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How to Make Numbers more Visual

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Ray Poynter shares how to make numbers more visual through using simple techniques such as hiding decimal places, using colour coding, sorting, and indices.

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How to Make Numbers more Visual

  1. 1. Making Numbers More Visual Ray Poynter The Future Place October 2018 How to Make Numbers More Visual Ray Poynter – October 2018
  2. 2. Today we will start with an example, idenCfy the key learning points, show them in a second example and sum up. Agenda •  Example 1 •  Key learning points •  Example 2 •  Review •  QuesCons and Answers
  3. 3. Data from ITU showing mobile phones per 100 people. The decimal places obscure the message. ITU Mobile Phones Per 100 People   2010 2011 2012 2013 2014 2015 2016 2017 Australia 101.7176 105.8239 106.6434 107.7288 106.7534 108.2793 110.0521 112.6886 Cambodia 56.9635 94.6286 129.2907 134.8927 133.9288 134.3668 126.3484 116.0421 China 63.1734 72.1219 80.8723 88.8862 92.5173 92.4809 97.2521 104.5817 Fiji 81.1582 83.8440 98.3073 105.7622 98.9129 108.2723 116.2363 114.1814 Hong Kong, China 196.3458 216.4354 230.6025 237.4339 235.7260 230.8188 240.7962 249.0243 Indonesia 87.1213 101.6679 113.2915 124.2805 127.6139 131.2928 147.6640 173.8402 Japan 95.9046 103.3117 109.8912 115.2554 123.1687 125.4618 130.6103 133.4504 Korea (Rep. of) 102.4507 105.5526 107.3514 108.9928 113.7039 116.4871 120.6797 124.8645 Malaysia 120.4409 128.0291 141.6663 144.7652 148.6323 143.5530 139.3678 133.8798 Myanmar 1.1843 2.4600 7.3149 13.2801 55.9072 78.2268 95.6532 89.8458 New Zealand 107.7788 109.0901 110.1675 105.5002 111.6780 121.3558 124.4413 136.0019 Philippines 88.7156 98.8579 105.2771 104.4095 111.2123 115.8497 116.2376 110.3956 Singapore 145.5308 150.5849 153.0576 157.4027 148.7388 148.7391 150.4805 148.2402 Thailand 106.7216 114.6881 125.3052 137.7235 141.9184 149.9353 173.7771 176.0347 Viet Nam 126.1072 142.3556 145.5732 135.2335 147.1157 128.5904 127.5261 125.6177 Source: ITU hQps://www.itu.int/
  4. 4. Hiding the decimal places makes the numbers more visible. Do not delete the decimal places, avoid displaying them. ITU Mobile Phones Per 100 People   2010 2011 2012 2013 2014 2015 2016 2017 Australia 102 106 107 108 107 108 110 113 Cambodia 57 95 129 135 134 134 126 116 China 63 72 81 89 93 92 97 105 Fiji 81 84 98 106 99 108 116 114 Hong Kong, China 196 216 231 237 236 231 241 249 Indonesia 87 102 113 124 128 131 148 174 Japan 96 103 110 115 123 125 131 133 Korea (Rep. of) 102 106 107 109 114 116 121 125 Malaysia 120 128 142 145 149 144 139 134 Myanmar 1 2 7 13 56 78 96 90 New Zealand 108 109 110 106 112 121 124 136 Philippines 89 99 105 104 111 116 116 110 Singapore 146 151 153 157 149 149 150 148 Thailand 107 115 125 138 142 150 174 176 Viet Nam 126 142 146 135 147 129 128 126
  5. 5. Excel’s condiConal formaTng can reveal paQerns. ITU Mobile Phones Per 100 People   2010 2011 2012 2013 2014 2015 2016 2017 Australia 102 106 107 108 107 108 110 113 Cambodia 57 95 129 135 134 134 126 116 China 63 72 81 89 93 92 97 105 Fiji 81 84 98 106 99 108 116 114 Hong Kong, China 196 216 231 237 236 231 241 249 Indonesia 87 102 113 124 128 131 148 174 Japan 96 103 110 115 123 125 131 133 Korea (Rep. of) 102 106 107 109 114 116 121 125 Malaysia 120 128 142 145 149 144 139 134 Myanmar 1 2 7 13 56 78 96 90 New Zealand 108 109 110 106 112 121 124 136 Philippines 89 99 105 104 111 116 116 110 Singapore 146 151 153 157 149 149 150 148 Thailand 107 115 125 138 142 150 174 176 Viet Nam 126 142 146 135 147 129 128 126
  6. 6. SorCng the data reveals paQerns. You will oVen need to sort data to see more of the meaning. ITU Mobile Phones Per 100 People   2010 2011 2012 2013 2014 2015 2016 2017 Hong Kong, China 196 216 231 237 236 231 241 249 Thailand 107 115 125 138 142 150 174 176 Indonesia 87 102 113 124 128 131 148 174 Singapore 146 151 153 157 149 149 150 148 New Zealand 108 109 110 106 112 121 124 136 Malaysia 120 128 142 145 149 144 139 134 Japan 96 103 110 115 123 125 131 133 Viet Nam 126 142 146 135 147 129 128 126 Korea (Rep. of) 102 106 107 109 114 116 121 125 Cambodia 57 95 129 135 134 134 126 116 Fiji 81 84 98 106 99 108 116 114 Australia 102 106 107 108 107 108 110 113 Philippines 89 99 105 104 111 116 116 110 China 63 72 81 89 93 92 97 105 Myanmar 1 2 7 13 56 78 96 90
  7. 7. Set 2010 as 100 to index the data. ITU Mobile Phones Per 100 People Using An Index Set 2010 as Index 2010 2010 2011 2012 2013 2014 2015 2016 2017 Australia 102 100 104 105 106 105 106 108 111 Cambodia 57 100 166 227 237 235 236 222 204 China 63 100 114 128 141 146 146 154 166 Fiji 81 100 103 121 130 122 133 143 141 Hong Kong, China 196 100 110 117 121 120 118 123 127 Indonesia 87 100 117 130 143 146 151 169 200 Japan 96 100 108 115 120 128 131 136 139 Korea (Rep. of) 102 100 103 105 106 111 114 118 122 Malaysia 120 100 106 118 120 123 119 116 111 Myanmar 1 100 208 618 1,121 4,721 6,605 8,077 7,586 New Zealand 108 100 101 102 98 104 113 115 126 Philippines 89 100 111 119 118 125 131 131 124 Singapore 146 100 103 105 108 102 102 103 102 Thailand 107 100 107 117 129 133 140 163 165 Viet Nam 126 100 113 115 107 117 102 101 100
  8. 8. Again, sorCng & colouring reveals paQerns. SubtracCng 100 gives the % increase – Singapore +2% ITU Mobile Phones Per 100 People Sort and Colour 2010 2010 2011 2012 2013 2014 2015 2016 2017 Myanmar 1 100 208 618 1,121 4,721 6,605 8,077 7,586 Cambodia 57 100 166 227 237 235 236 222 204 Indonesia 87 100 117 130 143 146 151 169 200 China 63 100 114 128 141 146 146 154 166 Thailand 107 100 107 117 129 133 140 163 165 Fiji 81 100 103 121 130 122 133 143 141 Japan 96 100 108 115 120 128 131 136 139 Hong Kong, China 196 100 110 117 121 120 118 123 127 New Zealand 108 100 101 102 98 104 113 115 126 Philippines 89 100 111 119 118 125 131 131 124 Korea (Rep. of) 102 100 103 105 106 111 114 118 122 Malaysia 120 100 106 118 120 123 119 116 111 Australia 102 100 104 105 106 105 106 108 111 Singapore 146 100 103 105 108 102 102 103 102 Viet Nam 126 100 113 115 107 117 102 101 100
  9. 9. Charts are clearer, but show less data. This sorted chart shows 2017 very clearly – Hong Kong stands out. ITU Mobile Phones Per 100 People 249 176 174 148 136 134 133 126 125 116 114 113 110 105 90 Hong Kong, China Thailand Indonesia Singapore New Zealand Malaysia Japan Viet Nam Korea (Rep. of) Cambodia Fiji Australia Philippines China Myanmar Mobile Phone SubscripFons per 100 - 2017
  10. 10. Key Steps 1.  Hide unnecessary decimal places –  Hide them, don’t delete them 2.  Use colour coding 3.  Sort the data 4.  Use indexes 5.  Charts are clearer – but show less informaCon
  11. 11. Data oVen show decimal places – obscuring the informaCon. The data are oVen in quesConnaire order – usually not helpful. Example 2 % Agreeing Brand 1 Beautiful 20.88% Cheap 63.40% Friendly 71.20% Sexy 41.73% Strong 41.67% Traditional 31.66%
  12. 12. Hiding the decimal places, and moving the % sign make the data easier to read. Example 2 % Agreeing Brand 1 Beautiful 20.88% Cheap 63.40% Friendly 71.20% Sexy 41.73% Strong 41.67% Traditional 31.66% % Agreeing Brand 1 Beautiful 21 Cheap 63 Friendly 71 Sexy 42 Strong 42 Traditional 32
  13. 13. Colour coding shows us where the top and boQom is. % Agreeing Brand 1 Beautiful 21 Cheap 63 Friendly 71 Sexy 42 Strong 42 Traditional 32 Example 2
  14. 14. SorCng the data makes the story clear. Brand 1 is Friendly and Cheap, but it is not TradiConal or BeauCful. Example 2 % Agreeing Brand 1 Beautiful 21 Cheap 63 Friendly 71 Sexy 42 Strong 42 Traditional 32 % Agreeing Brand 1 Friendly 71 Cheap 63 Sexy 42 Strong 42 Traditional 32 Beautiful 21
  15. 15. Some people prefer charts, and the same rules apply about percentages and sorCng. Example 2 20.88% 63.40% 71.20% 41.73% 41.67% 31.66% BeauCful Cheap Friendly Sexy Strong TradiCon Brand 1 - % Agreeing
  16. 16. Brand 1 is Friendly and Cheap, but it is not BeauCful and TradiConal. Example 2 20.88% 63.40% 71.20% 41.73% 41.67% 31.66% BeauCful Cheap Friendly Sexy Strong TradiCon Brand 1 - % Agreeing 71 63 42 42 32 21 Friendly Cheap Sexy Strong TradiCon BeauCful Brand 1 - % Agreeing
  17. 17. Let’s quickly consider a more complicated case. Example 2b % Agreeing Beautiful Cheap Friendly Sexy Strong Traditional Brand 1 20.88% 63.40% 71.20% 41.73% 41.67% 31.66% Brand 2 64.50% 22.16% 40.02% 87.21% 96.24% 61.31% Brand 3 53.86% 48.80% 30.08% 71.52% 27.88% 28.12% Brand 4 93.97% 9.70% 19.89% 97.23% 96.99% 98.19% Brand 5 88.36% 12.73% 34.63% 92.70% 30.03% 26.98% Brand 6 20.61% 60.82% 55.34% 30.58% 75.00% 64.70% Brand 7 1.93% 72.02% 83.76% 11.47% 59.86% 28.94%
  18. 18. Simple story, 2 groups of brands. One (4,2,5,3) Sexy & BeauCful. Two (6,7,1) Friendly and Cheap. No advanced analyCcs. Example 2b % Agreeing Sexy Beautiful Traditional Strong Friendly Cheap Brand 4 97 94 98 97 20 10 Brand 2 87 65 61 96 40 22 Brand 5 93 88 27 30 35 13 Brand 3 72 54 28 28 30 49 Brand 6 31 21 65 75 55 61 Brand 7 11 2 29 60 84 72 Brand 1 42 21 32 42 71 63 Hide decimal places, move % sign, use colour coding. Two-dimensional sorCng, called diagonalisaCon.
  19. 19. Review 1.  Hide unnecessary decimal places –  Hide them, don’t delete them 2.  Use colour coding 3.  Sort the data –  Two dimensional is called diagonalisaCon 4.  Charts are clearer – but show less informaCon
  20. 20. Videos Decimals, Colour & SorCng Indices Available from NewMR.org/PlayAgain
  21. 21. Thank You! Follow me TwiQer: @RayPoynter LinkedIn: www.linkedin.com/in/raypoynter
  22. 22. Q & A BeQy Adamou Research Through Gaming Ray Poynter NewMR Use the Q&A opCon in Zoom to ask your quesCons
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