Data and analytics should be a “nonnegotiable business priority,” according to a report from KPMG released this week. Yet, 60% of organizations report that they are not very confident in their data and analytics insights. Only 10% said they excel in managing the quality of data and analytics, and 13% said they excel in the privacy and ethical use of data and analytics, the report found.

Further, 49% of data and analytics decision makers reported that their C-level executives do not fully support the company’s data and analytics strategy.

In some cases, this lack of trust may be due to a lack of understanding of how the company uses analytics, said Bill Nowacki, managing director of decision science at KPMG in the US.

“If I think about a retailer, they do forecasting, recommendations, fleet optimization–analytics are core to everything,” Nowacki said. “When an executive talks about a lack of trust, there’s some incongruity there. One has to wonder where that comes from–don’t they realize they’re already doing this in spades?”

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In other cases, executives might be fearful after one poor experience with analytics where a prediction was incorrect and an opportunity was missed, Nowacki said. The fear that analytics and automation may eventually take over their job could also be a factor in the reported distrust, he added.

For the report, Building trust in analytics, KPMG commissioned Forrester Consulting to survey 2,165 decision-makers responsible for the management of business intelligence, data analytics, data warehousing, and big data management initiatives. Respondents worked across different industries in 10 countries.

About half of businesses use data and analytics tools to find new customers, analyze existing customers, and develop new products and services, the report found. Yet, data and analytics decision makers said they lack confidence in the insights they are gaining through these methods. Only 38% of respondents said they are very confident about the information they gain in customer insight, and 34% are confident in their insights around business operations.

Despite this lack of trust, 77% of organizations said that their customers trust their use of data and analytics.

“There’s nary an executive that would disagree that analytics are fundamental to the way we do business today, and the way we’ll do business in the future,” Nowacki said. “But there’s a lot of individual work to be done.”

Gaps in trust grow throughout the data and analytics process. While 38% of respondents said they have the most trust in data sourcing, only 21% have the most trust in the next step of analysis and modeling. Just 11% of decision makers said they have the most trust in actually using analytics, and only 10% expressed confidence in measurements of the effectiveness of their analytics use.

“The bottom line is that people are predominantly emotional decision makers, and this inhibits a quantitative, analytic and evidence-based approach,” said Alan Duncan, research director of data and analytics at Gartner. “Indeed, emotion is a motivating force behind all human decisions and judgments; without emotions, human beings cannot make decisions.”

Business leaders who want to develop their organization’s data-driven culture must therefore find ways to overcome that psychological resistance, Duncan said.

The KPMG report offers seven recommendations for building data and analytics trust in your organization:

1. Assess your trust gaps

Performing an initial assessment to see where your business needs trusted analytics the most, and focus on those areas.

2. Create purpose by clarifying and aligning goals

Make sure the purpose for collecting data and running analytics is clear for all involved. Measure data and analytics performance and impact, and share them with users so they can see the ROI.

3. Raise awareness to increase internal engagement

Build understanding of data and analytics among business users, and create a team of data and analytics decision makers and IT and business leaders to collaborate on projects. The CIO is perfectly positioned to help with these efforts, and can act as a liaison, ensuring governance is in place and that teams are collaborating, Nowacki said.

4. Build expertise

Develop an internal data and analytics culture by ensuring you have employees with expertise in analytics quality assurance. data and analytics employees are critical for building understanding of data and analytics companywide.

5. Encourage transparency

Improve transparency by establishing cross-functional teams, third-party reviews, peer reviews, wiki-style sites, and stronger quality assurance processes. “Essentially, have every data and analytics challenge reviewed independently,” the report stated.

6. Take a 360-degree view by building ecosystems

Look beyond organizational structures and silos to examine the value and risk that data and analytics can bring to the company as a whole. Create teams across departments to build data and analytics communities.

7. Stimulate innovation and experimentation

Develop a model for data and analytics innovation, and allow data and analytics teams to experiment with different methods without fear of failure. When possible, incentivize employees for innovating with data and analytics processes.