What gets built
- A data model and metric definitions
- Scheduled reports and summaries
- Alerts with thresholds and owners
- Queries over documented sources
Guide controls and deliverables
- A source inventory and refresh process
- Reviewable metric definitions
- Criteria for reviewing alerts and anomalies
Operational signs to review
- Data is scattered across spreadsheets, the ERP and the CRM, and nobody fully trusts the final number
- Reports are assembled by hand and arrive too late to act
- The team spends more time finding information than making decisions
How AI data analysis works
Unify
Data in spreadsheets, the ERP, the CRM, and files is identified; then a source and refresh rule are defined for each metric.
Ask
A language model turns an operational question into a query over documented data and returns the figure with its context.
Watch
Rules and models detect when something drifts from normal (a drop, a spike, a missing value) and notify the right person.
Summarize
A scheduled summary can list changes, alerts, and consulted sources at an agreed frequency.
Workflow description
Before
Data is scattered across spreadsheets, the ERP and the CRM, and nobody fully trusts the final number
After
The operational reporting and decisions workflow queries documented sources, applies each metric definition, and routes an alert according to the agreed threshold and owner.
Queried source, metric definition, refresh date, and alert owner.
What the company needs before starting
- Knowing which decisions should improve: three concrete questions are enough
- Read access to the current data sources
- An agreed definition of the key metrics
- A person who validates initial queries against the agreed source
Common mistakes in data analysis
Starting with the dashboard
First the questions that matter; the dashboard is the consequence, not the goal.
Trusting uncleaned data
Duplicates and empty fields produce confident but false answers. Cleaning is part of the work.
Measuring everything
Five metrics that get used are worth more than fifty nobody looks at.
Not closing the loop
An alert with no owner and no action is noise.
What to review during operation
- Time to answer a question about operational reporting and decisions
- Reports produced without manual work
- Anomalies caught before they hurt
- Decisions made with data versus by gut
Terms worth knowing
- Single source of truth
- One place where each metric has one definition and one value, instead of different versions per team.
- Anomaly
- A value outside the expected range given the history; the system detects it and alerts.
- Natural-language query
- Asking the data with a normal sentence, without formulas or SQL.
- Frontier model
- An AI model representing the most advanced capabilities available at a given time, evaluated for accuracy, cost, speed and privacy.