two thirds–65 percent–have either some reservations or active mistrust in their data and analytics, and 92 percent worry about the impact flawed data could have on their company’s business.

Interestingly, though perhaps not surprisingly, 62 percent say that “technology functions, not the C-level and functional areas, bear responsibility when a machine or an algorithm goes wrong.” That’s especially notable when you consider that 61 percent of CEOs said building trust was a “top three” priority for their company. Meanwhile, only 35 percent of technology leaders “have a high level of trust in their organization’s analytics.”

Who’s to Trust? Who’s to Blame?

In his forward to the report, Thomas Erwin, global head of KPMG Lighthouse, the firm’s center of excellence for data, analytics and intelligent automation, observed, “as organizations undergo digital transformation, with artificial intelligence sweeping through almost every industry, is someone taking responsibility for the quality, effectiveness, integrity and resilience of [data and analytics]?” The answer seems to be, “not really.”

The growing interrelationship between humans and technology requires stronger accountability at the C-level, KPMG believes. “As companies make the shift to fully digital, analytically driven enterprises, the management of machines is becoming as important as the management of people,” the report’s authors wrote.

“Once analytics and AI become ubiquitous, it will be imperative and more difficult to manage trust,” Erwin said in a press release. “With the rapid take-up of predictive analytics, we should prepare now to bring appropriate governance to this Wild West of algorithms.” The governance of machines, he continued, must become “a core part” of organization-wide governance, “with the goal being to match the power and risk of D&A with the wisdom to use it well.”

Vendors Need Their Message Ready

The report should concern HR for several reasons. For one thing, trust impacts how employees view their organization. For another, the impression that executives want to pass responsibility for data problems down the food chain doesn’t exactly reinforce notions of engagement.

As for vendors, the ante to provide ironclad data solutions is sure to increase, at least in the short and medium terms, as organizations thrash out who’ll be to blame when data is improperly compiled or scrubbed or the wrong decisions are made be analytics were misinterpreted. Trust and blame are human factors, not technological ones, and technology providers need to prepare game plans for scenarios where their products might take the fall for human errors.

The firm’s conclusions:

  • IT can no longer be on the hook for everything related to technology. “Instead, it’s time for the core business to take responsibility for its analytics and AI-and ensure quality, effectiveness, integrity and resilience.
  • Chief executives and functional leaders must to manage machines as rigorously as they manage their people.
  • Standards and controls must go beyond the operational and include cultural, ethical and “other emerging considerations” for managing advanced technology across the enterprise.

As KPMG notes, “the trust gap” between data, analytics and people “has as much to do with people’s expectations and perceptions as it does with the actual technology or the risks associated with it.” Often, people learn to trust technology only after years of successful use, as has been the case with consumer services like GPS, ecommerce, ride-hailing services and chatbots. “Education can help bridge this trust gap earlier, but the real trust comes only after a user has had repeated, successful experiences,” the report says.

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