- 01Distinguish a numerical difference from evidence of meaningful change.
- 02Recognize when routine variation is being treated as a problem to explain.
- 03Apply an evidence-first sequence before investigation, explanation, and action.
- 04Reach conclusions that are clear, defensible, and decision-ready.
Lindsay Alston — Speaking
Speaking · Workshops · Executive sessions
Analytical reasoning
with performance data.
Help your audience answer the question that should come before explanation and action:
What does the data justify concluding?
Data quality ≠ analytical quality.Difference ≠ change.Evidence before explanation.
01 / Signature talk
What does the data justify concluding?
Organizations have standardized how data are collected, moved, stored, modeled, governed, and visualized.
What many have not standardized is how performance data become evidence for conclusions and decisions.
When a KPI moves, analysts are often asked to explain why. But a difference between two numbers does not, by itself, establish that performance changed.
In this session, Lindsay introduces an evidence-first standard for analytical reasoning. Through practical examples, audiences see how the same data can produce different conclusions—and how understanding process behavior helps determine which differences deserve explanation and action.
The session equips analytics leaders to strengthen the reasoning between dashboards and decisions, especially as AI makes analysis and explanation easier to produce.
02 / Audience value
A common standard for reasoning with performance data.
Designed for leaders accountable for the quality of analytical conclusions—not only the quality of the underlying data.
- Data and analytics leaders
- BI and insights teams
- Revenue and operations leaders
- Organizations expanding AI and self-service analytics
“The evidence should determine the conclusion.”
03 / About the speaker
Lindsay Alston
Speaker · Creator of Evidence-First Analytics
Lindsay Alston helps data and analytics leaders improve how their organizations reason with performance data so decisions rest on evidence rather than assumption.
He developed Evidence-First Analytics and the 2.66 Method™ to address a critical gap between trustworthy data and trustworthy conclusions: determining whether performance data provide evidence that a system actually changed before explaining why.
Through presentations, workshops, and consulting, Lindsay gives analytics teams a common evidentiary standard for reaching defensible conclusions before decisions that matter.
Connect with Lindsay on LinkedIn04 / Engagement formats
Built for the room—and the decision context.
Keynote
A focused session that reframes how organizations move from performance data to decisions.
Conference session
A practical talk that helps audiences distinguish routine variation from evidence of change.
Workshop
An applied experience that gives analytics teams a common standard for reasoning with performance data.
05 / Speaking inquiries
Bring analytical reasoning to your next event.
Share your event, audience, format, and preferred date. Lindsay will follow up to explore the right session for your organization.
Start a speaking conversation Are you an analytics leader? Schedule an Analytical Quality Assessment™