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Building Data Analytics Platforms That Drive Decisions

August 27, 2025•5 min read•Team 400

Here's an uncomfortable truth: most companies have analytics dashboards that nobody looks at.

The CEO asked for visibility. IT built dashboards. They launched with fanfare. Six months later, executives make decisions the same way they always did—gut feel and spreadsheets.

We've built data analytics platforms that avoid this fate. Here's what makes the difference.

Why Analytics Platforms Fail

The Dashboard Graveyard

Symptoms:

  • Dashboards built but rarely opened
  • Same questions still go to "the data person"
  • Executives prefer their own spreadsheets
  • Nobody can explain what the metrics mean

Root causes:

  • Built for IT, not for decision-makers
  • Data accuracy questioned
  • Too many metrics, not enough insight
  • No connection to actual decisions

The Data Swamp

Symptoms:

  • Data exists but nobody trusts it
  • Same question, different answers from different sources
  • "Data projects" take months
  • Everyone has their own version of truth

Root causes:

  • No single source of truth
  • Poor data quality at source
  • Technical debt in data pipelines
  • No governance or ownership

The Shiny Object

Symptoms:

  • Impressive demos
  • Cutting-edge technology
  • No clear business questions answered
  • Perpetual "enhancement" projects

Root causes:

  • Technology-led rather than problem-led
  • No defined use cases
  • Metrics disconnected from decisions
  • Stakeholders not involved in design

What Actually Works

Start with Decisions

Don't ask "what data should we display?"

Ask "what decisions do we make, and what would help us make them better?"

For each key decision:

  • Who makes it?
  • When do they make it?
  • What information do they currently use?
  • What would make it better?

This flips the model from data-out to decision-in.

Pick Three Metrics

Most dashboards have too many metrics. The human brain can't track 47 KPIs.

For any role/function, identify:

  • 1 north star metric: The one number that matters most
  • 2 supporting metrics: Leading indicators that predict the north star

Everything else is detail, available on demand but not front-and-centre.

The DBM Global Visualiser we built followed this principle—focused visualisation of key project metrics, not data overload.

Make Data Trustworthy

Trust is the foundation. If people don't trust the data, they won't use it.

Build trust through:

  • Single source of truth: One canonical definition for each metric
  • Visible data freshness: Clear timestamps, data age indicators
  • Transparency about limitations: Known issues visible, not hidden
  • Reconciliation: Key metrics match financial systems

One client had dashboards with data that differed from their monthly reports by 5-10%. Executives stopped trusting the dashboards. We spent a month fixing data quality before touching visualisation. Worth it.

Embed in Workflow

Dashboards that require separate login never get used.

Embed analytics where decisions happen:

  • In the tools people already use
  • In regular meeting cadences
  • In automated alerts and reports
  • On screens in operations areas

A dashboard that sends a morning email gets more engagement than a beautiful portal nobody visits.

Architecture Patterns

The Modern Analytics Stack

Typical architecture for a decision-oriented analytics platform:

Source Systems → Data Pipeline → Analytics Database → Visualisation
                      ↓                  ↓                  ↓
                  (Airflow,         (Snowflake,        (Looker,
                   dbt,             BigQuery,          Metabase,
                   Fivetran)        Redshift)          Power BI)

Key principles:

  • ELT over ETL: Load raw data, transform in the warehouse
  • Version-controlled transformations: dbt or similar
  • Semantic layer: Consistent metric definitions
  • Self-service enabled: Users can explore safely

For Smaller Companies

You don't need the full stack immediately.

Start with:

  • Cloud data warehouse (BigQuery, Snowflake)
  • Basic ELT (Fivetran, or scripts)
  • Simple BI tool (Metabase, Looker Studio)

Add sophistication as you grow. The principles matter more than the tools.

Real-Time Considerations

Not everything needs to be real-time.

Real-time matters for: Operations, alerts, customer-facing applications Daily/hourly is fine for: Executive dashboards, reporting, analysis

Real-time is expensive and complex. Apply it selectively.

Building for Decision-Makers

Executive Dashboards

Executives are time-poor. Design accordingly:

  • Glanceable: Key metrics visible in 5 seconds
  • Exception-focused: Highlight what needs attention
  • Trend-oriented: Direction matters more than absolute numbers
  • Action-linked: What should they do about this?

Operational Dashboards

Operations teams need different things:

  • Real-time or near-real-time: Stale data isn't useful
  • Actionable detail: Specific items to address
  • Historical context: What's normal?
  • Alert integration: Don't make them watch screens

Analytical Tools

Analysts need to explore:

  • Self-service access: Don't bottleneck on data team
  • Safe environment: Can't break production
  • Good documentation: What's in each table?
  • Collaboration features: Share and discuss findings

Common Mistakes

Mistake 1: Too Much Too Soon

Building a complete data platform before proving value.

Better: Start with one use case. Prove value. Expand.

Mistake 2: Perfect Data Quality

Waiting for perfect data before delivering anything.

Better: Deliver with known limitations. Fix data quality in parallel.

Mistake 3: Technology Obsession

Choosing tools based on technical elegance, not business fit.

Better: Simple tools that solve the problem beat complex tools that impress other engineers.

Mistake 4: No Ownership

Analytics platform built by IT with no business owner.

Better: Business stakeholder owns the outcome. IT enables it.

Mistake 5: Build Everything Custom

Building from scratch when good tools exist.

Better: Buy where commoditised. Build where differentiated.

Making It Stick

Analytics platforms fail in the long term because of organisational issues, not technical ones.

Required for Success

Executive sponsorship: Someone senior cares about usage Data team capacity: Ongoing support, not just initial build User training: People know how to use it Feedback loops: Users can request improvements Metric ownership: Someone accountable for each key metric

Maintenance Reality

Budget for ongoing work:

  • Data quality monitoring and fixes
  • Pipeline maintenance
  • New data source integration
  • User support and training
  • Dashboard iteration

A platform without maintenance budget becomes legacy.

Getting Started

If you're building or rebuilding an analytics capability:

  1. Inventory current state: What exists? What's used? What's trusted?
  2. Identify key decisions: What decisions could be better-informed?
  3. Pick a focused scope: One use case, proved valuable
  4. Build trust first: Data quality before fancy features
  5. Iterate based on usage: What do people actually need?

We've built analytics platforms and data visualisation tools for Australian businesses. Happy to discuss your analytics challenges.

Get in touch