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Dashboards

From raw data to business insights

Dashboards are only as reliable as the data behind them. We design systems that turn fragmented data into consistent, usable insights.

1. Data sources

We start by mapping where your business numbers come from so we know what needs to be connected and normalized.

1
1C (orders)SAP (products)APIs (payments / external data)
Illustrative raw samples
1C (orders)
ORD-1048 | 12.03.2026 | "Northwind" | sum=1 240,00 RUB | status=paid
ORD-1047 | 2026/03/11 | ACME ltd | total:860.00 | cur=RUB
ORD-1046 ; 10-03-2026 ; Blue Ridge LLC ; amount=2 190 rub ; state=processing
ORD-1045 | 08.03.26 | Pine & Co. | refund? yes (320,00)
SAP (products)
MAT-2201 - Widget A (cat: Tools) - price=19,90 USD - stock=120
MAT-2202|widget b|Category=tools|unitPrice: 19.9|STOCK:- 
mat-2203 | Widget C | category: Services | net_price=0? (check)
MAT-2204; widget d; cat=TOOLS; unit=29 USD; availability=--
APIs (payments)
pay_88a1 {amount:124000, currency:"RUB", time:"2026-03-12T09:14:00Z", status:"succeeded"}
pay_88a2 {amount_cents:86000; USD ; timestamp: 12/03/2026 13:04 ; state: ok}
PAY-88A3 {amt=219000; cur=RUB; created=2026-03-10 18:22; result=fail?}
pay_88a4 {amount:320000; currency: RUB} // missing timestamp
Short note

Data is fragmented, inconsistent, and stored in different formats.

What this stage delivers
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2. Data processing and enrichment

We turn raw inputs into trustworthy datasets using pipelines and processing layers that perform data ingestion, transformation, matching and joining, and validation and data quality checks.

2
What this stage delivers
Ingestion: bring data into a consistent workflow
Transformation: standardize definitions and formats
Matching and joins: connect related records accurately
Validation: apply quality checks before data is used
Example enriched records
Normalized fields
order_id | order_date  | customer     | currency | total_amount
ORD-1048  | 2026-03-12 | Northwind    | RUB       | 1240.00
ORD-1047  | 2026-03-11 | ACME ltd     | RUB       | 860.00
ORD-1046  | 2026-03-10 | Blue Ridge   | RUB       | 2190.00
Matched joins
order_id | product_id | product_category | payment_status | quality_flag
ORD-1048  | MAT-2201   | Tools             | succeeded       | ok
ORD-1047  | MAT-2202   | Tools             | ok (mapped)    | ok
ORD-1046  | MAT-2204   | Tools             | failed/mismatch| needs_review

The result is consistent, validated records that can be trusted for reporting and analytics.

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3. Clean business data

This layer produces a unified data model with consistent structure and validated, enriched records. It becomes the foundation for reporting and analytics across your organization.

3
What this stage delivers
Unified data model
Consistent structure
Validated and enriched records
Example reporting-ready records
order_id | order_date | customer   | product_id | product_category | payment_status | currency | revenue_amount | record_quality
ORD-1048  | 2026-03-12 | Northwind  | MAT-2201   | Tools            | succeeded       | RUB       | 1240.00        | valid
ORD-1047  | 2026-03-11 | ACME ltd   | MAT-2202   | Tools            | succeeded       | RUB       | 860.00         | valid
ORD-1046  | 2026-03-10 | Blue Ridge | MAT-2204   | Tools            | needs_review    | RUB       | 2190.00        | needs_review

This is the layer reporting and analytics query against. Consistent definitions keep KPIs comparable over time.

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4. Dashboard and insights

Dashboards bring the clean business data into practical views for teams: KPIs for weekly rhythm, charts for trends, and tables for traceability.

4
What this stage delivers
Revenue
$248k
Orders
1,685
Average order value
$147
Revenue over time (USD)
Last 12 months
Line shows total revenue for the same months.
Top products by revenue share
Sunglasses
$219,000
51%
Sunscreen
$124,000
29%
Beach towels
$86,000
20%
What your data is telling you

These patterns help you spot what to check first when KPIs change.

Outside temperature vs sunglasses demand
When it gets warmer, sunglasses orders rise too.
Temperature (°C)
Sunglasses demand (people)

This kind of co-movement is a practical trigger for planning stock before demand shifts.

Orders vs revenue
When orders rise, revenue rises too.
Orders
Revenue (USD)

This is why operational teams often track order volume as a leading indicator for financial results.

Data quality guardrails
When enrichment detects mismatches, records are flagged so KPIs stay trustworthy.
Valid: matches passed checks
Needs review: enrichment mismatch flagged
Business action (example)
Prioritize order volume fixes when revenue drops

Start by checking ingestion and join quality for the sources that drive order counts. If payment success degrades, review the last enrichment step before KPIs are published.

Simple table: orders (snapshot)
OrderCustomerStatusTotal
#1048NorthwindPaid$1,240
#1047AcmePaid$860
#1046Blue RidgeProcessing$2,190
#1045Pine & Co.Refunded$320