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# Data Visualisation using SSRS: Euclid's Royal Road to the numbers

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Data Visualisation using SSRS: Euclid's Royal Road to the numbers

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• In his Eudemiarz Summary, Proclus (410-485) tells us that Ptolemy Soter, the first King of Egypt and the founder of the Alexandrian Museum, patronized the Museum by studying geometry there under Euclid. He found the subject difficult and one day asked his teacher if there weren&apos;t some easier way to learn the material. To this Euclid replied, &quot;Oh King, in the real world there are two kinds of roads, roads for the common people to travel upon and roads reserved for the King to travel upon. In geometry there is no royal road.&quot;
• Curiosity and parsimony in data analysisAssumes that the data is clean and valid! Data visualisation can offer methods of finding patterns in the data that are perhaps not so well-known, such as bullet charts, table lenses, and other visualisations based on Tufte&apos;s &quot;small multiples&quot; such as sparklines.
• Data taken from Princeton’s International Archive network
• Linear versus Quadratic ChangeFlorence Nightingalerepresented the numbers of soldiers using the area, not the radius, of the circle segments
• Data taken from Stacked Line Fuel Consumption.rdlThe database is SQLBIts.The stored procedure is usp_MV_ConsumptionTablesmall multiples many years ago to describe an arrangement of small graphs, all within eye span, which look precisely the same (including a consistent quantitative scale), except that each displays a different subset of a larger set of data.This clarity which would not have been achieved by using a single graph and making it three dimensional.
• Compact way of getting lots of information on one pageThis contains nearly 3000 rows, with 6 columns = 18000 pieces of information on one pageIdea is to have a 30,000 foot view of the dataSome highlighting added in order to make it easierColour is used here in order to convey the message about the maximum itemsattempting to detect and understand theunique features that the data set may contain and then to interpret them.
• Projecting multivariate data onto two dimensions for displayattempting to detect and understand the unique features that the data set may contain and then to interpret them.The underlying data may not be related; the problem then becomes an effort to understand the unique features in the data set.The analyst can then interpret them.This type of analysis: principal component analysis (Chapter 2) and cluster analysisUse two interval-level variables. Fully define the variables with the axis titles. Use the chart title should identify the two variables and the cases (e.g., cities or states) If there is an implied causal relationship between the variables, place the independent variable (the one that causes the other) on the X-axis and the dependent variable (the one that may be caused by the other) on the Y-axis. ·         Scale the axes to maximize the use of the plot area for displaying the data points.·         It’s a good idea to add data labels to identify the cases.I have used blue here because the points are smaller; this way, the colour used can be determined by the visual display itself.
• Heatmap – Visual display that encodes quantity as colour.Colour - (Quantity) - 2D representation so brighter and darker colours = higher values (Few, 2009)The opposite is also true: lighter colours are not so important.Please don’t use red and green for these heatmaps!
• Data Visualisation can help to offer a ‘royal road’ to the numbers, if done properly.
• ### Data Visualisation using SSRS: Euclid's Royal Road to the numbers

1. 1. Data Visualisation using SSRS: Euclid's Royal Road to the numbers<br />Author: Jen Stirrup<br />March 2011<br />1<br />Copper Blue Business Intelligence Ltd<br />
2. 2. Euclid’s Royal Road<br />4/14/2011<br />Footer Text<br />2<br />
3. 3. Data Visualisation<br />Trellis Chart<br />Table Lens<br />Sploms<br />Heatmap<br />4/14/2011<br />Footer Text<br />3<br />
4. 4. What makes a good visualisation<br />Effective: the viewer gets it (ease of interpretation)<br />Accurate: sufficient for correct quantitative evaluation. Lie factor = size of visual effect/size of data effect<br />Efficient: minimize data-ink ratio and chart-junk, show data, maximize data-ink ratio, erase non-data-ink, erase redundant data-ink<br />Aesthetics: must not offend viewer's senses (e.g. moiré patterns)<br />Adaptable: can adjust to serve multiple needs<br />
5. 5. Chartjunk Example<br />
6. 6. Chartjunk: unintended<br />
7. 7. Guidelines<br />
8. 8. Trellis Chart - Full<br />4/14/2011<br />Footer Text<br />8<br />
9. 9. Table Lens<br />4/14/2011<br />Footer Text<br />9<br />
10. 10. Sploms<br />4/14/2011<br />Footer Text<br />10<br />
11. 11. Heatmap<br />4/14/2011<br />Footer Text<br />11<br />
12. 12. Heatmap<br />4/14/2011<br />Footer Text<br />12<br />
13. 13. Back to the Royal Road<br />Questions?<br />4/14/2011<br />Footer Text<br />13<br />