“Data is not truth. Data is a clue. Trust is built when we know where it came from, how it was shaped, and why it matters.” — Monica Rogati
Building Trust in Data: From Gut Feelings to Instrumented Confidence
For as long as businesses have existed, decision-making has wrestled between intuition and information. In the early 20th century, industrialists like Henry Ford relied heavily on observable efficiencies—how quickly a car could be assembled, how many bolts were wasted per unit. These were tangible, verifiable metrics. By contrast, many executives in the 1970s and 80s relied on top-down reports, often hand-compiled, error-prone, and slow to arrive. Trust in data wasn’t assumed; it was often a casualty of poor lineage, human bias, or outright manipulation.
Fast forward to today: leaders in every industry declare themselves “data-driven.” But beneath the glossy dashboards and predictive models lies a central tension—do people actually trust the data they’re using?
The Historical Arc of Trust in Data
The field of data governance emerged in the 1990s with pioneers like Larry English (a thought leader in information quality) emphasizing that “information is a product” that requires quality control. By the 2000s, Thomas Redman, known as the “Data Doc,” pushed the conversation further—arguing that organizations needed to treat data as a corporate asset with accountability.
Cloud platforms, self-service BI tools, and machine learning brought a flood of access—but also exposed cracks in trust. A PwC survey in 2019 revealed that only 35% of executives had a high level of trust in their data. The proliferation of siloed systems, questionable lineage, and opaque models made it increasingly hard for employees and executives alike to know whether the numbers in front of them were credible.
What “Good” Looks Like
Some organizations have cracked the code. Consider Netflix: their data platform doesn’t just generate recommendations; it’s underpinned by rigorous observability. Every dataset is tagged, lineage is tracked, and engineers and analysts alike know the freshness, source, and completeness of the data before using it. Trust isn’t assumed—it’s designed into the system.
Another example is Capital One, which invested early in data cataloging and governance tooling, building user confidence by making datasets searchable, well-documented, and rated for quality. A data scientist knows before querying whether a dataset has passed data quality checks or whether anomalies exist.
In both cases, the key is transparency and accountability—users can see the provenance, quality, and health of the data in near real-time.
What “Bad” Looks Like
On the other end of the spectrum are cautionary tales:
- Healthcare.gov (2013 launch) suffered from mismatched and poorly integrated data sources, resulting in enrollment errors and a loss of public trust. The technical flaws weren’t just infrastructure—they were fundamentally about whether the underlying data could be relied upon.
- A global bank once discovered its trading desk had been relying on spreadsheets with broken macros for critical risk reporting. The data appeared complete but masked systemic errors. When uncovered, the trust gap set back the organization’s move toward data-driven culture by years.
In both cases, the absence of clear lineage, governance, and instrumentation meant the data looked fine—until it mattered most.
Components of Trust in Data
To build and sustain trust, organizations need more than tools—they need a structured approach. At its core, trust in data rests on four pillars:
- Quality – Accuracy, completeness, timeliness, and consistency of data.
- Lineage – Knowing where data came from, how it was transformed, and by whom.
- Accessibility – Making data findable, understandable, and usable without gatekeeping.
- Accountability – Assigning ownership for data assets and holding teams responsible for quality.
These are often codified through patterns and practices like:
- Data Observability: Monitoring pipelines for freshness, schema drift, anomalies.
- Data Catalogs & Glossaries: Shared definitions, business terms, and dataset metadata.
- Federated Governance Models: Balancing central control with distributed stewardship.
- Trust Signals in Dashboards: Surface freshness, sample size, error bars—so decision-makers know what confidence to place in the numbers.
Tools, Techniques, and Practices
A modern trust-in-data stack often includes:
- Data Observability Platforms: Monte Carlo, Bigeye, Databand.
- Data Catalogs: Collibra, Alation, Atlan.
- Lineage & Governance: OpenLineage, dbt, Great Expectations.
- Monitoring & Alerts: Integration with PagerDuty, Grafana, or Slack to notify of anomalies.
But tools alone don’t build trust—practices do. Regular data quality SLAs, publishing data health scorecards, and enabling crowdsourced ratings and reviews of datasets embed trust into everyday workflows.
Measuring Trust: KPIs and Metrics
If you can’t measure trust, you can’t improve it. Forward-thinking data leaders track KPIs such as:
- Data Quality Index: % of datasets passing defined quality checks.
- Freshness SLA Compliance: % of pipelines delivering within expected latency.
- Lineage Coverage: % of critical datasets with documented end-to-end lineage.
- Data Incident Rate: Number of data quality incidents per quarter.
- User Trust Scores: Survey results from analysts and executives on perceived reliability.
- Adoption Metrics: Growth in usage of certified datasets vs. ad hoc or shadow data sources.
These metrics not only prove value but allow the data organization to speak the same language as business stakeholders—demonstrating how trust in data translates into reduced risk, faster decisions, and ultimately competitive advantage.
Instrumenting Trust for Success
Building trust isn’t a one-off project—it’s an ongoing program. Leaders should think in terms of instrumenting the business:
- Dashboards for Trust: Just as we track revenue or uptime, organizations should visualize data health and trust KPIs.
- Feedback Loops: Give users the ability to flag suspect data, creating a rapid cycle of correction.
- Cultural Reinforcement: Reward teams not just for delivering insights but for ensuring those insights are built on trusted, transparent foundations.
Wrapping up…
Trust in data isn’t abstract—it’s as tangible as uptime in a cloud service or ROI in a marketing campaign. As Thomas Redman often argues, “If you can’t trust your data, you can’t trust your business.”
Organizations that invest in observability, governance, and transparent accountability are the ones that can truly call themselves data-driven—not because they have dashboards, but because people believe in the numbers behind them.
