Decision frame
Write the decision, who takes it, how often, and what would change their mind.
Done when: The decision owner signs the frame before any modelling starts.
Analytics & Decision Framework (DECIDE)
Analytics & Decision Framework
From a metric tree to a decision that actually changes, instead of another dashboard nobody opens.
The business problem
Hundreds of dashboards. Few decisions.
Reports are built from available data, not from the KPIs leaders use to decide, so meetings still run on opinion.
What you hear inside the organisation
Nobody opens this dashboard.
Every department defines revenue differently.
I need the reason, not just the number.
We changed BI tools and nothing improved.
Who it serves and what changes
Your teams, before and after the Analytics & Decision Framework
Client teams and what they need
Executives
The few KPIs that drive decisions
Finance and performance
One definition per metric
BI developers
A semantic layer on any tool
Managers
Alerts and forecasts they act on
The outcome you get
- A KPI tree linked to business decisions
- Shared metric definitions
- Dashboards on Power BI, Tableau, Qlik or open source
- Alerts, forecasts and data bots for action
A worked example
A retailer with hundreds of dashboards
The problem we saw
Meetings opened with arguments about whose margin figure was right. Most dashboards were never opened.
How the framework solved it
We started from the decisions leaders make, built a KPI tree with one definition each, and rebuilt a small set of views on their BI tool.
The business result
Meetings now start from the same numbers, alerts flag what changed, and managers act instead of debating.
Illustrative example based on common client situations.
In short
What is the Analytics & Decision Framework?
Analytics & Decision Framework (DECIDE): From a metric tree to a decision that actually changes, instead of another dashboard nobody opens.
Analytics is usually measured by delivery of reports, not by decisions changed.
- How it moves
- Each step closes with a check before the next begins
- Owned by
- Run by analytics with the decision owner in the business; signed off by the executive whose numbers change.
- You keep
- Decision brief, Certified metric registry, Adoption review
How it works
How the Analytics & Decision Framework works
Retirement is part of the method. An analytics estate that only grows is an estate nobody trusts.
Metric tree
Decompose the outcome into drivers until each leaf is something someone can act on.
Done when: Every leaf metric has one definition and one owner.
Semantic layer
Implement the definitions once, above the warehouse, so every tool answers identically.
Done when: Two tools asked the same question return the same number.
Deliver
Build the smallest surface that supports the decision, a view, an alert, or a single screen.
Done when: The surface answers the decision question without a second query.
Adopt
Review real usage and the decision log thirty days later; retire what is unused.
Done when: Either the decision cadence changed, or the asset is retired.
Is it right for you?
Is the Analytics & Decision Framework right for you?
A strong fit if
- Dashboards exist but decisions do not change
- Departments argue over metric definitions
- You are moving or consolidating BI tools
Probably not yet if
- You need a single operational report
- Source data is not yet captured in any system
Free assessment
Discuss the Analytics & Decision Framework with our team
A working session with our senior team. We review what fits your situation, what does not, and where to start.
A focused conversation
We listen to your situation and goals.
Fit review
Which parts of this framework apply to you, and which do not.
A written view
Clear next steps, whether or not you work with us.
Go deeper
Applying the Analytics & Decision Framework in practice
Open only the part you need: how it starts, what your team keeps, maturity levels, sector rules and the evidence a reviewer will ask for.
Analytics is usually measured by delivery of reports, not by decisions changed. DECIDE starts from the decision, works back to the metric tree and the semantic layer, and treats adoption as the exit gate.
Version
v1.0
Last reviewed
August 2026
Family
Data & Analytics Operating System
How this was developed: Built from delivery practice on Saudi and international engagements, then reconciled clause by clause against the published standards below. Revised when a standard is updated or a gate proves unworkable in the field.
Decision brief
The decision, its owner, its cadence and the evidence that would change it.
Certified metric registry
One definition per metric, implemented once in the semantic layer.
Adoption review
Measured usage after thirty days, with a retirement decision.
Banking & finance
Risk and finance definitions must reconcile to the regulatory submission, not merely to each other.
- Who reviews it
- SAMA, plus PDPL and NCA controls
- Systems in scope
- Core banking, cards, KYC and AML systems, credit bureau feeds, the general ledger
- Where it lands first
- KYC and AML review, credit decisioning support, complaint and dispute handling
1
Decision frame
- Decision brief
- Decision rights note
2
Metric tree
- Metric tree
- Definition sheet
3
Semantic layer
- Semantic models
- Certified metric list
4
Deliver
- Decision surface
- Usage instrumentation
5
Adopt
- Adoption review
- Retirement list
Retirement is part of the method. An analytics estate that only grows is an estate nobody trusts.
Stage
Exit gate
Artefacts
1
Decision frame
Write the decision, who takes it, how often, and what would change their mind.
Exit gate
The decision owner signs the frame before any modelling starts.
Artefacts
- Decision brief
- Decision rights note
2
Metric tree
Decompose the outcome into drivers until each leaf is something someone can act on.
Exit gate
Every leaf metric has one definition and one owner.
Artefacts
- Metric tree
- Definition sheet
3
Semantic layer
Implement the definitions once, above the warehouse, so every tool answers identically.
Exit gate
Two tools asked the same question return the same number.
Artefacts
- Semantic models
- Certified metric list
4
Deliver
Build the smallest surface that supports the decision, a view, an alert, or a single screen.
Exit gate
The surface answers the decision question without a second query.
Artefacts
- Decision surface
- Usage instrumentation
5
Adopt
Review real usage and the decision log thirty days later; retire what is unused.
Exit gate
Either the decision cadence changed, or the asset is retired.
Artefacts
- Adoption review
- Retirement list
Standard
What this framework satisfies
DAMA / DCAM
Business intelligence, metadata and data literacy knowledge areas.
NDMO
Data value realisation and metadata specifications evidenced by the metric registry.
NIST AI RMF
Govern function, decision rights and accountability for analytic outputs.
Dashboard as the deliverable
Shipping a screen ends the project before the decision has changed. Instrument usage and review it.
Two definitions of one metric
Meetings become arguments about the number. Certify one definition in the semantic layer.
No decision owner
Without a named owner, insight has nowhere to land and no one to act on it.
Built on
- 1
DAMA-DMBOK and EDM Council DCAM
Data management knowledge areas and capability assessment.
- 2
NDMO data management and personal data protection standards
National Data Management Office specifications and domains.
- 3
FinOps for AI
Unit economics and cost accountability for AI workloads.
- 4
NIST AI Risk Management Framework 1.0 and Generative AI Profile
National Institute of Standards and Technology, Govern, Map, Measure, Manage.
Citations describe the parts of each standard the framework answers. Bilytica is not an accreditation or certification body.
In Saudi Arabia
The data layer, as NDMO reads it
Most AI programmes in Saudi Arabia stall underneath the model. These frameworks work on that layer, against the control set the national office actually assesses.
AuthorityWhat it requiresWhat we buildWhat you keep
NDMO data management controls
Owners, policies and measurable maturity across every data management domain.
Baseline assessment, then control-by-control remediation with dates and owners.
Maturity baseline and a re-assessment on the same control set.
SDAIA, PDPL
Classification and retention applied to personal data wherever it is copied.
Classification enforced in the platform, so a copy inherits the rules of its source.
Lineage view showing classification travelling with the data.
ZATCA, for invoicing and transactional data
Invoice and transaction records reconcile to the filed position.
Reconciliation checks run as data quality rules, with exceptions routed to an owner.
Reconciliation report and an exception log with resolution times.
Where it runs
Registers, lineage and quality evidence live in your environment inside Saudi Arabia and stay yours.
Arabic, as it is spoken here
Business glossaries are maintained in Arabic and English together, with Arabic as the governing definition.
FAQ
Frequently Asked Questions
Is this a BI project?
It is the layer above BI. The tooling is secondary; the decision, the definition and the adoption gate are the method.
What if adoption fails?
The asset is retired and the decision frame is re-examined. That outcome costs less than maintaining an unused estate.
How does it connect to AI work?
The same metric tree becomes the benefit baseline the AI Value Framework measures against.
Where this connects
Rest of the library
DFFData Foundation FrameworkAIRMAI Readiness ModelVALUEAI Value FrameworkTRUSTAI Assurance FrameworkDELIVERAI Delivery FrameworkAGENTAgent Operations FrameworkSOVEREIGNSovereign AI FrameworkDMMData Management Maturity ModelDPAData Platform Architecture FrameworkDQFData Quality & Observability FrameworkDGOVData Governance Operating ModelSDCFSaudi Data Compliance FrameworkAQSArabic Quality StandardKSA-READYIn-country Deployment Framework
Topic cluster
Explore the topic cluster
Curated links across our Saudi hubs, sector pages and long-form blog research.