DatabricksDatabricks StaffExplainer
The post examines why business intelligence (BI) dashboards lose user trust, even when data is accurate, focusing on how inconsistent metric definitions and opaque data sources cause users to doubt results. Richard Tomlinson argues that AI tools accelerate chart creation but also amplify trust debt if underlying data governance and semantics are weak, since AI can generate many inconsistent charts quickly. He recommends governing data and meaning, preserving provenance, and designing for inspection so visualizations invite investigation rather than presenting unexplained answers. Tomlinson stresses that dashboards should be self-explanatory, traceable, interactive, and context-aware, and that the critical metric for modern BI is ‘time to trust’ not just time to answer. Self-service fails when verifying an AI-generated answer takes longer than asking an analyst, so good visualization must enable inspection and challenge.
Richard Tomlinson, a BI and product leader, explains why dashboards fail due to trust issues stemming from inconsistent metrics and opaque data sources, and discusses how AI tools both accelerate analytics and risk deepening trust debt without proper governance.
BI dashboards fail not because they are ugly but because users encounter numbers they cannot reconcile with other reports, leading to trust erosion.
AI accelerates chart creation but amplifies trust debt if underlying data, semantics, and governance are inconsistent.
Good visualization should be self-explanatory, traceable, interactive, and context-aware, enabling users to inspect and challenge AI-generated results.
Dashboards fail when users see a number they cannot reconcile with other reports, often due to inconsistent definitions or sources. They stop trusting the system and quietly go back to spreadsheets or analysts.
Leaders should prioritize governing data and meaning, preserving provenance, and designing for inspection so AI-generated charts are traceable and invite investigation rather than presenting unexplained answers.
AI struggles with purpose and context. It can plot data correctly but cannot determine who will use the visualization, what decision is being made, or what might be misinterpreted. Human judgment remains essential.
Summaries are generated from the original by a language model. Check the original before relying on it.
See the idea
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