Trust: How organizational confidence impacts an organization’s ability to create value from analytics
This is part seven of our series on the findings and text from IBM Institute for Business Value’s latest study and paper: “Analytics: A blueprint for value - Converting big data and analytics insights into results," from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley.
In part six, we looked at the second Drive lever, Data, and what was needed to realize value from data and analytics by examining data management practices. In this part we will look at Trust, the final lever in the Drive level of impact, and examine how organizational confidence directly impacts an organization’s ability to create value from analytics.
The surprising lever that directly impacts an organization’s ability to create value from analytics is the level of trust between people within an organization. Our research finds that, in fact, a lack of trust within an organization is one of the most significant hurdles to value realization. This is not trust in the quality of the data, the reliability of analysis or the veracity of data; this is the trust between individual people, the old-fashioned kind of trust that is earned by getting to know someone’s character and what they are capable of delivering.
The level of trust—a belief that others will do a competent job, deliver on promises and support the organization’s best interest—among executives, analysts and data managers significantly impacts the willingness to share data, rely on insights and work together to deliver value. Within organizations creating value from analytics, there is a strong and pervasive level of personal trust.
Leaders generally (and seemingly genuinely) believe the people within their organization will do a competent job with the best of intentions. Business executives trust other executives, and to a lesser degree, business and IT executives trust one another. There is strong confidence between business executives and the business analysts who report to them, and those business analysts, in turn, share trust with the data analysts with whom they work (see Figure 9).
Where the level of trust starts to breakdown in Leader organizations, however, is when it becomes less personal. Fewer than half (44 percent) of respondents from Leader organizations reported a strong level of trust between business units and the IT department in general.
Wes Hunt, vice president of customer analytics at Nationwide Insurance, is a big believer in the value of trust, and one of the business advisors who pushed us to examine the idea of trust as part of the analytics process.
“I am not sure how you can develop analytics insights or act on analytics in the absence of trust,” Hunt said.
The way his organization breaks down trust barriers is through education and personal interaction, whether it is by explaining how the analysis was done, why certain recommendations are being made or even why the analysts believe the recommended actions will work. He said resistance can often have nothing to do with the current actions themselves, but rather can be a result of a past failed attempt with a similar look or feel.
“In analytics, there are multiple data sources, multiple analytic messages and multiple analytic teams, each with an insight or recommendation or point of view,” he explained. “So what makes a (an internal data) consumer—whether on the frontline with customers or an executive—trust or rely upon one set of insights over another?” The answer, he said, often lies in the confidence the presenter has with the data, a familiarity with its nuances, for example, and the personal relationship between the two parties.
In part eight we’ll look at the final level of impact, Amplify, which consists of levers that boost value creation. These levers provide the momentum and capabilities to transform insights into actions that positively impact an organization’s bottom line. Levers at this level are Sponsorship, Funding and Expertise.
Catch up on the entire series so far with parts one through six:
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