Scenario / 01
Unified mart
Sources load into an agreed model, pass checks, and become the report foundation.
1Sources2Model3Checks4Mart
Data Leaders and analysts who need agreed metrics from multiple sources
Agreed metrics from every source — back to the source record
We start with metric definitions, sources, and calculation rules — so the same question does not get different answers in different reports.
01Sources
02Rules
03Metric
04Detail
OutputAgreed metric
Every number shows formula, freshness, and source records.
Practical outcome
Each metric has a formula, sources, and freshness; gaps can be traced to a record
Problem
The same KPI is calculated differently, reports go stale, and the number's origin is unclear
Primary route
sources → cleanup → metric model → dashboard → drill-down
Working scenarios
From source to an explainable metric
Demonstration routes, not client results. Exact logic depends on your rules, data, and systems.
Scenario / 02
Freshness control
Each load records time and volume; lag or anomalies create an alert.
1Load2Metrics3Threshold4Signal
Scenario / 03
Metric drill-down
From an aggregate you reach components and find the change cause without a new manual report.
1KPI2Slice3Records4Cause
Solution scope
Data model and working report
- Metric dictionary
- Source diagram
- Ingestion and data model
- BI panel or report
- Quality and freshness control
Data and systems in the loopPower BIREST APIGoogle SheetsCSV / XMLDatabasesYandex Metrica
A good fit for
Which decisions analytics supports
Leaders and analysts who need agreed metrics from multiple sources
Management dashboard
Key metrics from multiple sources with shared definitions and update time.
Operational control
Queues, SLAs, exceptions, and other process states that need team attention.
Recurring reporting
Automatic prep of a validated dataset instead of manual spreadsheet merges.
Engagement trigger
When reports lost trust
Management decisions depend on manual summaries and debates about metric correctness
- 01Reports disagree on the same KPI
- 02A summary is assembled by hand before meetings
- 03A number's origin cannot be verified
- 04Source errors quietly enter the dashboard
Fit / boundaries
What must not go unchecked
A dashboard will not fix undefined metrics or poor source data without separate work
01A dashboard cannot fix contradictory metric definitions
02Result quality is limited by source quality and availability
03Operational decisions need an agreed refresh lag up front
How we launch
From a real process to a working loop
Tools are chosen after we verify inputs, exceptions, and the success criterion.
- 01
We define questions
Decisions the report must support — then metrics.
- 02
We describe metrics
Formulas, periods, owners, sources, and allowed lag.
- 03
We build ingestion
APIs, files, or databases; history; error control.
- 04
We create the model
Unify entity meaning and add automatic quality checks.
- 05
We design the UI
Context, trends, filters, and a path from aggregate to source rows.
Where does an analytics project start?+
With management questions and metric definitions — not chart colors or a BI tool pick.
Can sheets and APIs be combined?+
Yes. Each source gets format, refresh period, IDs, and quality checks.
How do you avoid conflicting KPI values?+
One definition, one source of truth, one implemented formula used in every view.
Can we see source records?+
Yes when permissions allow: from aggregate to slice to concrete rows.
How are load failures detected?+
We watch time, volume, required fields, anomalies, and checksums; deviations alert.
First step
Map the analytics loop
We'll map inputs, exceptions, and constraints. You leave with a priority scenario and a next step — no obligation to start a project.
Map the analytics loopService: BI and analytics