Service
Data Engineering
Trusted data feeding decisions leaders actually use.
Executive summary
What this practice is for.
Most reporting problems are data problems. Dashboards get abandoned when two numbers disagree and nobody can explain why.
We build the boring foundations first — ingestion, modelling, lineage and quality tests — then the executive surfaces on top.
The measure of success is a leadership meeting that argues about the decision, not about the number.
Business challenges
What we are usually called in to fix.
Conflicting numbers
Every department reports a different figure for the same metric.
Manual MIS
Analysts spend the first week of the month assembling spreadsheets.
No history
Operational systems overwrite state, so trends cannot be reconstructed.
Abandoned dashboards
Reports were designed for the tool rather than for a decision.
Why traditional approaches fail
- BI tools are deployed on top of unmodelled operational data.
- Metric definitions live in report filters instead of a governed semantic layer.
- Data quality is discovered by executives rather than by tests.
- Reporting requirements are gathered without asking what decision follows.
Methodology
The SJAIN method.
Six stages, each with defined inputs, outputs and owners.
- Step 01
Define
Agree the decisions, metrics and definitions with the people accountable for them.
- Step 02
Model
Design the warehouse and semantic layer around those metrics.
- Step 03
Ingest
Build reliable, monitored pipelines from source systems.
- Step 04
Test
Automate data quality, freshness and reconciliation checks.
- Step 05
Visualise
Build role-specific dashboards and executive MIS.
- Step 06
Embed
Wire reporting into the operating rhythm and retire manual packs.
Scope of services
What is included
- Data strategy and metric definition
- Data warehouse and lakehouse design
- ETL and ELT pipelines
- Data quality and lineage
- Power BI and dashboard development
- Executive MIS and board reporting
- Self-service analytics enablement
- Advanced and predictive analytics
Deliverables
What you receive
- Metric dictionary and semantic layer
- Warehouse schema and pipeline code
- Automated quality and freshness tests
- Executive and operational dashboards
- Analyst enablement and documentation
Architecture overview
How it is built.
- 01
Source extraction with change-data-capture where volume demands it.
- 02
Raw, staged and curated layers with clear ownership.
- 03
Dimensional models tuned for the questions people actually ask.
- 04
Semantic layer holding single definitions of every published metric.
- 05
Lineage and quality dashboards exposed to data consumers.
Implementation process
Weeks 1–2
Metric definition workshops and source assessment.
Weeks 3–8
Warehouse, pipelines and first executive dashboard live.
Ongoing
Expand subject areas and enable self-service analysis.
Security & compliance
- Row and column level security aligned to organisational roles
- Masking of sensitive attributes in non-production environments
- Full lineage for regulatory reporting
- Retention and archival policy per data domain
Benefits & ROI
The numbers this practice moves.
0 days
Typical reduction in month-end reporting cycle
0
Agreed definition per published metric
0%
Fewer manual reporting requests
Capabilities in this practice
Industries served
Related technologies
Related AI solutions
Frequently asked questions
It depends on volume, variety and team skills. We size the decision rather than default to the fashionable option.
Yes — a governed data layer is the cheapest path to reliable AI, and we design it with that next step in mind.
Related case study
Private AI for credit operations
Six times faster credit file review with a full audit trail
Read the case studyDownloadable guide
Data Engineering capability guide
A practical guide covering methodology, architecture patterns, deliverables and typical commercial models.
Not sure where this practice
fits your roadmap?
We start with a discovery workshop: current state, constraints, the highest-value first slice and what it costs to prove it.