Your Data Workflow Is Costing You Time.

• Your Data Workflow Is Costing You Time

The Full Journey

• Start with a raw SQL query
• Transform data for business logic
• Connect to a BI tool like Power BI
• Build visualizations and dashboards
• End with actionable insights
• Each step can be optimized
• Reusability is key to efficiency

Why Reuse Queries?

• Saves significant development time
• Ensures consistency across reports
• Reduces errors from manual copying
• Simplifies maintenance and updates
• Makes onboarding new analysts easier
• Creates a single source of truth
• Improves overall team productivity

Common Reuse Pitfalls

• Hard-coded values and filters
• Copy-pasting entire query blocks
• No central documentation
• Inconsistent naming conventions
• Missing comments for context
• One-off queries for similar needs
• Creating dashboard-specific data sources

Modular Query Design

• Break complex logic into CTEs
• Use views for frequent logic blocks
• Create base queries for core metrics
• Build staging queries for transformations
• Layer queries for specific needs
• Reference other queries as building blocks
• Think in components, not monoliths

Parameterize Your Filters

• Replace hard-coded values with variables
• Use WHERE clauses with placeholders
• Define parameters for date ranges
• Allow dynamic selection of categories
• Enable user-driven filtering
• Parameters make queries flexible
• BI tools can supply parameter values

BI Tool Parameters

• Power BI uses M language parameters
• Tableau has parameter controls
• Pass parameters to SQL queries
• Filter data at the source
• Improve performance by reducing data
• Create interactive user experiences
• Keep logic in the database layer

Performance: The Big Challenge

• BI tools are not databases
• They pull data into their engine
• Large datasets cause slow refreshes
• Complex joins can bottleneck
• Network latency adds to load times
• User interactions require quick responses
• Poor performance leads to dashboard abandonment

Push Work to the Database

• Let the database do the heavy lifting
• Filter and aggregate data in SQL
• Avoid importing raw, massive tables
• Use WHERE clauses to reduce rows
• Use GROUP BY to aggregate early
• Databases are built for this work
• BI tools are built for visualization

Optimize Your SQL

• Index columns used in JOINs and WHERE
• Avoid SELECT *; specify only needed columns
• Use efficient JOIN types
• Analyze query execution plans
• Break extremely complex queries into steps
• Consider materialized views for slow aggregates
• Test query performance independently

Strategic Data Import

• Import aggregated data, not granular
• Schedule full refreshes during off-hours
• Use incremental refresh strategies
• Only refresh data that has changed
• Balance freshness with performance needs
• Cache data when appropriate
• Understand your tool's import options

The Ideal Workflow

• Write a modular, parameterized SQL query
• Test performance in the database
• Connect to BI tool with parameters
• Import only the necessary data
• Build visuals on a lean dataset
• Share an interactive, fast dashboard
• Maintain and update queries centrally

Your Action Plan

• Audit existing queries for reuse potential
• Identify common filters to parameterize
• Create a library of base queries
• Establish naming and documentation standards
• Test performance with sample parameters
• Train the team on the new approach
• Start your next project with this workflow

Key Takeaways

• Reusable queries save time and reduce errors
• Parameters enable flexibility and interactivity
• Always filter and aggregate at the source
• Database performance is dashboard performance
• A structured workflow is a efficient workflow
• Small optimizations compound into huge gains
• Empower your users with fast, reliable data