Visualising Data: ISB Solstice 2011

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Presented at Solstice 2011 (http://www.isb.edu/solstice/) at ISB on 16 December 2011 as part of Prof. Galit Shmueli's workshop on Visual Analytics (http://www.isb.edu/VisualAnalytics/)

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<ul><li>1.S AnandVISUALISING DATA</li></ul> <p>2. GramenerA data analytics and visualisation companyWe handle terabyte-size data via non-traditional analytics and visualise it in real-time. Gramener visualises Gramener transforms your data into concise dashboards that make your business problem &amp; solution visually obvious.your dataWe help you find insights quickly, based on cognitive research, and our visualisations guide you towards actionable decisions. 3. WHY VISUALISE?Consider an Organizational2010 BangaloreDelhi Hyderabad MumbaiSales report shown alongside MonthPrice Sales Price Sales Price Sales Price Sales Jan10.0 8.04 10.0 9.14 10.0 7.468.0 6.58It shows performance of 4 Feb 8.0 6.958.0 8.148.0 6.778.0 5.76branches with average priceand sales across 4 citiesMar13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Apr 9.0 8.819.0 8.779.0 7.118.0 8.84Each of the branches changeMay11.0 8.33 11.0 9.26 11.0 7.818.0 8.47prices every month with aJun14.0 9.96 14.0 8.10 14.0 8.848.0 7.04corresponding change in the Jul6.0 7.246.0 6.136.0 6.088.0 5.25sales value Aug 4.0 4.264.0 3.104.0 5.39 19.0 12.50Basic analytics of these Sep12.0 10.8412.0 9.13 12.0 8.158.0 5.56numbers reveal consistentOct 7.0 4.827.0 7.267.0 6.428.0 7.91performanceacross 4Nov 5.0 5.685.0 4.745.0 5.738.0 6.89branches.Average 9.0 7.509.0 7.509.0 7.509.0 7.50Further, these sales figures Variance 10.0 3.75 10.0 3.75 10.0 3.75 10.0 3.75have a consistent Correlationand Linear regression across allcities 4. BECAUSE NUMBERS DONT TELL THE FULL STORYPlotting the same datashows markedly differentbehaviour.Bangaloresaleshasgenerally increased withprice.Hyderabad has a perfectincrease in sales with price,except for one aberration.Delhi, however, shows adecline in sales as price isincreased beyond a certainpoint.Mumbai sales fluctuated alot despite a constant price,except for one month. 5. DETECTING FRAUD We know meter readings are incorrect, for various reasons. We dont, however, have the concrete proof we need to start the process of meter readingENERGY UTILITY automation. Part of our problem is the volume of data that needs to be analysed. The other is the inexperience in tools or analyses to identify such patterns. 6. This plot shows the frequency of all meter readings fromWhy wouldApr-2010 to Mar-2011. An unusually large number ofthese happen? readings are aligned with the tariff slab boundaries.This clearly showsApr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11collusion of some form217 219200 200 200200 200 200200350200 200with the customers. 250 200200 200 201200 200 200250200200 150250 150150 200 200200 200 200200200200 150This happens with specific150 200200 200 200200 200 200200200200 50customers, not randomly.200 200200 150 180150 50100 50 70100 100Here are such customers100 100100 100 100100 100 100100100110 100100 150123 12350100 50100100100100 100meter readings. 0111100 100 100100 100 100100100 50 50 0100 27 10050100 100 100100100 70 100If we define the extent of 111 10099 50 100 100100100100 100fraud as the percentageexcess of the 100 unitmeter reading,Section Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11the value varies Section 170%97% 136% 65%110% 116% 121% 107% 114%88%74% 109%considerablySection 2 66%92% New section66% 87% 70% 64% is and 63%50%58%38%41%54%manager arrivestransferred50% outacross sections, Section 390%46%47% 43% 28% 31%32%19%38% 8%34%Section 4 44%24%36% 39% 21% 18% 24%49%56%44%31%14%and timeSection 5 4% 63% -27% 20%41% 82% 26%34% 43% 2% 37% 15%Section 618% 23%30% 21%28% 33% 39%41% 39%18%0% 33% with someSection 736% 51%33% 33%27% 35% 10%39% 12% 5% 15% 14%explainable Section 822% 21%28% 12%24% 27% 10%31% 13%11% 22% 17%anamolies.Section 919% 35%14%9%16% 32% 37%12%9% 5% -3% 11% 7. MONITORING COSTS Our raw material cost varies considerably across farms, though we share best practices. We have over 5,000 farms. TheCONTRACT raw material cost report is a 75- page Excel report that no one FARMING reads. Also, we gain no insights as to how the productivity changes over time 8. PREDICTING MARKSWhat determines a childs marks?Do girls score better than boys?Does the choice of subject matter?EDUCATION Does the medium of instruction matter?Does community or religion matter?Does their birthday matter?Does the first letter of their name matter? 9. and peaksBased on the results of the 20 lakhfor Sep-bornsstudents taking the Class XII exams The marksat Tamil Nadu over the last 3 years,shoot up for Aug bornsit appears that the month you wereborn in can make a difference of asmuch as 120 marks out of 1,200. 120 marks out of1200 explainableby month of birth June borns score the lowestAn identical pattern was observed in 2009 and 2010Its simply that in Canada the eligibilitycutoff for age-class hockey is January 1. Aboy who turns ten on January 2, then,could be playing alongside someone whodoesnt turn ten until the end of the yearand at that age, in preadolescence, atwelve-month gap in age represents anenormous difference in physical maturity.-- Malcolm Gladwell, Outliers and across districts, gender, subjects, and class X &amp; XII. 10. SECURITIES FINDING PATTERNS Which securities move together? How should I diversify? What should I sell to reduce risk? Whats a reliable predictor of a security? 11. 68% correlationbetween AUD &amp; EURPlot of 6 month daily AUD - EUR values that movecounter-cyclically toindicesBlock of correlatedcurrencies clustered hierarchically 12. VISUALISING CHANGEWhat was the weather in India likeEDUCATION WEATHER THE LAST 100 YEARS? 13. VIDEOhttp://youtu.be/WT0Aq41BaOQ 14. www.gramener.comblog.gramener.com </p>