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Case Study Retail March 2024

Retail Dashboard Performance Tuning

performance-tuning dataset-redesign sql-optimization power-bi
Anonymized performance review frame

Summary

What changed, in short

Average dashboard load time dropped from 90 seconds to 7 seconds, restoring usability across five departments.

Outcome-first metrics

Outcome-first metrics

Outcome signal

90s to 7s average load time

Load time dropped to 7 seconds through dataset and SQL logic changes

Focus

Dashboard performance tuning

Industry: Retail

My role

Dataset redesign and SQL optimization

Tools: Power BI, SQL, DAX

Problem

What needed to change

A retail analytics dashboard used by five departments loaded in over 90 seconds, making it impractical for daily decision-making.

Context / Constraints

What shaped the work

Industry context: Retail. Primary focus: Performance Tuning. The scope and sequencing were shaped by concrete delivery constraints.

  • 90-second dashboard load time across five departments

Approach

How the work was handled

Redesigned the dataset structure and optimized SQL query logic to remove the primary performance bottlenecks.

Outcome

What changed in practice

Average dashboard load time dropped from 90 seconds to 7 seconds, restoring usability across five departments.

  • Average dashboard load time dropped from 90 seconds to 7 seconds.
  • The optimization applied across analytics used by five departments.
  • The improvement came from dataset redesign and SQL logic changes.

My Role

Where I contributed most

Dataset redesign and SQL optimization

Trade-offs / Lessons

Choices, constraints, and what mattered

  • Redesigned the dataset structure to remove performance bottlenecks.
  • Optimized SQL query logic to improve execution speed.
  • Focused the work on analytics used daily across five departments.

Additional Notes

Extra implementation detail

Highlights

  • Reduced average dashboard load time from 90 seconds to 7 seconds.
  • Optimization applied across analytics used daily by five departments.
  • Improvement came from dataset redesign and SQL logic changes, not infrastructure scaling.

Contact

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