Alice’S Adventures In Reporting Services Final
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Alice’S Adventures In Reporting Services Final

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Presentation from SQLBits community event Oct 2010 www.sqlbits.com

Presentation from SQLBits community event Oct 2010 www.sqlbits.com

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  • In today's current economic climate, it is not an option to leverage the intelligence hidden in data; Poor data leads to poor decisions. (Economist, 2009 6 ). Otherwise we end up like poor Alice in Through the Looking Glass; Alice is a competitor in the Red Queen's Race, running madly on the spot without moving forward. When Alice complains that she is getting nowhere, the Red Queen remarks to Alice, "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!" Thus, the question is, how can Business Intelligence be leveraged successfully and reliably in order to inform strategy, encourage innovation, and nurture competiveness to move us forward in the Red Queen's race? Reports are like good writing; should express ideas with clarity. Terry Pratchett once wrote that it was better to encourage 'thinking outside the box when there's evidence of any thinking going on inside it'. Any 'out of the box' thinking, based on the data, will only be relevant if the data is all present and correct. To do this, users’ input is absolutely essential. If they don’t like the project, then they won’t use it and the project will ultimately fail. It’s all very well having slick data warehouses with fast access to data; if the users can’t understand it, then the project has failed.
  • Why did the space shuttle Challenger explode? Many people assume it was because of poorly-functioning O rings on the booster rocket. However, those O rings didn’t send the Challenger into space - people did, and those people drew their most critical information from two simple charts, screened by an overhead projector. The graphs displayed tiny pictures of each shuttle booster, lined up in chronological order, showing launch temperatures and any potential O ring damage. This occluded the data around the effect of temperature on the O Rings, so it was not clear.
  • Mobilise knowledge of human visual processing to show patterns in the data. Brave New World of Business Intelligence: opening data up to information consumers. Information is only useful when it is has been understood Exposing data, and by extension Information visualisation, is at the centre of business intelligence Stephen Hawking commented once that each equation in ‘A Brief History of Time’ (1988) would 'halve the sales', because it would make the book much more difficult to understand. The aim of a business intelligence solution is to make everything as straightforward as possible to the information consumer; if the users don't like it, then they won't use it. This would result in failure, so it is a key success criterion of the project. Text-based reports require cognitive effort to analyse the presented information.  On the other hand, in order to leverage the abilities of the human visual perception system in addition to alleviate cognitive effort, it is possible to use the principles of information visualisation in order to display data.
  • Same data, this time in a picture. Some of the detail is lost, but we do gain a better appreciation of the patterns in the data. The table and graph complement each other; not necessarily replace one another.
  • Preattentive processing - the ability of the low-level human visual system to rapidly identify certain basic visual properties, parallel processing by the low-level visual system visual properties that are detected very rapidly and accurately by the low-level visual system. These properties were initially called  preattentive,  since their detection seemed to precede focused attention. Later research showed that attention plays a critical role in what we see, even at this early stage of vision. The term preattentive continues to be used, however, since it conveys an intuitive notion of the speed and ease with which these properties are identified.
  • Automatically and simultaneously for the whole entire visual field of view. "Who would believe that so small a space could contain the images of all the universe? O mighty process!" - Leonardo Da Vinci Pre-Attentive attributes of visual perception are pre-conscious (Colin Ware) Make use of the pre-attentive attributes of visual perception to make reports clearer; take advantage of these pre-conscious attributes so that users do not have to work so hard to understand the report In 2D position, we assume that higher is greater.
  • In 2D position, we assume that higher is greater.
  • What’s wrong with this line chart?
  • The best effective quantitative graphs are 2-D, X-Y axis type Scattergrams – good for distinguishing attributes Bar charts – good when the categories are distinct from one another Line graphs – good for when the categories are related to one another Heatmap – colour used to encode quantity Treemap – uses size and colour to encode quantity Insert a quick demo here?
  • On June 16, a second containment system connected directly to the blowout preventer became operational carrying oil and gas to the Q4000 service vessel where it was burned in a clean-burning system.
  • Quick sentence summary of ‘Preattentive attributes’ – the whole visual field, automatic, pre-conscious Moving onto ‘Visual Integration’ - determine which element goes with which data e.g. colour matches which legend Cognitive Integration – final stage of comprehension, figure out the relationship between the elements and graph components e.g. compare. Heidigger ( seeing and seeing as distinction, top-down constructive interaction as a way of understanding how we see)
  • - ChartJunk - "In anything at all, perfection is finally attained not when there is no longer anything to add, but when there’s no longer anything to take away.“ Antoine de St. Exupery ‘ Bauhaus’ of data visualisation Elimination of any ornament Emphasising simplicity Harmony between function and design We all suffer from Change Blindness e.g. movie mistakes Constructive element to what we see, which can depend critically on where attention is focused. The eye and the brain are ‘seeing’ differently in the same picture. Too many distractors can mean that the message is lost – produce graphs that are visually salient. (Exaggerated form of Change Blindness is topographic agnosia) All of the non-necessary imagery that appears alongside a graph or chart (Tufte) – does not tell the viewer anything new Unnecessary elements e.g. Non-data ink Makes it difficult to understand e.g. Items depicted out of scale
  • Don’t ask your users to input more visual processing resources on the display than absolutely required Colour should be used to: - Convey a message - Highlight - Grouping - Denoting quantity Excel example of grey background in the chart area – choose ‘None’ Stick with a standard set of palettes so you don’t have to waste time choosing colours. Alternatively your customer or company may require that a standard ‘company’ set of colours are used to match company logo etc. so use those ones.
  • Don’t ask your users to input more visual processing resources on the display than absolutely required Colour should be used to: - Convey a message - Highlight - Grouping - Denoting quantity Excel example of grey background in the chart area – choose ‘None’ Stick with a standard set of palettes so you don’t have to waste time choosing colours. Alternatively your customer or company may require that a standard ‘company’ set of colours are used to match company logo etc. so use those ones.
  • 3D obscures the message – chartjunk! – so do not use it! Who can tell the value of 0.75, 0.75, 0.75?
  • Quick sentence summary of ‘Preattentive attributes’ – the whole visual field, automatic, pre-conscious Quick sentence summary ‘Visual Integration’ - determine which element goes with which data e.g. colour matches which legend Moving onto Cognitive Integration – final stage of comprehension, figure out the relationship between the elements and graph components e.g. compare. Heidigger ( seeing and seeing as distinction, top-down constructive interaction as a way of understanding how we see)
  • Cognitive Integration is about putting the pieces together. Filter versus a parameter – filters retrieve all the data and then select the information that’s required. Parameters initially select out the information that’s required at first.
  • Cognitive Integration is about putting the pieces together. Filter versus a parameter – filters retrieve all the data and then select the information that’s required. Parameters initially select out the information that’s required at first.
  • How many minutes is the blue section worth?
  • Like a bar chart, that takes up only a little space and does not contain any extra information. Uses variations of the same hue to try and keep things as simple as possible.
  • Example of the original showing ‘The Knowledge’ (MacGuire, 1999) i.e. taxi drivers have statistically significant larger volume posterior hippocampal regions than control subjects, since the brain ‘borrows’ from the anterior hippocampus. However, this isn’t very clear from the bar chart.
  • With the advent of cheap, easily available, colour printers, the dot plot went out of vogue. However, this graph emphasises the statistical significance between posterior hippocampal region of the taxi drivers versus the control subjects. Use 2-D position to denote the value; we assume the right hand side increases in value as we go from left to right. Gets around ‘length’ pre-visual attribute by removing the bar and using a ‘marker’. However, note that the scales must match i.e. 80 on the left hand side, 110 on the right hand side; example of poor practice, discuss in order to highlight it.
  • Quick Demo of this; a bullet graph is offered as a gauge type in SSRS 2008 R2.This is likely to need some explanation! Stephen Few’s invention, as an attempt to clarify graphs and optimise the data/ink ratio. Also used in Facebook for privacy settings

Alice’S Adventures In Reporting Services Final Alice’S Adventures In Reporting Services Final Presentation Transcript

  • Alice’s Adventures in Reporting Services Presenter: Jen Stirrup, Contact: jenstirrup@jenstirrup.com SQLBits VII Saturday 2 nd October, 2010
  • Agenda
    • Business Intelligence and the Red Queen’s Race
      • ‘ you want to get somewhere… you must run at least twice as fast as that!’
  • Agenda
    • Business Intelligence and the Red Queen’s Race
      • ‘ you want to get somewhere… you must run at least twice as fast as that!’
    • Business Intelligence and Alice’s Question
      • ‘ What is the use of a book, without pictures?’
  • Agenda
    • Business Intelligence and the Red Queen’s Race
      • ‘ you want to get somewhere… you must run at least twice as fast as that!’
    • Business Intelligence and Alice’s Question
      • ‘ What is the use of a book, without pictures?’
    • Practical Applications – the Doorknob
      • ‘ Read the directions and directly you will be directed in the right direction.’
    • Over to you!
  • Reporting and the Red Queen
  • Reporting and the Red Queen
  • Why not just tables? Zimbabwean inflation rates (official) since independence Date Rate Date Rate Date Rate Date Rate Date Rate Date Rate 1980 7% 1981 14% 1982 15% 1983 19% 1984 10% 1985 10% 1986 15% 1987 10% 1988 8% 1989 14% 1990 17% 1991 48% 1992 40% 1993 20% 1994 25% 1995 28% 1996 16% 1997 20% 1998 48% 1999 56.9% 2000 55.22% 2001 112.1% 2002 198.93% 2003 598.75% 2004 132.75% 2005 585.84% 2006 1,281.11% 2007 66,212.3% 2008 231,150,888.87% (July)
  • Thinking with your Eyes
  • Stages of Processing
  • Stages of Processing
  • Pre-attentive Attributes Attribute Example Assumption Spatial Position 2D Grouping 2D Position Sloping to the right = Greater Form Length Width Orientation Size Longer = Greater Higher = Greater Colour Hue Intensity Brighter = Greater Darker = Greater
  • Pre-attentive Attributes Attribute Example Graph Type Spatial Position 2D Grouping 2D Position Line Graph Form Length Width Orientation Size Bar Chart Colour Hue Intensity Scatter Chart
  •  
  •  
  •  
  • Stages of Processing
  • Visual Integration
    • Chartjunk
    • Data/Ink Ratio
  • Mobilising Visual Integration
      • Affordance
        • Highlighting – bright colours
        • Increasing Intensity = Increasing Values
      • Eye Tracking Studies
        • Eye Path going from cluster to legend, and back again (Ratwani, 2008)
  • Mobilising Visual Integration
      • Sequential Palettes
      • Diverging Palettes
      • Qualitative Palettes
  • Visual Integration
  • Stages of Processing
  • Cognitive Integration
    • Building an understanding of the graph
      • Eye Tracking Studies
        • Eye Path going from cluster to cluster, rather than cluster to legend (Ratwani, 2008)
  • Cognitive Integration
    • Summary first
    • Zoom and filter
    • Then details ‘on-demand’
            • (Schneiderman, 1999)
  • Cognitive Integration
    • Comparison
    • Sorting
    • Bookmarks – analytical view of browsing
  • Mobilising Cognitive Integration
    • Humans are not good at judging:
      • 2D Area
      • Angles
  • Mobilising Cognitive Integration
    • Humans are not good at judging:
      • 2D Area
      • Angles
    • Pie Charts and Gauges rely on these characteristics…
  • Lost Finale: Mins Breakdown
  • Bullet Chart Text Label Bar to display Performance measure Marker to display Comparative measure
  • Bar Chart Example - Original
  • Dot Plot Example
  • Bullet Graph Example
  • Summary
    • Reporting and the Red Queen’s Race
    • Three Stages of Processing
    • Some Practical Suggestions!
    • Over to you!
  • Contact Details
    • [email_address]
    • Twitter.com/jenstirrup
  • Coming up… P/X001 Understanding and Preventing SQL Injection Attacks Kevin Kline P/L001 SSIS Fieldnotes Darren Green P/L002 The (Geospatial) Shapes of Things to Come Simon Munro P/L005 End to End Master Data Management with SQL Server Master Data Services Jeremy Kashel P/T007 Understanding Microsoft Certification in SQL Server Chris Testa-O'Neill # SQLBITS
  • References
    • Information Visualisation, Perception For Design, Second Edition, Colin Ware, Morgan Kaufmann Publishers, San Francisco CA.
    • Cleveland, W.S. The elements of graphing data. Wadsworth Advanced Books and Software, Monterey, Canada, 1985
    • Treisman, A. Features and Objects in visual processing. Scientific American, 255(2): 114 - 125, 1986 
    • Navigation-related structural change in the hippocampi of taxi drivers, MacGuire et al 1999
    • Chartjunk image used 80 4-point " ley lines " pass through 137 random points. Original raster image by The Anome, vectors by Mysid. http://commons.wikimedia.org/wiki/File:Ley_lines.svg for details. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
  • References
    • Raj M. Ratwani, J. Gregory Trafton, Deborah A. Boehm-Davis (2008). Thinking graphically: Connecting vision and cognition during graph comprehension.  Journal of Experimental Psychology: Applied, 14  (1), 36-49 DOI:
    • Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman,  Readings in Information Visualization: Using Vision to Think , Academic Press, San Diego, California, 1999, quoting a research paper by Kumar, Plaisant, and Shneiderman.) 
    • Visual Explanations , Edward Tufte (1997)