DataZone — data & analytics
We build data platforms, BI and analytics systems — from connecting sources to management dashboards.
Sources
Dashboard
Diagram: Excel, CRM, ERP, billing, website, API, CSV and Google Sheets → data pipeline → warehouse → analytics → dashboard.
Today
Management gets the report three days later — and every department has its own number.
With DataZone
Every stage is a concrete action with a clear result. Scroll: the panel on the right shows what each step produces.
CONNECT
CRM, ERP, billing, website, spreadsheets. Incremental loads on a schedule or on events — no manual exports.
source: crm.deals mode: incremental (updated_at) schedule: */15 * * * * last run: 1 204 rows · 6.2 s
CLEAN
Duplicates, empty values, five spellings of the same thing. Validation rules are written down and run on every load.
check: unique(order_id) ✓ check: not_null(region) ✗ 14 rows normalize: ' region b' → 'B' quarantine: 14 rows → review
TRANSFORM
Orders, customers, payments and tickets linked into one model. Business rules live in versioned code, not in Excel formulas.
orders ⨝ customers ON customer_id ⨝ payments ON order_id → fct_orders (+segment, +paid)
STORE
A data warehouse and marts per department. History is kept: you can see what a number was a month ago.
dwh.fct_orders partitioned by day dwh.dim_customer SCD type 2 mart.sales_daily refreshed 06:00 mart.finance_month refreshed 1st
ANALYZE
Metrics are defined once: “conversion” and “revenue” mean the same thing across the company.
metric conversion = count(paid) / count(placed) dimensions: region, segment, week owner: sales ops
VISUALIZE
A dashboard built around the decision it serves. Every chart answers a question instead of filling space.
dashboard: Weekly sales KPI: revenue, conversion, AOV drill-down: region → manager access: role = sales_head
PREDICT
Once data is clean and regular, it supports demand and workload forecasts, anomaly detection and an AI analyst.
model: demand_weekly features: season, promo, price horizon: 8 weeks anomaly: conversion B −18% → alert
Four sources go all the way to a management action. Every number is computed from the demo data, not written into the copy.
How data arrives: a duplicate order, an empty region, different spellings, amounts as text.
| order_id | customer_id | region | amount |
|---|---|---|---|
| 10481 | C-201 | "Region A" | "1 200,00" |
| 10482 | C-117 | " region b" | "860,50" |
| 10482 | C-117 | " region b" | "860,50" |
| 10483 | C-342 | NULL | "2 400,00" |
| 10484 | C-088 | "REGION C" | "310" |
| 10485 | C-117 | "Region B" | "1 050,00" |
01
CRM · ERP · Billing · API · Databases · Excel · Google Sheets · External data · Logs
02
PostgreSQL · ClickHouse · Cloud warehouse · Data marts · Dimensional models
03
Dashboards · Management reporting · KPI · Drill-down · Alerts
04
Duplicates · Missing values · Normalization · Reference data · Validation
05
Cohorts · Segmentation · Churn · Sales · Operations · Market
06
Sales · Demand · Workload · Churn · Anomalies
Each one answers a single question and ends with an action. The data is for demonstration — the logic is real.
CEO
Demo ScenarioMargin by week
| Margin by week | Target | |
|---|---|---|
| W1 | 31.6% | 31% |
| W2 | 31.2% | 31% |
| W3 | 31.8% | 31% |
| W4 | 31.5% | 31% |
| W5 | 31.4% | 31% |
| W6 | 31.7% | 31% |
| W7 | 31.5% | 31% |
| W8 | 31.8% | 31% |
| W9 | 30.2% | 31% |
| W10 | 29.3% | 31% |
| W11 | 28.8% | 31% |
| W12 | 28.4% | 31% |
Revenue is above plan, but margin has fallen for four weeks in a row: growth is coming from discounts.
Action Review the discount policy by region
We collect open market data: competitor prices and plans, assortment, geography — with the history of every change.
A good fit for Telecom · Pharma · Retail · E-commerce
Price of a comparable base plan, by week
Demo Scenario| Your plan | Competitor A | Competitor B | Competitor C | |
|---|---|---|---|---|
| W1 | 79k | 82k | 85k | 72k |
| W2 | 79k | 82k | 85k | 72k |
| W3 | 79k | 82k | 85k | 72k |
| W4 | 79k | 82k | 85k | 72k |
| W5 | 79k | 75k | 85k | 72k |
| W6 | 79k | 75k | 69k | 72k |
| W7 | 79k | 75k | 69k | 74k |
| W8 | 79k | 75k | 85k | 74k |
On price your plan was number 2 of 4; now it is number 3 (1 is the cheapest).
Change feed
Pick sources and goals — the diagram appears on the right straight away: sources, pipeline, warehouse, BI and outputs.
SOURCES CRM · ERP · 1C · Excel · Sheets PIPELINE 3 connectors · File ingestion with schema checks · Deduplication · validation · reference data STORAGE Data Warehouse (PostgreSQL or ClickHouse by volume) · Data marts per department OUTPUTS Dashboards · Alerts
This is a preliminary outline. The architecture may change once we look at your sources.
The agent watches the metrics, finds deviations and prepares an explanation with a recommendation — no request to an analyst needed.
Want the data to report problems on its own?
AI on top of dataAgentZonePortals, APIs and specialised interfaces on top of your data platform are built by DevZone.
Need your own analytics system?
Custom developmentDevZoneFirst what hurt the business, then the architecture. For now these are demo scenarios, and we label them that way.
Five questions and you get a preliminary data architecture and a first step. Or just write to us.
Preliminary data architecture