The guide explores strategies for improving data efficiency by focusing on metrics, frameworks, and technical techniques to optimize data workflows and reduce costs. It highlights the importance of maximizing the value of data by minimizing waste in storage, compute, and time, and emphasizes the need for efficient workflows to speed up insights and decision-making. Key metrics include storage utilization, query latency, cost-per-workload, and time-to-insight, while frameworks such as cost-model and data lifecycle frameworks help evaluate data efficiency. Techniques like ELT pushdown, incremental processing, data compression, and parallel pipeline execution are recommended to enhance efficiency, supported by tools like Hex. The guide also stresses the significance of fostering a culture of data efficiency within organizations by promoting visibility, encouraging purposeful experimentation, and rewarding efficient behaviors to achieve faster, smarter decisions and reduced operational costs.