Executive Summary

Many teams still suffer the "soul-crushing" BI moment: a presenter discovers a mis-aggregation, bad filter, or misleading chart in front of stakeholders, which erodes trust and wastes hours; this is especially common among non-technical users who now build most dashboards. That recurring pain creates real operational and credibility costs for analytics teams and the business partners who rely on their reports. You could build an analyst-facing pre-presentation checker that plugs into major BI tools to run automated validations (schema, joins, aggregations, filters), generate plain-language summaries and risk flags, and offer suggested fixes or rollback options as part of the slide-prep workflow. Combining lightweight structured-data validators with LLM-generated explanations would make these checks accessible and actionable for non-engineers in one click. The market is sizable and tangible — ~200,000 target businesses at a $30K ACV equals a $6.0B addressable market, and with an 88/100 market score and 88/100 revenue potential plus rising self-service BI adoption, monetization looks promising. To differentiate, prioritize UX for analysts, shallow cross-platform integrations (Tableau/Looker/Power BI), and validation outputs tailored to presenters rather than engineers; this shifts the value proposition from observability to prevention. The biggest challenges are heterogeneous BI APIs, avoiding false positives, and earning enterprise trust, but those are addressable with prioritized connectors, conservative default checks, and an enterprise onboarding/trust program.

Market Opportunity

Preventing the "soul-crushing" BI moment with pre-presentation checks targets a $6.0B = 200,000 businesses × $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 20% = estimated growth for data observability and analytics tooling (MarketsandMarkets and Gartner 2023-2024 estimates).

Key trends driving demand: Self-service BI adoption is increasing — more non-technical users create dashboards, which raises the frequency of human-facing mistakes that undermine trust.; Data observability and testing tools are moving from engineering-only to analyst-facing, creating opportunities for UX-focused products that prevent presentation failures.; LLMs and structured-data validators can now explain, summarize, and detect misleading aggregations, enabling automated 'presentation checks' that were previously manual.; Executive focus on data-driven decision-making means a single public failure can lead to rapid vendor churn, increasing willingness to pay to avoid such incidents..

Key competitors include Monte Carlo, Great Expectations, Datafold.

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Preventing the "soul-crushing" BI moment with pre-presentation checks

Executive Summary

Market Validation

Market Opportunity

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