People, Data, and Learning

Machines learn from data.

People learn from data.

People learn from people.

As we know, people engage with data in a variety of ways:

• Strategize – Define the vision, goals, and purpose for data use.
• Architect – Design scalable, efficient data systems and pipelines.
• Collect – Gather raw data from various sources.
• Transform – Clean, structure, and prepare data for use.
• Enrich – Add context, making data more meaningful.
• Store – Securely maintain data for accessibility and longevity.
• Govern – Ensure quality, security, and compliance.
• Describe – Summarize trends, patterns, and past events.
• Analyze – Extract insights and valuable patterns.
• Interpret – Make sense of the findings in context.
• Label – Assign meaning for machine learning models.
• Visualize – Communicate insights effectively.
• Model – Simulate scenarios and explore possibilities.
• Predict – Forecast likely outcomes based on trends.
• Prescribe – Recommend actions based on insights.
• Automate – Enable intelligent systems to take action at scale.
• Decide – Drive business impact through informed choices.

How does your team embrace continuous learning and action with data?

How often do teams in your organization reflect on what data is teaching them—not just what it’s reporting?

• Regularly—we prioritize learning from outcomes
• Occasionally—when time permits
• Rarely—data is used more for tracking than learning
• I’m not sure

What best describes how learning from data is shared across your organization?

• Learning is documented and shared across teams
• Insights are discussed within teams but not shared widely
• There’s no structured way to share what we learn from data
• Not applicable—we don’t focus on learning patterns

What’s the biggest challenge to learning from data in your organization?

• Focus is on reporting, not reflection
• Lack of cross-functional dialogue or learning spaces
• Limited time or resources to analyze meaningfully
• Unclear what learning should look like from data