One analyst says01
“Revenue is declining. We need to find out why.”
Analytical reasoning with performance data
What do the data justify concluding?
I help analytics leaders improve how their organizations reason with performance data—so decisions rest on evidence rather than assumption.
See the reasoning
Performance behaviorOne signal changes the question.
Signal
Routine variationEvidence of change
Data quality ≠ analytical quality.
Difference ≠ change.
Evidence before explanation.
01 / About Lindsay
01
Same data.
Same data.
Different reasoning.
Revenue fell 12% last month.
Another analyst says02
“Revenue is 12% lower. Before we explain why, do we have enough evidence to conclude that revenue performance actually changed?”
The first treats the difference as evidence of change and begins searching for an explanation.
The second asks what the evidence supports concluding before deciding whether there is anything to explain.
That is analytical reasoning.
It is the discipline of moving from data to conclusions based on evidence rather than assumption.
Organizations have standardized how they collect, move, store, model, govern, and visualize data.
What many organizations haven’t standardized is how performance data become evidence for conclusions and decisions.
Without a common standard, the same performance data can produce different conclusions.
One analyst sees improvement. Another sees decline. A leader sees urgency.
Yet everyone may be reacting to routine variation rather than evidence that performance has actually changed.
I developed Evidence-First Analytics to address that gap.
The premise is simple:The evidence should determine the conclusion.
A common evidentiary standard
Before explaining what happened or deciding what to do, there should be a common evidentiary standard:
- 01
What are we trying to accomplish?
- 02
What question must the analysis answer?
- 03
What evidence is needed?
- 04
What do the data support concluding?
- 05
What decision does that evidence justify?
For ongoing performance data
This includes one critical distinction:
Difference ≠ Change.
Every process varies.
The fact that this month is different from last month does not, by itself, establish that performance has changed.
That’s where the
2.66 Method™
fits:- 01Define the business outcome.
- 02Select the operational measure.
- 03Understand process behavior.
- 04Determine whether the process has changed.
- 05Learn and improve.
The goal isn’t to explain every difference.
It is to determine which differences provide evidence of change—and therefore deserve explanation and action.
Through workshops, presentations, and consulting, I help analytics leaders establish a common standard for reasoning with performance data—so their organizations reach defensible conclusions before making decisions that matter.
Schedule a complimentary 30-minute Analytical Quality Assessment™.02 / Ways to work together
Build analytical judgment across your organization.
A shared evidentiary standard helps analysts, leaders, and AI systems distinguish a difference worth noting from a change worth acting on.
Speaking
Practical talks that change how leaders interpret performance data.
Workshops
Hands-on learning that gives teams a shared method for reasoning from data to conclusions.
Consulting
Focused engagements to strengthen analytical quality in KPI reviews, dashboards, and decisions.
03 / Start a conversation
Your numbers may be accurate.
Do they justify the conclusion?
Invite Lindsay to speak, lead a workshop, or help your organization establish a stronger standard for analytical reasoning.
Connect with Lindsay