Making sense of data - Learning Lab 2021
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This document provides guidance on making sense of data and effectively communicating insights through visualizations. It discusses challenges organizations face in analyzing large amounts of data and offers tips for selecting appropriate chart types to analyze and present different types of data. Examples include using histograms to show variation, Pareto charts for identifying priorities, and line and moving average charts for trends over time. The goal is to help organizations and individuals at all levels better understand and make decisions based on data.
Making sense of data - Learning Lab 2021
- 1. MAKING SENSE OF DATA A Learning Lab forThird Sector organisations Facilitated by: Ian J Seath © Improvement Skills Consulting Ltd. 2021
- 2. 2 | Contents “Withoutdatayou’rejustanother personwithanopinion.” DrWEdwardsDeming ❑ Some basic principles for displaying data ❑ How to select appropriate types of chart to analyse and present data ❑ Why an “average” could be very misleading ❑ A simple chart to identify priorities and achieve focus ❑ The one chart you need if you want to demonstrate improvement
- 3. 3 | Data-rich, information-poor The way we communicate is changing. In our time- strapped world, we like information that is quick and easy to consume. Most organisations are overwhelmed by data, some of which gets analysed and turned into reports, but all too often senior and front-line managers simply don’t have the skills to get real insight from the data. Perhaps some people are “scared of maths”, others may not have the time and yet others may not see data analysis and interpretation as part of their role. Plus, too many people think that “numbers speak for themselves”. Add to that the manipulation of data by the media and the political spin put on “official statistics” and it’s no wonder that managing by numbers gets a bad press. Organisations large and small are grappling with ever more complex data to improve their performance. They look to staff at all levels to make sense of the numbers, provide insight and guidance. Paradoxically, traditional education and training often ignores simple tools (like Pareto and Run Charts) to make numbers work harder. Training rarely shows you how to communicate what the numbers mean in a compelling, persuasive way. “AMathematicianisadevicefor turningcoffeeintotheorems.” PaulErdos (HungarianMathematician) AS YOU CAN CLEARLY SEE… OR MAYBE YOU CAN’T? Data – Information – Knowledge - Wisdom Tables Reports Charts Infographics Dashboards Interactive & Scrollable Charts Data Stories & Visualisations
- 4. 4 | Charts or Tables? TIPS FOR TABLES: ❑ Round the data to “2 significant digits” – it’s easier to read and most people don’t need absolutely precise numbers ❑ Columns of data are almost always better than rows for making comparisons because the eye can scan up and down more easily ❑ Sorting the biggest numbers to put them at the top of the table is also usually helpful (but not if the data is time-based) ❑ Time-based data should be presented with the oldest at the left and newest at the right One of the main decisions to make about sharing data is whether to use a chart or a table. Here are our guidelines but, remember, sometimes you might need to use both. The eye often finds it easier to scan up and down columns Put the biggest numbers at the top unless your data is time- based
- 5. 5 | Understanding variation TIPS FOR HISTOGRAMS: ❑ In histograms, a variable (e.g. waiting time) is displayed on the horizontal axis and frequency up the vertical axis ❑ Use a histogram to highlight how much variation there is in a process ❑ If the data is skewed, the Mean will not be in the middle and you may need to quote the Median and/or Mode ❑ Look out for bi-modal histograms which might suggest you actually have 2 different data sources with 2 different Means ❑ If the distribution has a “cliff-edge”, some of the data might be missing Beware the misleading “average” Lots of reports quote the average (usually the Mean), but this can be very misleading unless we also know how much variation there is in the data. Mean = 4.7 Median = 5 Mean = 3.6 Median = 3 AHistogramisnotthesameasa BarChart. Barchartsshowcategorical information(e.g.month,region, project)onthehorizontal(x)axis. HISTOGRAMS
- 6. 6 | Common types of Chart PIE CHARTS: ❑ The data points in a Pie Chart are displayed as a percentage of the whole pie ❑ More than 6 slices often makes the chart look cluttered Good for: showing proportions, at a glance Not good for: showing trends or comparisons over time LINE GRAPHS: ❑ If you want to understand any trends in “time series data”, you have to use line graphs (“Run Charts” in Statistical Process Control - SPC - terminology) ❑ Excel enables you to overlay a statistically derived trend line Good for: showing how results have changed over time (trends) Not good for: comparing lots of different sets of data (more than 3 lines makes it hard to see what’s going on) BAR CHARTS: ❑ Categories are typically organised along the horizontal (x) axis and values up the vertical (y) axis ❑ Bar Charts illustrate comparisons among individual items and may be “stacked” or “100% stacked” Good for: showing quantities of responses in different categories; sometimes best when sorted into biggest to smallest (as a Pareto Diagram) Not good for: showing trends over time (use a Line Graph) “Apicturepaintsathousandwords.”
- 7. 7 | ParetoAnalysis The Pareto Principle: ❑ 80% of problems or errors are often due to only 20% of the causes (the “vital few”) ❑ 80% of income often comes from just 20% of the income sources ❑ 80% of demand is often for just 20% of our services or support types Good for: showing the *0-20 Rule; highlighting how a few categories account for the majority of performance Not good for: comparing performance over time (although you can draw a “before” and “after” Pareto Diagram) PARETO DIAGRAM: ❑ A particular type of Bar Chart ❑ Category data is presented in decreasing size, from left to right ❑ A cumulative percentage line may also be drawn Alsoknownasthe80-20Rule
- 8. 8 | Identifying Trends & Improvement PEAKS AND TROUGHS: Sometimes, your time-series data will have lots of peaks and troughs, making it hard to identify any underlying trends. This may be as a result of seasonality (e.g. Summer vs. Winter) or daily/weekly factors (e.g. weekday vs. weekend). You need a way to smooth the peaks and troughs; moving averages enable you to do this. You could also use Excel’s Trend Line feature to overlay any underlying trend. MOVING AVERAGES: ❑ Moving averages take a group of data points and average them ❑ These moving averages are then overlaid on the original Line Graph Monthly Data: calculate a 3-month moving average to smooth out the effect of quarterly differences Weekly Data: calculate a 4- or 5-week moving average to smooth out the effect of monthly differences Daily Data: calculate a 5- or 7-day moving average to smooth out the effect of weekly differences 4 week moving average Quick & dirty: Weeks 1-10 average = 80 Weeks 11-20 average = 52
- 9. 9 | ian.seath@improvement-skills.co.uk 07850 728506 @ianjseath uk.linkedin.com/in/ianjseath Prepared for Measuring the Good and Coalition for Efficiency by Ian J Seath Improvement Skills Consulting Ltd. www.improvement-skills.co.uk