The scenario
“Active customers” means one thing in the CRM, another in billing, and a third in the board pack. Every AI initiative downstream inherits that disagreement.
Data Foundation demo
One governed source, proven on the number your teams argue about
We take one metric that three systems answer differently, build the governed path end to end, and show the same number arriving in a report, a model and an agent.
In Saudi Arabia
What turns the Data Foundation demo into a production system
A working demo and a system a Saudi reviewer will sign off are two different things. These are the questions asked before go-live, and what we hand over against each one.
AuthorityWhat it requiresWhat we buildWhat you keep
SDAIA, PDPL
Lawful basis for every personal-data field the demo touches, before it runs on real records.
Field-level data map produced during scoping, sanitised extract used until that map is signed off.
Data map and lawful-basis note the client's DPO can file before production access is granted.
NDMO
Data classification and quality controls on the sources the demo would depend on in production.
Source-by-source classification, with quality thresholds agreed before the pilot widens.
Classification map and a quality gate report your data office keeps.
NCA ECC
Access control, logging and read/write boundaries clear before wider rollout.
Entitlements scoped per role, every action logged, write paths gated behind approval.
Control matrix and an action log your risk function can replay.
Where it runs
The demo itself can run on a sanitised or representative extract, but where the data cannot leave a system, the build happens inside your environment inside Saudi Arabia, not ours.
Arabic, as it is spoken here
Arabic questions and documents are handled as first-class input, not translated to English before processing, including organisational and Gulf-dialect terms.
Who it is for
CDOs, data leaders and CFO offices who cannot get one agreed number out of the current estate.
Sample workflow
The chain of work behind the answer
1
Trace
- Every source that claims the metric
- The transformations between them
- Where the definitions diverge
2
Ingest
- Batch and streaming into the lakehouse
- Schema contracts at the boundary
- Late and duplicate data handled
3
Model
- Conformed dimensions and master keys
- One governed metric definition
- Semantic layer the tools read
4
Assure
- Quality tests on every load
- Lineage from report back to source
- Classification and access controls
5
Serve
- Reports, models and agents on one path
- Freshness and ownership published
- Alerts when the number breaks
The foundation is proven when the same governed number appears in a dashboard, a forecast and an agent answer without reconciliation.
What you will see
Two examples, start to finish
“How many active customers did we have at month end?” asked in three tools.
- Behind it
- Lineage traced → three definitions surfaced and reconciled with the business → one definition governed and published → all three tools repointed.
- What comes back
- One number in all three, with the definition, the owner and the lineage one click away.
A nightly load arrives with twelve percent of rows missing a key.
- Behind it
- Quality test fails at the boundary → the load is quarantined instead of published → owner alerted with the failing rows attached.
- What comes back
- Yesterday's trusted data stays live, and nobody presents a broken number to the board.
Deliverables
What you keep after the session
Every item below is an artefact your team can reuse, whether or not the engagement continues.
Lineage map for one metric
From every source system to every place it is reported today.
Governed definition
One agreed definition with a named owner and the disputes it resolved.
Working pipeline slice
Ingest to serving for that metric, with quality tests running.
Foundation assessment
What the same treatment would take across the wider estate, in sequence.
Session agenda
- Pick the number your teams argue about, live
- Trace it back through the estate to its sources
- Watch a quality test fail on purpose
- The same governed number in a report, a model and an agent
What we need from you
- One contested metric and the systems that report it
- Read access to those sources or representative extracts
- A business owner who can settle the definition
Where data cannot leave a system, the demo is built inside your environment instead of ours.
FAQ
Frequently asked questions
Do we need to replace our warehouse?
Usually not. Most of the gain comes from contracts, definitions, quality tests and lineage on what you already run.
Is this a prerequisite for AI, or can they run in parallel?
They run in parallel, but only where the AI use case reads from the governed slice. Anything reading around it inherits the old disagreement.
What does the demo actually leave behind?
A working pipeline slice for one metric plus the lineage and definition artefacts, reusable, not a slide deck.
Topic cluster
Explore the topic cluster
Curated links across our Saudi hubs, sector pages and long-form blog research.