Evidence-Based Management: How Agile Forecasting and EBM Lead to Better Outcomes

Evidence-Based Management (EBM) is a framework that helps organizations make better decisions by using empirical data to evaluate and improve performance. Created by the creators of Scrum, it pairs perfectly with Scrum and focuses on measuring value delivery through Key Value Areas such as Current Value, Time-to-Market, and Unrealized Value, ensuring decisions are grounded in evidence rather than assumptions. By continuously inspecting and adapting, EBM supports incremental improvements, enabling teams to deliver measurable outcomes aligned with business goals.

EBM ties into Agile Forecasting by providing a framework for using empirical data to guide decision-making, measure progress, and forecast outcomes. EBM focuses on outcomes and the evidence of value delivered, making it a natural complement to Agile Forecasting, which uses data and probabilistic methods to predict future performance.

Here’s how they align:

1. Focusing on Value Delivery

EBM emphasizes maximizing value delivery rather than focusing solely on outputs. Agile Forecasting aligns with this by helping teams predict when and how much value they can deliver based on historical data and flow metrics. For instance:

• Throughput forecasting can estimate how many high-value features will be delivered by a target date.
• Monte Carlo simulations can predict the likelihood of delivering a prioritized set of features within specific timeframes, ensuring that work aligns with business goals.

2. Data-Driven Decision Making

EBM relies on empirical evidence (e.g., Key Value Areas like Current Value, Time-to-Market, and Unrealized Value) to make informed decisions. Similarly, Agile Forecasting uses historical data (e.g., throughput, cycle time) to make predictions. This shared reliance on data ensures:

• Teams prioritize work that maximizes value (e.g., delivering features faster to meet customer needs).
• Forecasting helps estimate the Time-to-Market for specific features or product increments, allowing stakeholders to understand delivery timelines.

3. Continuous Improvement through Metrics

Both EBM and Agile Forecasting depend on continuous measurement and feedback loops to improve processes and outcomes. For example:

• Agile Forecasting uses metrics like cycle time, WIP, and throughput to refine predictions over time.
• EBM incorporates these metrics into broader Key Value Areas to evaluate how process improvements impact business outcomes, such as reducing delays (Time-to-Market) or increasing stakeholder satisfaction (Current Value).

4. Risk Management

In EBM, understanding risks is crucial for managing Unrealized Value (the gap between potential and actual value delivered). Agile Forecasting supports risk management by:

• Providing probabilistic forecasts that show the likelihood of meeting specific delivery targets, allowing teams to plan for variability.
• Highlighting bottlenecks and inefficiencies in delivery processes through flow metrics, which can help identify areas where delays could jeopardize value delivery.

5. Transparency and Stakeholder Confidence

EBM encourages transparency by using metrics to communicate progress and value delivery. Agile Forecasting supports this by:

• Offering clear, data-driven forecasts that stakeholders can trust.
• Demonstrating probabilities and scenarios (e.g., "There’s an 85% chance of delivering 15 features by the end of the quarter") to manage expectations effectively.

By combining EBM’s focus on delivering measurable value with Agile Forecasting’s probabilistic methods, organizations can:

• Improve predictability while maintaining flexibility.
• Align team efforts with strategic goals.
• Use real-world data to guide decision-making and deliver incremental value.

Together, these practices create a virtuous cycle of data-driven improvement, ensuring teams deliver the right value at the right time.