Cut BI costs without breaking Tableau
A practical ELT pattern for reducing warehouse and consulting spend while keeping Tableau dashboards fast, governed and easier to maintain.
Read the ELT playbook
Start with the business problem
Find the right article, then the right service path.
Each article now links back to the relevant DataDive service so Google and readers can understand how the topic connects to dbt consulting, BigQuery warehouse work, Tableau delivery, Power BI reporting, migration support or managed analytics.
High-intent guides for teams fixing reporting and data foundations
Before you build AI agents, check whether your data is ready
A maturity check for teams planning automation on top of scattered, incomplete or inconsistent operational data.
Assess AI readinessHow to drive a successful BI program
Steps for turning reporting demand into a clear BI roadmap, governed dashboards and business adoption.
Improve BI deliveryChoose the reporting issue you need to solve
Modern data stack, dbt and warehouse quality
For teams fixing warehouse structure, transformation logic, data quality and governed reporting foundations.
- Modern ELT with dbt, BigQuery and Snowflake
- Improve data quality before reporting goes live
- Lay the data foundation for AI
- Build scalable data infrastructure
Tableau, Power BI and dashboard performance
For teams redesigning dashboards, improving adoption or moving from manual reports to clearer BI workflows.
- AI-powered dashboards and smarter reporting
- Choose the right BI tools
- Turn data into actionable insights
- Common BI project misconceptions
AI-ready analytics and automation
For teams planning AI agents, automated commentary, process automation or predictive analytics on top of business data.
- Check data maturity before AI agents
- Move from no data strategy to AI-enabled
- Automating processes with AI
- Future of data analytics
Strategy, delivery and change
For leaders deciding what to fix first, how to reduce delivery risk and how to make analytics work stick.
- Five steps for a successful BI program
- Start small, fail fast and improve delivery
- What business thinks vs reality in BI projects
- Build insight into the reporting workflow
DataDive blog archive
Modern ELT with dbt, BigQuery and Snowflake
Lower BI cost and improve Tableau performance with a cleaner analytics engineering pattern.
Read articleBefore you build AI agents, check whether your data is ready
A simple maturity check before AI automation sits on top of operational data.
Read articleAutomating processes with AI
How to move from isolated AI experiments to measurable operating workflows.
Read articleGaining actionable insights
Turn patterns and trends into decisions that shape business priorities.
Read articleAI-powered dashboards
Where AI can improve dashboards, predictive insight and decision support.
Read articleFuture of data analytics
Trends shaping analytics, AI, platforms and business intelligence delivery.
Read articleBuilding scalable infrastructure
Why growing data needs stronger storage, processing and reporting foundations.
Read articleLaying the data foundation
How to inventory, connect and prepare data before analytics or AI work scales.
Read articleFrom no data strategy to AI-powered
A practical path from scattered reporting to AI-enabled workflows.
Read articleAccuracy of data in your data lake
Why meaningful analytics starts with validation, lineage and quality checks.
Read articleStart small, fail fast
Build, deliver, get feedback and improve analytics without long project cycles.
Read articleTechnology that complements, not complicates
Choose BI and data tools that fit the business instead of adding complexity.
Read articleHow to drive a successful BI program
Five steps for building a BI strategy that users trust and adopt.
Read articleWhat business thinks vs reality in BI projects
Common BI delivery traps and what successful teams do differently.
Read articleGoogle Cloud data analytics
A platform overview for teams assessing BigQuery and cloud analytics options.
Read article
Need help applying this?
Discuss your reporting stack
Turn the article into a practical data or reporting plan.
Tell us what is slow, manual, unclear or hard to trust. We will help you identify whether the fix is data engineering, dbt modelling, BigQuery structure, Tableau design, Power BI reporting or ongoing analytics capacity.