Power BI & Microsoft Fabric
See what is happening.
Understand why.
Know where to look next.
DataDecision
Most organisations do not need more data. They need a clearer connection between operational information and the decisions people are trying to make. Power BI can turn ERP, CRM and operational data into useful management insight. Microsoft Fabric may become relevant when the analytical requirement extends across more systems, more history, more data or more advanced analytical and AI needs.
Start with the decision. Then determine the data architecture required to support it.
A dashboard is useful only if somebody knows what decision it is supposed to improve.
A technically impressive report can still fail if the metric is not trusted, the definition is unclear, the data is late, nobody owns the exception, or the report does not change what anybody does.
Before the dashboard
What decision should become easier?
Define the question before designing the visual. The role usually tells you what the report has to answer.
Where is cash pressure building?
The data journey
From recording what happened to knowing what to do.
Broadly, ERP and CRM record, Power BI reports and explains, Fabric supports the wider analytical picture, and AI or predictive work follows where it is justified. Not every organisation needs to move through every stage.
Record
What happened? Usually ERP and CRM.
Report
What is happening? Often Power BI.
Understand
Why is it happening? Definitions and context matter here.
Anticipate
What may happen next? Where Fabric and modelling can help.
Act
What should we do, and who owns it?
Answer it where it lives
Some questions should be answered directly from the operational system.
Do not create a data platform to answer a question the ERP already answers well.
- Open sales orders
- Overdue invoices
- Inventory availability
- Open cases
- Purchase orders
- Production status
Management insight
Bring operational information into a clearer management view.
Power BI should explain the position across the areas the business actually manages, rather than reproduce transaction lists in a different colour.
- Finance
- Sales
- Purchasing
- Inventory
- Warehouse
- Manufacturing
- Projects
- Customer Service
- Support
- Public services
- Care
- Data quality
What happened?
The position, stated plainly.
What is happening now?
Current state, not last month's export.
What changed?
Movement is usually more useful than a total.
Where is the exception?
The thing that is not behaving as expected.
Why should I care?
The commercial or operational consequence.
Who needs to act?
A named owner, not a general audience.
A dashboard should reduce the number of questions leadership needs to ask before taking action. Five to eight decision-led measures, with trend, exception, drill-down, context and ownership. Not forty tiles.
What good looks like
The same discipline, applied role by role.
Each view exists to support a decision somebody is accountable for making.
- Cash, receivables, payables and working capital
- Revenue, margin and forecast against budget
- Inventory and project profitability
- Where is cash becoming constrained?
- Which customers are delaying receipts?
- What supplier commitments are due?
- Where is margin moving, and why?
With your systems
Reporting sits on top of the systems you already run.
Business Central, Finance & Supply Chain and CRM each create useful data. The question is which decisions need a broader management view.
Power BI and Business Central
Business Central already creates useful operational and financial data. The question is which decisions need a broader management view.
- Finance
- Sales
- Purchasing
- Inventory
- Projects
- Manufacturing
- Warehouse
Power BI and Finance & Supply Chain
Enterprise operations usually span more processes, more sites and more history, so the management view has to hold more together.
- Finance
- Supply chain
- Warehouse
- Manufacturing
- Planning
- Asset
- Procurement
- Enterprise operations
Power BI and CRM
Customer data becomes more valuable when pipeline, service demand and account development are read together.
- Pipeline
- Sales activity
- Customer Service
- Cases
- Customer trends
- Account development
- Service demand
The honest answer
Not every analytics requirement needs Fabric.
Power BI may be enough in more situations than the market tends to admit.
A manageable number of sources
The data does not span the whole estate.
Straightforward history
The required history is contained.
Contained transformation
The logic can be understood and maintained.
Manageable volumes
Performance is not the constraint.
Reporting and analysis
The need is insight, not data engineering.
Simple governance
Ownership can stay clear without a platform team.
Use the simplest architecture that can support the decision responsibly.
Cross-system data
Fabric becomes more relevant when the analytical problem is bigger than the individual application.
These are signals, not thresholds. The more of them that apply together, the more likely a shared data platform earns its place.
- Multiple operational systems
- Large data volumes
- Long historical analysis
- Shared enterprise data
- Complex transformation
- Data engineering
- Data science
- Advanced analytics
- AI foundations
- Broader governance
- Multiple reporting domains
Source systems
ERP, CRM, WMS, ecommerce, public-sector systems and other data.
Microsoft Fabric
Ingest, store, transform, model and govern.
Power BI
The management view people actually use.
AI and predictive intelligence
Where the decision genuinely justifies it.
Microsoft Fabric should solve a data problem, not create a data programme because the technology exists.
If the requirement is one finance dashboard, one operational report, or a contained Power BI model, Fabric may add complexity without enough value.
One data foundation
Cross-system reporting becomes easier when common definitions are agreed.
A shared data platform does not automatically create shared meaning. The business still needs common definitions for the concepts it reports on.
- Customer
- Product
- Supplier
- Order
- Project
- Employee
- Site
- Case
- Service
- Date
- Finance dimensions
The most valuable data model often defines the business before it defines the dashboard.
What does this metric mean?
In business language, not in formula language.
Who owns the definition?
A named person, not the report author by default.
Which source is authoritative?
One system of record per concept.
How is it calculated?
Written down, and the same everywhere.
Trust
Somebody needs to own the meaning of the number.
Data governance cannot be delegated entirely to the BI developer.
CFO
Owns the financial definition.
Sales leadership
Owns the pipeline definition.
Operations
Owns the service and capacity definition.
Data team
May own the technical implementation, not the meaning.
KPI design
Measure what helps somebody make a decision.
A metric can be accurate and still be useless. Total opportunities, total users, total orders and total cases may say nothing about quality, risk, service, profitability or future constraint.
KPI
What is happening?
Target or expectation
What should be happening?
Trend
Is it improving?
Exception
Where is the problem?
Owner
Who can act?
Action
What happens next?
Leadership sees the signal. Operational teams need the evidence behind it.
KPI
The signal leadership sees.
Trend
Direction, not a single point.
Business area
Where the movement sits.
Customer, item, project or service
The specific case.
Transaction or event
The evidence behind the number.
Avoid dashboards that need a separate analyst to explain every exception.
Timing
Not every decision needs real-time data.
Data should be fresh enough for the decision, not as fast as technically possible.
How quickly can the situation change?
Some positions move hourly. Most do not.
How quickly can the organisation act?
Freshness beyond the response time is waste.
What does freshness cost?
Engineering, licensing and ongoing support.
What operational value does it create?
State the benefit before buying the speed.
Fabric can become particularly useful where the organisation wants to keep and analyse longer history than the operational application handles comfortably.
- Demand history
- Inventory
- Project performance
- Customer trends
- Supplier performance
- Service demand
- Predictive modelling
Before more analytics
Better dashboards can expose poor data. They cannot magically repair it.
If the source data cannot be trusted, the dashboard should make that visible too.
- Missing values
- Duplicate records
- Incorrect classifications
- Stale master data
- Inconsistent dates
- Unowned definitions
- Manual overrides
- Integration failures
Control
Analytics should not become a route around application security.
Access, data minimisation and a maintained report estate matter as much as the reports themselves.
- Role-based access
- Row-level security where applicable
- Workspace governance
- Data ownership
- Sensitive data handling
- Sharing, export and external users
- Analytics should not become a route around application security
Where AI fits
AI belongs after the information is connected and trusted.
Prediction is worth having when a forward view genuinely changes a decision, with confidence stated rather than implied.
Connect
The data is available and consistent.
Trust
The definitions are agreed and owned.
Explain
People understand why the number moved.
Anticipate
Patterns support a forward view, with stated confidence.
Assist
AI summarises, explains and prepares, under review.
How this connects
Better information supports recovery, transformation and improvement.
Analytics is rarely the whole answer. It is usually how everyone agrees what is actually happening.
Recover
Recovery often finds reporting nobody trusts, and definitions nobody owns.
Transform
Transformation is the moment to agree what the business measures and why.
Optimise
Optimisation often improves decisions without building a data platform at all.
Predictive intelligence
Forecasting is worth having once the underlying information is trusted.
Common questions
Questions leaders ask about reporting and data platforms.
Your decision
Which decision should become easier first?
We are happy to say when Power BI is enough, and equally happy to say when the analytical problem genuinely justifies Microsoft Fabric.