Combining Past and Present Data to Inspire Action
How do you know you're making the right decision?
Historical data is a key component of data-driven decision-making. By examining where you've been and what worked (or didn't), you can make smarter decisions that align with your future goals.
In the series introduction, we covered the importance of having a frame of reference as well as the difference between past, present, and future data.
In this section, we'll explore how comparing historical and current data can reveal valuable insights about your trajectory and inform your next steps and how you can summarize historical data into key findings.
Past to Present: How did we get here?
In the context of timeframes, examining past to present data allows us to see how things have evolved over time, identifying trends, patterns, or events that have occurred that have led to the current state of things.
By evaluating how we got from the past (point A) to the present (point B), we can identify opportunities for improvement or areas that need to be addressed.
Question to ask when analyzing Past to Present data
How has the data evolved over time?
Has it increased, decreased, or stayed the same?What trends or patterns do you notice?
Are there repeating occurrences or spikes in the data?What are the key drivers or factors behind the observed changes?
Can you identify what caused this change?How can you make use of these observations going forward?
What actions or strategies should be implemented based on what you've learned?
Scenario
In the instance of the socks, you recall buying a six-pack of wool socks last January. How did you go from 6 pair of socks to 2 over the past year?
By examining how we got from the past (point A) to the present (point B), we can identify opportunities for improvement or areas that need to be addressed.
How has the data evolved over time?
The quantity of socks gradually decreased 66% from January 2023 to February 2024.
Need help calculating percentages? Try out this resource: Percent Calculator
What trends of patterns do you notice?
You tend to ‘lose’ a pair during the colder months.
What are the key drivers or factors behind the observed changes?
Your socks wore out from increased use during the colder months. They developed holes along the heels and toes, prompting you to discard them throughout the year.
How can you make use of these observations going forward?
You can opt to replenish your socks as they wear out, ensuring that you maintain the desired quantity throughout the entire year. You could also invest in higher quality socks that can withstand more use.
Inspiring action with data-driven insights
Let’s turn your analysis of past to present data into a statement that provides an actionable step, supported by data.
Structure your statement in 3 parts:
Data observation
Pattern/insight
Actionable recommendation
Include key elements:
Specific metrics and time periods
Clear patterns identified in the data
Documented methodology and sources
External influencing factors
Let’s apply this method to the sock scenario:
"With a 66% decrease in sock inventory over 13 months and consistent loss during winter months, implementing a quarterly sock replacement schedule and investing in higher-quality materials would help maintain optimal inventory levels."
Remember: Your goal is to guide meaningful action through evidence-based recommendations.
Key Takeaways
Historical Data Analysis: Examining past data is crucial for making informed decisions and identifying trends that can shape future strategies.
Past to Present Comparison: Understanding how situations evolved from point A to point B helps identify improvement opportunities and areas needing attention.
Essential Analysis Questions
Chart data evolution over time
Identify patterns and trends
Determine key drivers of change
Plan future actions based on insights
Creating Data-Driven Action Statements
Include specific metrics and timeframes
Document methodology and sources
Consider external factors
Structure statements with observation, insights, and recommendations
Focus on Actionable Insights: The ultimate goal is not just present data, but to use it to guide meaningful improvements and actions supported by evidence.
