Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Making sense of data - Learning Lab slides

113 views

Published on

Coalition for Efficiency Learning Lab 13/2/19 - workshop slides

Published in: Business
  • Be the first to comment

  • Be the first to like this

Making sense of data - Learning Lab slides

  1. 1. Making sense of data A “Learning Lab” for Third Sector Organisations Facilitated by: Ian J Seath © 2019 Copyright ISC Ltd.
  2. 2. I have worked with… © 2019 Copyright ISC Ltd.
  3. 3. I help organisations with… ContinuousImprovement Strategy & Planning Project Management Process Improvement Leadership & People Development © 2019 Copyright ISC Ltd.
  4. 4. © 2019 Copyright ISC Ltd.  Some basic principles for displaying data  How to select appropriate types of chart to analyse and present data  Decide how big a sample to choose in order to be confident in the results  Why an “average” could be very misleading  A simple chart to identify priorities and achieve focus Data – Information – Knowledge - Wisdom Tables Reports Charts Infographics Dashboards Interactive& ScrollableCharts DataStories& Visualisations
  5. 5. MOST PEOPLE HATE MATHS! “A Mathematician is a device for turning coffee into theorems.” Paul Erdos (Hungarian Mathematician) © 2019 Copyright ISC Ltd.
  6. 6. © 2019 Copyright ISC Ltd.  An aeroplane flies round the four sides of a 100 mile square  It flies at 100 mph on side 1, 200 mph on side 2, 300 mph on side 3 and 400 mph on side 4.  What is its average speed? 100 m.p.h. 300 m.p.h. 200 m.p.h.400 m.p.h. 100 miles square
  7. 7. The Golden Rules of Measurement  No measurement without recording  No recording without analysis  No analysis without action © 2019 Copyright ISC Ltd.
  8. 8. MAKE NUMBERS “OBVIOUS” “As you can clearly see…” © 2019 Copyright ISC Ltd.
  9. 9.  Which of these is easier to identify the biggest percentage increase in funding types? Restricted funding has increased from £47,250 to £63,210 whereas unrestricted funding increased from £30,150 to £46,430 last year. Restricted funding has increased from £47,000 to £63,000 whereas unrestricted funding increased from £30,000 to £46,000 last year. © 2019 Copyright ISC Ltd.
  10. 10. Badly presented data makes it hard to understand & improve performance  It’s much easier on the eye and to do a bit of mental arithmetic on the second example and say that an increase of £16,000 in restricted funds is about a third (33%) and an increase of £16,000 in unrestricted funds is about half (50%)  Very few people need absolutely precise numbers (Actuaries, Accountants, Scientists and Engineers are common exceptions)  So, for most management information, rounded data will be easier to handle © 2019 Copyright ISC Ltd.
  11. 11. Tip 1: Round to “2 effective digits”  Applied with common sense, rounding to two effective digits usually makes numbers easier to cope with and to get a quicker understanding of what’s going on © 2019 Copyright ISC Ltd. 47000 63000 30000 46000 0 20000 40000 60000 80000 100000 120000 Last year This year Income(£) Restricted & Unrestricted Funding Restricted Unrestricted
  12. 12. Which is easier to read? © 2019 Copyright ISC Ltd. Income (£) Surplus (£) 2019 250,000 24,000 2018 220,000 20,000 2017 180,000 16,000 2016 140,000 10,000 2015 100,000 6,500 2015 2016 2017 2018 2019 Income (£) 100,000 140,000 180,000 220,000 250,000 Surplus (£) 6,500 10,000 16,000 20,000 24,000 A B
  13. 13. Tip 2: Columns of data are almost always easier to read than rows  Put the latest data, or the biggest numbers, at the top of the table  N.B. You may not be able to do this if the data is time-based  Columns of data allow the eye to scan up and down more easily © 2019 Copyright ISC Ltd.
  14. 14. How would you improve this? © 2019 Copyright ISC Ltd. Project spending (£k) Q1 Q2 Q3 Q4 Project A 34.4 32.1 27.7 32.2 Project B 148.6 139.6 144.3 166.5 Project C 305.7 284.4 245.3 377.8 Project D 25.8 29.2 24.9 27.8 Project E 256.7 242.1 212.9 243.0 Project F 68.5 73.3 67.9 84.6
  15. 15. Better? © 2019 Copyright ISC Ltd. Project spending (£k) Q1 Q2 Q3 Q4 Total Project C 310 280 250 380 1220 Project E 260 240 210 240 950 Project B 150 140 140 170 600 Project F 69 73 68 85 295 Project A 34 32 28 32 126 Project D 26 29 25 28 108 Total 849 794 721 935 3299
  16. 16. Which chart(s) would you use? © 2019 Copyright ISC Ltd. 0 50 100 150 200 250 300 350 400 Project C Project E Project B Project F Project A Project D £(k) Project Spending (£) Q1 Q2 Q3 Q4 0 200 400 600 800 1000 Q1 Q2 Q3 Q4 £(k) Project Spending (£) Project C Project E Project B Project F Project A Project D 0 100 200 300 400 Q1 Q2 Q3 Q4 £(k) Project Spending (£) Project C Project E Project B Project F Project A Project D
  17. 17. What can you conclude from this? © 2019 Copyright ISC Ltd. # of Beneficiary requests Q1 Q2 Q3 Q4 Support Type A 370 350 320 350 Support Type B 160 150 150 180 Support Type C 47 51 46 63 Support Type D 42 40 36 40 What else would you want to know?
  18. 18. Better, plus charts? © 2019 Copyright ISC Ltd. # of Beneficiary requests Q1 Q2 Q3 Q4 Total Average Support Type A 370 350 320 350 1390 348 Support Type B 160 150 150 180 640 160 Support Type C 47 51 46 63 207 52 Support Type D 42 40 36 40 158 40 Total 619 591 552 633 2395 Average 155 148 138 158 0 100 200 300 400 Q1 Q2 Q3 Q4 Beneficiary Requests Support Type A Support Type B Support Type C Support Type D 0 50 100 150 200 250 300 350 400 Support Type A Support Type B Support Type C Support Type D Beneficiary Requests Q1 Q2 Q3 Q4
  19. 19. Tip 3: When to use Tables for data  Use tables when you have ten, or fewer data points, or if you need people to see the exact numerical values in your results  Round the data to two effective digits unless readers need the precise numbers © 2019 Copyright ISC Ltd.
  20. 20. Charts or Tables? - Summary Charts Tables Fewer than 6 data points  7 – 10 data points   More than 10 data points  Need to see individual values  Need to show trends over time  Need to show the distribution / variation in data  More than 1 independent variable  © 2019 Copyright ISC Ltd.
  21. 21. HOW MUCH DATA IS ENOUGH? “Anecdotes are not statistics.” © 2019 Copyright ISC Ltd.
  22. 22. Sampling  In many cases, we obtain data through sampling; often because it is simply not possible to measure every single item, or to log every activity, transaction or contact  The purpose of sampling is to collect an unbiased subset which will give you a manageable amount of data  When you take samples, they should be representative (statistically valid and reliable) and economic to collect (quick and cost-effective) © 2019 Copyright ISC Ltd.
  23. 23. Population vs. Sample © 2019 Copyright ISC Ltd. Beneficiary Satisfaction Unhappy Happy If we surveyed every single beneficiary over a year to find out how happy they were with our support, this is what we might find.
  24. 24. Contact Centre’s sample of 10 people © 2019 Copyright ISC Ltd. Customer Satisfaction Unhappy Happy How happy are our beneficiaries according to our staff?
  25. 25. Volunteers’ sample of 10 people © 2019 Copyright ISC Ltd. Customer Satisfaction Unhappy Happy How happy are our beneficiaries according to our volunteers?
  26. 26. Validation: depends on sample size © 2019 Copyright ISC Ltd. Customer Satisfaction Unhappy Happy Your ability to validate beneficiary satisfaction data depends on sample size. If you pick too small a sample you could, purely by chance, find very different results and draw the wrong conclusions.
  27. 27. © 2019 Copyright ISC Ltd. http://www.surveysystem.com/sscalc.htm
  28. 28. If you have 6000 beneficiaries per year © 2019 Copyright ISC Ltd. + or - 3 500 beneficiaries/month 75 beneficiaries/month You might, therefore, say if 83% of beneficiaries are ‘Happy’: “We are 95% confident that between 80 and 86% of beneficiaries are Happy” You can also work out the CI for a known sample size
  29. 29. Terms you need to understand  Confidence Interval (Margin of Error)  The plus-or-minus figure usually reported in newspaper or television opinion poll results  If you pick a CI of 5 and 83% of your sample picks ‘Happy’, you can be “sure” that the 78-88% of the entire population would have picked ‘Happy’  Confidence Level  Tells you how “sure” you can be that the population would pick an answer within the Confidence Interval  A 95% CL is most commonly used and means, for the example above, you can be 95% sure that the true population is between 78 and 88% © 2019 Copyright ISC Ltd.
  30. 30. Tip 4: Sampling guidelines  With static populations (e.g. customers, staff), use random sampling; for example using Random Number Tables to decide what (and when) to sample  Random sampling means that every unit in a population will have an equal probability of being chosen in the sample  With time-based data, collect data in sub-groups of 5 values, equally spaced in time (e.g. services are delivered, or transactions are carried out continuously over a period of time – call handling in a contact centre)  If it is not feasible to take sub-groups, take individual values at regular intervals; e.g. every 10th or 100th © 2019 Copyright ISC Ltd.
  31. 31. THE “MISLEADING” AVERAGE “Lies, damned lies and statistics.” © 2019 Copyright ISC Ltd.
  32. 32. © 2019 Copyright ISC Ltd.
  33. 33. Do you know what “average” means?  The length of time (in days) taken for 10 grant applications to be processed was recorded  What was the average time it took (from application received to completion)? © 2019 Copyright ISC Ltd. Grant 1 Grant 2 Grant 3 Grant 4 Grant 5 Grant 6 Grant 7 Grant 8 Grant 9 Grant 10 6 6.5 7 7 7 7.5 8 8 10 13
  34. 34. Mean, Median and Mode  Arithmetic Mean - the sum of values divided by the number of values, often called “the average” (8.0 in our example)  Median - the middle value when all the values are arranged in order [or the mean of the two middle values if there is an even number in the list] (7.25 in our example)  Mode - the most frequently occurring value (7 in our example) If the Mean = the Median, the data is distributed symmetrically The Median and Mode are not affected by extreme values in a set of data, unlike the Mean © 2019 Copyright ISC Ltd.
  35. 35. Which “average” would you use & why? © 2019 Copyright ISC Ltd. 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 No.ofcases Time to repond (Days) Time to respond to Grant Application (Days) N = 33 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 No.ofcases Time to repond (Days) Time to respond to Grant Application (Days) N = 33 A B
  36. 36. Some more questions…  Which one would you want to be held accountable for managing?  Where would you set a Service Level Agreement? © 2019 Copyright ISC Ltd. 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 No.ofcases Time to repond (Days) Time to respond to Grant Application (Days) N = 33 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 No.ofcases Time to repond (Days) Time to respond to Grant Application (Days) N = 33 Mean Median Mean Median 3.6 3 4.7 5
  37. 37. You also need to understand Variation © 2019 Copyright ISC Ltd. Bell-shaped Skewed PlateauBi-modal
  38. 38. What a Histogram might tell you  Bell-shaped - a symmetrically shaped distribution which typically represents data randomly distributed, but clustered around a central value  Positive or negative skews - where the average value of the whole set of data is to the left (-) or right (+) of the central value. Look out for specification limits at the boundaries of the distribution which might be causing data to be dropped from the population. More extreme shapes are also known as “precipices”  Bimodal - where there are two peaks. Usually indicates two sets of data (e.g. two teams or locations), with different Means have been mixed  Plateau - occurs where several sets of data have been mixed (e.g. from a number of customers/locations/groups) © 2019 Copyright ISC Ltd.
  39. 39. GRAPHS AND CHARTS “A picture paints a thousand words.” © 2019 Copyright ISC Ltd.
  40. 40. Graphs and Charts © 2019 Copyright ISC Ltd.
  41. 41. Tip 5: When to use Graphs for data  Use graphs when you have more than ten data points, or if you want to show people “the big picture”, not detailed data  Use graphs when you need to show trends, over time  Don’t clutter a graph with too many different sets of data; it’s usually better to split the data into separate graphs © 2019 Copyright ISC Ltd.
  42. 42. Pie Charts  The data points in a Pie Chart are displayed as a percentage of the whole pie  Good for: showing proportions, at a glance  Not good for: showing trends or comparisons over time © 2019 Copyright ISC Ltd.
  43. 43. Bar Charts  In Bar Charts, categories are typically organised along the horizontal axis and values up the vertical axis  Bar Charts illustrate comparisons among individual items and may be “stacked” or “100% stacked”  Good for: showing quantities of responses in different categories; often best when sorted into biggest to smallest  Not good for: showing trends over time (use a Line Graph) © 2019 Copyright ISC Ltd.
  44. 44. Histograms  In Histograms, a variable (e.g. Time) is displayed along the horizontal axis and frequency up the vertical axis  Good for: showing the variation in a set of data and to help decide if the Mean or Median are the best choice of average to quote  Not good for: showing variations over time  N.B. Excel also calls these “Bar Charts” © 2019 Copyright ISC Ltd.
  45. 45. PARETO ANALYSIS “Separate the vital few from the trivial many.” © 2019 Copyright ISC Ltd.
  46. 46. Pareto Analysis © 2019 Copyright ISC Ltd. 20% 80%  80% of problems or errors are often due to only 20% of the causes (The “Vital Few”)  The remaining 80% of causes account for only 20% of the problems or errors (The “Trivial Many”) CausesProblem Occurrences Also known as the 80:20 rule 20% 80% The “Vital Few” Causes The “Trivial Many” Causes Most of the problems
  47. 47. Pareto Diagram  A Pareto Diagram is a particular type of Bar Chart  Category data is presented in decreasing size, from left to right and a Cumulative % line is also drawn  Good for: showing the 80:20 Rule – highlighting the few categories that account for the majority of performance or issues  Not good for: showing data over time (but sometimes worth showing “before” and “after”) © 2019 Copyright ISC Ltd.
  48. 48. Example: Sources of Income © 2019 Copyright ISC Ltd. Inc. (£k) Cum. £k Inc. (%) Cum. % National Lottery 241 241 47% 47% Trusts & Foundations 110 351 21% 69% Fundraising events 44 395 9% 77% Local Authority Grant 31 426 6% 83% 1-off donations 28 454 5% 89% Commercial sponsors 18 472 4% 92% Regular individual donations 15 487 3% 95% Merchandise 13 500 3% 98% Major donors 7 507 1% 99% Legacies 5 512 1% 100% Total 512
  49. 49. Pareto Diagram © 2019 Copyright ISC Ltd.
  50. 50. IS PERFORMANCE IMPROVING? “Two data points do not indicate a trend.” © 2019 Copyright ISC Ltd.
  51. 51. Line Graphs  In a Line Graph, time data is distributed evenly along the horizontal axis, and all value data is distributed up the vertical axis  Good for: showing how results have changed over time (trends)  Not good for: comparing lots of different sets of results (too many lines make it hard to see what's going on)  N.B. Excel enables you to overlay a statistically derived trend line © 2019 Copyright ISC Ltd.
  52. 52. What can you conclude from this data? © 2019 Copyright ISC Ltd. Is weekly caseload increasing, decreasing, or not changing? Week New Cases Week New Cases 1 35 11 45 2 79 12 37 3 125 13 102 4 85 14 47 5 60 15 52 6 3 16 16 7 138 17 9 8 120 18 86 9 116 19 60 10 40 20 66
  53. 53. 4 Week Moving Average © 2019 Copyright ISC Ltd. Weekly caseload is decreasing
  54. 54. 4 Week Moving Average © 2019 Copyright ISC Ltd. Week New Cases Week New Cases 1 35 11 45 2 79 12 37 3 125 13 102 4 85 14 47 5 60 15 52 6 3 16 16 7 138 17 9 8 120 18 86 9 116 19 60 10 40 20 66 Quick & dirty: Weeks 1-10 average = 80 Weeks 11-20 average = 52
  55. 55. WORKSHOP REVIEW “Learning points for action at work?” © 2019 Copyright ISC Ltd.
  56. 56. © 2019 Copyright ISC Ltd. 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

×