You hired Power BI developers to build dashboards and reports that drive decisions. Instead, they're spending most of their time finding data, cleaning data, reconciling data, and explaining why numbers don't match.
This isn't a Power BI problem. It's a data platform problem.
When the foundation is wrong, even the best analysts struggle. When the foundation is right, they flourish. Here's how to set your Power BI team up for success.
The Problem: Analysts as Data Plumbers
Ask your Power BI team how they spend their time. The answers are usually painful:
Data acquisition takes forever
Every new report starts with finding the data:
- Which system has this information?
- Who can give me access?
- How do I extract it?
- Is this the same data [other analyst] used?
What should take minutes takes days. What should be self-service requires IT tickets.
Transformation happens everywhere
Without centralised data preparation, each analyst builds their own transformations:
- Power Query in every PBIX file
- Complex DAX calculations duplicating business logic
- Spreadsheets filling gaps between systems
- Nobody quite doing it the same way
The same data transformed three different ways produces three different answers.
Reconciliation consumes weeks
When reports show different numbers:
- Which is right?
- Where did the difference come from?
- Is it a data issue or a calculation issue?
- Who has time to figure it out?
Leadership debates numbers rather than direction because they've learned not to trust any single source.
Good analysts leave
Your best Power BI developers didn't sign up to be data janitors. They want to build compelling visualisations, uncover insights, and drive decisions.
When the job becomes data wrangling, they find opportunities where they can do actual analytics work.
The Solution: A Data Platform That Serves Analysts
The fix isn't more analysts or better Power BI skills. It's building the data foundation that Power BI was designed to sit on top of.
Single source of truth
A proper data platform integrates your systems and provides one version of key metrics:
- Customer data from CRM, ERP, and support systems—unified
- Sales data with consistent definitions across regions
- Financial data that ties to the general ledger
- Product data with current and historical attributes
When analysts query the platform, they get the same numbers. Arguments about data quality become discussions about business decisions.
Pre-built datasets ready for analysis
Instead of accessing raw tables and transforming them in Power Query, analysts get curated datasets:
- Facts and dimensions already joined
- Business logic applied centrally
- Data quality rules enforced
- Documentation explaining what everything means
Your analysts start from analysis-ready data, not from "let me figure out how these tables relate."
Self-service access without IT tickets
With proper security and access controls:
- Analysts browse available datasets
- They connect Power BI directly to the platform
- They explore, prototype, and build without waiting
- Governance happens at the platform level, not through access restrictions
Self-service BI finally delivers on its promise because self-service data access actually works.
Historical data that makes sense
Legacy data often haunts analytics:
- Codes that changed meaning
- Products that were restructured
- Territories that were redrawn
- Acquisitions that merged incompatible data
A data platform handles this complexity centrally. Analysts get historically consistent views without understanding every migration and reorganisation.
What This Changes for Your Team
When the platform is right, Power BI work transforms:
New reports in hours, not weeks
Need a new sales dashboard?
- Connect to the curated sales dataset
- Build visualisations on trusted metrics
- Share with stakeholders
- Done
No data hunting. No transformation building. No reconciliation cycles.
Consistent numbers across reports
Every report using the same dataset gets the same answers:
- Finance's revenue matches sales' revenue matches ops' revenue
- No more "my spreadsheet shows something different"
- Confidence in the data translates to confidence in decisions
Analysts doing actual analysis
When data acquisition drops from 80% of time to 20%, analysts do what you hired them for:
- Uncovering trends and anomalies
- Building predictive models
- Exploring correlations
- Answering questions leadership didn't know to ask
This is where analytics value actually comes from.
Faster onboarding
New analysts become productive quickly:
- Documented datasets explain what's available
- Standard definitions prevent confusion
- Existing reports provide patterns to follow
- They build on the platform instead of reinventing data access
The Platform Requirements
What does your Power BI team need from a data platform?
High-performance query layer
Power BI needs to query your data without timeout errors or frustrated users:
- Sub-second response for interactive dashboards
- Ability to handle concurrent users across reports
- Direct Query support for real-time needs
- Import mode support for complex calculations
Platforms like Azure Synapse, Databricks SQL, and Snowflake are designed for this workload.
Semantic layer (optional but valuable)
A semantic layer between the platform and Power BI can add value:
- Business-friendly names for technical tables
- Standard metrics defined once, used everywhere
- Relationships pre-defined for consistent joins
- Row-level security enforced centrally
Power BI's own semantic models can serve this purpose, or you can use dedicated tools like dbt or AtScale.
Good documentation
Analysts need to understand what's available:
- Dataset catalogues with descriptions
- Field definitions explaining business meaning
- Lineage showing where data originates
- Usage examples and common patterns
Without documentation, analysts build tribal knowledge instead of shared understanding.
Appropriate security
Data access needs balance:
- Open enough that analysts can explore and experiment
- Controlled enough that sensitive data is protected
- Auditable for compliance requirements
- Simple enough that access requests don't take weeks
Role-based access aligned to job functions usually hits the right balance.
The Investment
Building a data platform requires investment, but so does the current state—you're just paying in hidden costs.
Current hidden costs
What are you spending now?
- Analyst time on data acquisition and transformation
- Reconciliation cycles when numbers don't match
- Delayed decisions waiting for trusted data
- Turnover when good analysts get frustrated
These don't appear as line items, but they're real.
Platform investment
A modern data platform for Australian enterprises typically involves:
- Initial build: $60,000-400,000 depending on scope and complexity
- Ongoing platform: $1,500-20,000/month in cloud, licensing and support
- Internal capability: Data engineering headcount to maintain and evolve
Return timeline
Most organisations see measurable improvement within 3-6 months:
- Analysts spending more time on analysis
- Faster report development cycles
- Fewer data quality disputes
- Increased confidence in reporting
Full ROI typically materialises within 12-18 months as the platform matures and usage expands.
Getting Started
If your Power BI team is struggling with data rather than thriving with insights:
Assess the current state
- Where does your team spend their time?
- What data sources are most problematic?
- Which reconciliation issues recur?
- What would change if data access were seamless?
Define priority use cases
Don't try to solve everything at once:
- Which reports or dashboards are most important?
- What decisions depend on data you can't currently trust?
- Where would faster access create immediate value?
Start with high-value, achievable wins.
Build incrementally
The platform doesn't need to be complete before it's useful:
- Start with highest-priority data sources
- Build first curated datasets for immediate needs
- Get analysts using the platform quickly
- Expand based on demand and feedback
Iteration beats big-bang deployment.
Related Reading
- Building a Modern Data Platform for Australian Enterprises
- Self-Service BI: Why It Never Works (Until It Does)
- Your Data Warehouse Isn't Working—Here's Why
- The Real Cost of Bad Data
- Why Leadership Debates Numbers, Not Direction
Ready to set your Power BI team up for success? Book a call with our team. We help Australian businesses build data platforms that empower analysts instead of frustrating them. We'll discuss your current challenges and what a realistic path forward looks like.
