What to inspect
Where each key number comes from, who defines it, how often two tools disagree about it, and which decision waits on it.
- Source of each number
- Who owns the definition
- Tools that disagree
Assessment area
Data gap
Data and analytics decides whether your numbers are trusted enough to act on. This area looks at the reports, forecasts and customer values your team argues about instead of using.
Data & Analytics
Fix the numbers before you predict with them.
AI analytics is only as good as the records under it. We start with the question a leader needs answered every week, trace where that number comes from, and check whether the data can carry a forecast or a customer value estimate.
What AI can run
Reconciling numbers across tools, drafting the weekly summary, and producing forecasts and value estimates with their assumptions shown.
- Reconciled reports
- Weekly summary drafts
- Forecasts with assumptions
What stays human
Metric definitions, which forecast the plan uses, and any decision about budget, hiring or stock.
- Definitions
- Planning calls
- Budget decisions
What we score
The data checks that come first.
Most analytics trouble shows up here long before a model is involved.
Definitions
Whether a lead, a customer and a sale mean the same thing in every tool and every meeting.
- Shared definitions
- Stage rules
- Changes logged
Connections
Whether orders, contacts and campaigns link up, so any number can be traced back to where it came from.
- Customer matching
- Orders linked to sources
- Duplicate records
Use
Whether a named person reviews the number on a schedule and changes something because of it.
- Named reviewer
- Review schedule
- Decisions taken
Workflows
Two analyses that depend on clean history.
Start with whichever decision is already waiting on an answer.
Buyer questions
Data questions leaders ask us.
Do we need a data warehouse before using AI analytics?
Rarely at the start. Many teams begin by connecting their CRM, store and ad accounts directly. A warehouse helps once several teams need the same combined data.
Why do our tools show different numbers?
Each tool counts from its own events and time zones, and each gives itself credit. Pick one source for each number and write down how the others differ.
What should we fix first?
The definition behind the number your leadership reviews every week. Everything built on that number inherits its errors.
Next step
Get to one number everyone trusts.
We trace your most-used number back to its sources and say what to fix before any forecast is built on it.
Get your free plan