The data quality rescue mission

When data quality fails, it doesn’t fail quietly. It fails in headlines, audits, lost customers and missed opportunities. From compliance risks to AI misfires, the consequences of poor-quality data are tangible — and often preventable.

In this section, we look at how quality issues undermine trust, trigger crises, and stall innovation, and what forward-thinking organizations are doing to address these issues.

When bad data breaks things

For many organizations, the tipping point of poor quality data isn’t theoretical — it’s a mission-critical incident impacting the business. A failed product launch. A regulatory fine. A damaging audit. These moments expose just how fragile decisions become when they’re built on flawed data.

And they’re happening often. 51% of professionals say that data quality is the area most in need of improvement in the year ahead. That number alone signals a widespread lack of confidence in the foundation of most data-driven initiatives.

60% of respondents cited data quality as the biggest barrier to confident data use — more than visibility, access or security combined.

The consequences are already becoming apparent. 41% report that data quality issues are directly impacting the trustworthiness of shared data. In other words, even when data is shared across teams, it’s not always used because people don’t trust it.

Manual processes make matters worse. 30% cite delays caused by manual workflows as a core obstacle to timely access and 29% of professionals report they are unable to effectively enforce or manage data quality standards. Without a shared understanding of who owns what — or lacking the tools to track and fix issues at speed — data problems compound under pressure. In fact, only 43% of organizations have assigned stewardship roles to maintain quality across domains. Without automation and accountability, these efforts become reactive, not preventative.

Even among high-confidence orgs, only 55% say their data is highly consistent.

Are you worried that bad data will hinder you AI efforts?

Read our ebook to learn how data reliability can save the day

Read ebook