Boost Data Efficiency with Metrics, Frameworks & Hex

This post details how to measure and improve data efficiency by defining key metrics (storage utilization rate, query latency, cost-per-workload, total cost of ownership, data duplication rate, time-to-insight, data freshness) and frameworks (cost-model, data lifecycle, utilization efficiency, team productivity, storage vs compute trade-offs, pipeline observability). It also outlines techniques for improving efficiency: ELT pushdown, incremental data processing, data compression, data partitioning, parallel pipeline execution, and query execution plan tuning.