Good metrics can help a team continuously improve and deliver consistently, while poor metrics can create a culture of fear; this video shows how to measure Agile teams effectively and help create an environment that maximizes value and learning.
Finding the right balance of metrics for your Agile teams can significantly help your continuous improvement efforts, forecasting, and risk management on software development projects. Instilling curiosity about performance data—instead of dreading arbitrary performance goals—is the best way to promote a culture of continuous improvement. This video will guide you through how best to incorporate metrics into your teams and organizations to diagnose issues, unearth trends, predict issues, and apply historical results to prescribe preventative actions. You will also learn multiple techniques and approaches for creating data visualizations that make your metric reporting clear and impactful.
Learn How To
- Define a metric in a way to best observe and influence team behavior and avoid unwanted side effects.
- Effectively use standard deviation, thresholds, and boundaries to better understand your data trends and better identify potential risks.
- Understand the four types of data analytics (informative, diagnostic, predictive, and prescriptive) to get the most out of your metrics.
- Build and use a balanced metrics dashboard to create a culture of continuous improvement that maximizes value and learning.
- Create and define metrics that will influence team behaviors that maximize value and learning.
- Use metrics more effectively by incorporating the four types of data analytics: informative, diagnostic, predictive, and prescriptive.
- Create a balanced dashboard that can offer critical insights into how your teams are performing, feeling, and producing.
Lesson 1: Why We Measure: This lesson discusses the underlying drivers for our data analytics and the importance of awareness and intent when measuring data. It’s important to approach measuring data with the right intent. A common negative consequence of measuring data with the wrong intent is creating a culture of fear.
Lesson 2: Defining a Metric: This lesson explores what is a good metric, how to use simple questions to best outline what to measure, and how to avoid some common pitfalls when working with data.
Lesson 3: Descriptive Data Analytics: This lesson describes the first step to creating metrics, which is to collect and visualize historical data to see what’s been happening with your team. Lesson 3 is a perfect example of the quote, “A picture maybe worth a thousand words but a good report is worth a thousand data points.”
Lesson 4: Diagnostic Data Analysis: This lesson explores the importance of understanding and uncovering why certain things happen with your team. Lesson 4 gives you behind-the-scenes kind of experience.
Lesson 5: Predictive Data Analytics: This lesson shows about how to get a decent idea of what’s likely to happen within your team since the future is unpredictable. The instructor gives tips and tricks of using empirical data and simple math to get ahead of potential issues that might arise going forward.
Lesson 6: Prescriptive Data Analytics: This lesson shows how to analyze your data, identify trends therein, and make informed decisions for your team based on your analysis.
Lesson 7: Creating a Balanced Dashboard: This lesson covers the importance of considering the full landscape of collected data, and how to find the best mix of connective metrics to inform your analysis.
Lesson 8: Instilling a Culture of Continuous Improvement: This lesson shows how to use data safely while maintaining the freedom to experiment. You will learn to approach metrics in a way that inspires curiosity and not fear.
Lesson 9: Agile Metrics in Action: This lesson consists of real-world examples of topics and tools covered in previous lessons, and is intended to help you understand how metrics work in agile teams and organizations.
Table of Contents
1 Agile Metrics Applied Introduction
2 Agile Metrics Applied Summary
4 How Metrics Correspond to Outcomes
5 Qualities of a Good Metric
6 Collecting Data
7 Investigating Why
8 Measuring Variability
9 Identifying Data Trends
10 Setting Up Guardrails
11 Data-Driven Improvement
12 End-to-end Metrics Demo
13 How Metrics Influence Behavior
14 Creating a Metric Questionnaire
15 Visualizing Data
16 Drilling Down in Detail
17 Forecasting with Empirical Data
18 Data Informed Guidance
19 Defining a Metrics Quadrant
20 Avoiding a Culture of Fear
21 Common Metric Pitfalls
22 Thresholds and Boundaries
23 Trending Dashboards