Business Data Visualization

Aug 28, 2019 | Publisher: Jack Zheng | Category: Technology |  | Collection: Data and Analytics Lecture Notes | Views: 2683 | Likes: 14

Data Visualization for Analytics and Business Intelligence A Comprehensive Overview IT 7113 Data Visualization http://idi.kennesaw.edu/it7113/ J.G. Zheng Fall 2019 This lecture notes is hosted at https://www.edocr.com/v/yqwmqeba/jgzheng/Business-Data-Visualization Overview This lecture notes provides a high level overview of data visualization primarily used in business intelligence and analytics. This overview is comprehensive to cover as many aspects as possible but keep them at a high level: • What is (business) data visualization? – What are the types of visualization? What are the related terms and fields? How are they similar or different? • The role and value of data visualization in business intelligence and analytics (information seeking and decision making) • Basic data visualization elements and forms • Data visualization and IT – Design, development, delivery – Applications and tools • Skills, jobs, and career • Learning resources 2 Visualization • Visualization is the process of forming a visual imagery of things in human mind • What (things) can be visualized? – Visible reality: person, animal, building, mountain – Hidden reality: earth core, blood vessel, universe – Invisible reality: wind, air, heat, electron, sound, smell, magnetic fields – Abstract: data, information, idea, hierarchy, process, relationship • How to visualize them? - Types of visualization forms – 2D vs. 3D – Static vs. motion – Virtual vs. materialized – Realistic vs. abstract 3 Data Visualization • Data visualization is the visual and interactive exploration and graphic representation of data of any type (discrete, nominal, relational, etc.). – Forms a visual imagery representation of data/information (meaning) – The process of presenting (mapping) data as visual symbols and properties – Utilizes a combination of visual elements (shapes) and variables like size, color, positions, etc. • The general purpose of data visualization is to provide a better way of presentation and interaction; more specifically, we use data visualization for these purposes (different types of data visualization will serve different purposes): – Understanding - comprehension of abstract ideas and processes – * Information seeking - browsing, navigation, exploration, discovery – * Data analysis and decision support – * Event or operation monitoring – Communication, story telling – Artistic (beauty) expression and appreciation – Entertaining and for fun 4 * The focus of this course A Visualization of “Data Visualization” http://prezi.com/qvhyfup5z7yz/dashboard-design-making-reports-pop/ 5 Related Terms and Fields • Data presentation • Information visualization (including infographic), illustration • Computer graphics, reality visualization (VR, AR), scientific visualization • Simulation http://setosa.io/bus/ • Business data visualization • Big data visualization 6 Data Presentation • (In the field of statistics) data presentation is the method by which people summarize, organize and communicate information using a variety of tools, including tables and diagrams/charts. Extended readings on data presentation: • http://www.slideshare.net/ahsanshafiq90/data-presentation-2-15572325 • https://www.slideshare.net/31mikaella/presentation-analysis-and-interpretation-of-data 7 Also textual: bullet points, text style, spacing, etc. People usually don’t treat text based paragraphs and tables as visualizations. But they do or can have ample visual properties. This is generally more aligned with data visualization. Information Visualization • Information visualization is the study of visual representations of information or data to reinforce human cognition. The data include both numerical and non-numerical data, such as text and geographic information. – A very close field, and very often used as the synonym for, or even include, data visualization – Often in the form of illustrations and infographics – https://en.wikipedia.org/wiki/Information_visualization • Infographics are graphic visual representations of information, data or knowledge. – Usually a mixture of texts, graphics, and data visual forms (charts, diagrams, tables, maps, etc.) to quickly and vividly communicate complex information (multiple variables or dimensions). – http://en.wikipedia.org/wiki/Information_graphics – https://visual.ly/blog/11-infographics-about-infographics/ – Often used in mass communication (e.g. journalism) and marketing • https://www.business2community.com/digital-marketing/visual-marketing-pictures-worth-60000-words-01126256 • https://www.interaction-design.org/literature/article/information-visualization-who-needs-it • Examples – http://dailyinfographic.com/ – http://www.cooldailyinfographics.com/ – http://blogs.scientificamerican.com/sa-visual/2014/10/14/sa-recognized-for-great-infographics/ – https://visual.ly/m/design-portfolio/ 8 See more resource about information visualization • https://www.interaction-design.org/literature/topics/information-visualization • https://informationisbeautiful.net Illustration of an Idea/Concept • NOT data visualization 9 More examples of information visualization can be found at https://informationisbeautiful.net Infographics vs. Data Visualization • Major differences – One time creation and use; mostly created using graphic design tools rather than using data processing tools – Information often is more general and can be more qualitative. – Utilizes more free forms (non standard) of visual diagrams or illustrations (illustrational diagrams); emphasizes creativity and artistically expression – Often hand-crafted instead of automatically populated from a data source. – Not for interactive exploration or decision making; intended for more casual use (informational) for general people. • More readings: infographics vs. data visualization – https://visage.co/throwdown-data-visualization-vs-infographics/ – http://www.jackhagley.com/What-s-the-difference-between-an- Infographic-and-a-Data-Visualisation 10 Computer Graphics • Computer graphics is graphics created using computers, mainly based on computing logic and algorithms. • Reality visualization (Virtual Reality or Augmented Reality) is the application of computer graphics 11 Scientific Visualization • “Primarily concerned with the visualization of three-dimensional phenomena (architectural, meteorological, medical, biological, etc.), where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth.” – https://en.wikipedia.org/wiki/Scientific_visualization • Physical science visualization – Visualization (simulation) of reality (universe, sun, explosion, atom, climate, etc.) • Mathematical model/algorithm visualization – http://acko.net/blog/how-to-fold-a-julia-fractal/ 12 Business Data/Information Visualization • Business is a general term to describe activities, events, and operations that make an system running (more like the term field or domain) – Business includes many activities directly associated with human, like commerce, government, education, sports, charity, entertainment, etc. – Or events that impact human, such as weather, earthquake, etc. – Business data or information records various aspects of these activities. • Main features of business data – Abstract: data is not directly defining or visualizing a real world phenomenon – Often quantitative – Often structured or semi-structured, repeated – Multidimensional – Directly comprehendible by average human (in a particular “business”) • Business data visualization features – Main purposes are information seeking, analysis, decision support, monitoring, and communication. – Using simple, standard, and abstract images (symbol/chart/diagram/map) – Highly reused and commonly accepted visualization forms – Utilizes data binding techniques to generate visualizations in an automated way • Where is data visualization used in businesses? – Part of a BI or analytics process especially in self-service – Communication of results all kinds of reports (periodical/seasonal or real time) and presentations (e.g. PowerPoint) – Presentation of results in statistical analysis, data mining or other advanced analytics. – Visual analytics – Operational or administrative monitoring 13 [Yes] Business Data Visualization • Periodical reports – https://myit-2019.itdashboard.gov • Performance dashboards – https://www.geckoboard.com/learn/dashboard-examples/ • Visual data exploration and seeking – https://www.productchart.com/smartphones/ – https://finviz.com/map.ashx – https://www.census.gov/dataviz/ • Visual analytics – https://www.google.com/publicdata/directory • Real time monitoring – https://www.nytimes.com/interactive/2018/11/06/us/elections/r esults-dashboard-live.html 14 [Not] Business Data Visual These examples are not really considered to be business data visualization – not the focus of this class • Not meaningful business data – http://classes.dma.ucla.edu/Spring13/161/projects/students/david/project-5/html/?/image-gallery/ • Mathematical visuals – https://mathigon.org/world/Fractals – https://en.wikipedia.org/wiki/Fractal • Scientific visualization – https://en.wikipedia.org/wiki/Scientific_visualization • Reality simulation – https://weather.com/weather/radar/interactive/l/USGA0028:1:US • Infographics/data graphics – https://visual.ly/m/design-portfolio/ – https://informationisbeautiful.net (not all but many are) – http://www.visualisingdata.com (not all but many are) – http://courses.ischool.berkeley.edu/i247/s18/ (not all but many are) • Too much artistic (visual embellishment) – http://hci.usask.ca/uploads/173-pap0297-bateman.pdf 15 Comparison of Related Visualization Fields Content Visual Forms/Tools Purpose Business data visualization Quantitative data related to business activities; metrics, key performance indicators (KPIs) Charts, diagrams dashboards Data exploration, analysis, decision making General data visualization General quantitative data Charts, diagrams dashboards Data exploration, analysis, decision making Information visualization All kinds of information, quantitative and qualitative Infographics, illustrational diagrams Information seeking, artistic illustration, casual communication, story telling Illustration Processes, structures concepts, ideas Diagram, image, graphics Making the content more vivid and engaging, easier to understand the complexity. Scientific visualization Real world object or phenomenon, mathematical functions and formulas Computer generated graphics, 3D virtual reality Recreate or simulate the real-world object or phenomenon, or visualize an algorithm effect. Simulation Calculated data based on formulas or rules Animated diagram or virtual reality Demonstrate the effect of scenarios under certain rules 16 Big Data Visualization • Big data visualization usually refers to a visualization with a large number of data points (items and attributes) on a large space. – The goal is to see patterns and relationships beyond a few items – Using more contemporary visualization techniques including real- time changes, animations, rich interactions, etc. – Using more illustrative graphics and more artistic visual representation of the data. • Is big data visualization a hype? – “big data visualization is generally not helping humans make novel discoveries.” https://jeffjonas.typepad.com/jeff_jonas/2016/02/data- visualization-outing-hype.html • What’s the purpose of big data visualization? And what’s the effective way to use them? – https://pudding.cool/2018/10/city_3d/ – https://pudding.cool/2019/07/book-covers/ 17 Why Data Visualization? • Visualizing is basically a human physiological and psychological capability, and plays an important role in human information behavior – Facilitates data exploration that leads to insights – Greatly helps data comprehension and enhance problem solving capabilities – Enable perceptual inference operations and detection of patterns (see the figure on the right) – Provide a high level overview of complex data sets for quick comprehension – Extract/provoke additional (implicit) perspectives and meanings – Ease the cognitive load of information processing – Recall or memorize data more effectively – Effective communication (story telling) • More specifically (see examples in the following slides) – Identify patterns and trends – Quickly focus on area of interest or area of difference (can be an anomaly) – Identify structures or relationships – More comprehendible with familiar visual context 18 A picture is worth 1000 words (clicks) Source: How Data Visualization Empowers Decision Making https://community.watsonanalytics.com/wp- content/uploads/2015/04/BlueHill_HowDataVi sualizationDrivesDecisionMaking_Dec14.pdf Identify Trends and Patterns What's the difference between these two cities? Which one is Atlanta? In 10 seconds? 19 Identify Trends and Patterns Monthly average temperature Monthly average precipitation What's the difference between these two cities? Which one is Atlanta? In 10 seconds? 20 Quickly Focus on Area of Interests • Which stock performed different from others? http://finviz.com/map.ashx 21 Identify Structures/Relationships • Does June report to Joy? Employee Reports to Jane Jack Jessie Jane Jason Jane John Joy Joseph Joy Joy Jack June Jessie Jack Jane Joy Jessie Jason John June Joseph 22 More Comprehendible with Familiar Visual Context 23 Text (non visual) Visual Geo map is the recognizable familiar visual context Data Visualization in BI/Analytics • Data visualization is an important part of data exploration and decision making. Given the power of visualization, it is only natural to apply the rich communication techniques in the field of BI and analytics. • Visualization has been considered as a separate field from BI in the early days (prior to 2010), but it quickly brought the traditional business intelligence to life – As organizations seek to empower non‐technical users to make data‐driven decisions, they must consider the prowess of data visualization in delivering digestible insights. – Visualization tools have become increasingly important to business intelligence, in which people need technology support to make sense of and analyze complex data sets and all types of information. Dashboards, reporting, end-user self- service, and advanced visualization are the top most important technologies and initiatives strategic to BI in 2018. https://www.forbes.com/sites/louiscolumb us/2018/06/08/the-state-of-business- intelligence-2018/#b2fca2878289 Cognitive Theories • The visualization process, human perception, and elements of visual mapping – Explains the cognitive visualization process in human brain – https://www.interaction-design.org/literature/ article/visual-mapping-the-elements-of-information-visualization – https://www.interaction-design.org/literature/article/the-properties-of-human-memory-and- their-importance-for-information-visualization • Pre-Attentive Processing – Any visual processing of that item prior to the act of selection can be called “preattentive”. [Wolfe, Treisma, 2003] – Preattentive processing can help to rapidly draw the focus of attention to a target with a unique visual feature (i.e., little or no searching is required in the preattentive case). [Healey, 2005] – http://www.infovis-wiki.net/index.php/Preattentive_processing • Gestalt Principles of Perception – Gestalt principles describes a set of ways how human perceives images and how visual information are identified and related from images. – These principles have profound implication on visual design, UX/UI and interaction design, and data visualizations (charts, maps, dashboards) – https://www.interaction-design.org/literature/topics/gestalt-principles – http://www.scholarpedia.org/article/Gestalt_principles 25 We will cover these topics in details in module 3. http://dl.acm.org/citation.cfm?id=1082104 Data Visualization Concepts • Visual elements – Visual elements are the basic building blocks in a chart or diagram to visualize data items. The most fundamental and abstract elements are: point, line, surface (area), and volume (3D). These basic elements, and the more complex elements built up on them, can represent almost anything in a visualization. • Visual variables (visual properties, or visual attributes) – Visual property, or attribute, or variable, is the “decoration” applied to visual elements to represent data values. – A visual property is used to encode different values of a particular dimension of data • Multiple visual variables can be used together to represent multiple dimensions of data – There are six basic visual properties - easier to be remembered as “SCOPeS” – For reference: http://wiki.gis.com/wiki/index.php/Visual_Variables • Visual forms/styles – Visual form is the basic style a visualization is presented based on user needs. 26 We will cover this topic in details in module 3. Size Color Orientation Position Texture Shape Basic Visual Forms/Styles used in BI 27 Form/Style Description Typical Types and Examples Embedded visual It is embedded in, or directly on top of, texts and other forms of data presentation (table, graphic, etc.). • Conditional formatting (visual cues) • Inline chart (Sparkline) Block visual It is displayed as an independent visual unit and occupies a larger space. It is often a part of a report or dashboard, appearing together with other content. But sometimes it can become a standalone visual with many data points or enough complexity. • Chart • Illustrational diagram • Map (smaller) • Data table (usually with embedded visuals) Standalone visual It is a standalone application and is not mixed with other types of content or tool. Most interactions are within the visual. It may consist of a combination of different types of visuals. • Dashboard • Visual analysis tool (or an analytical dashboard) • Map (bigger or full screen) Conditional Formatting • Conditional formatting – Direct formatting on text or numbers using visual properties, embedded in a pre-established presentation • Example – Golf http://www.masters.com/en_US/scores/ – Tag cloud 28 Sparkline • A sparkline is a small chart embedded in a context of words, numbers, tables, images, or other type of information. – It presents the general shape of the variation in a simple and highly condensed way. – http://en.wikipedia.org/wiki/Sparkline • Examples – http://omnipotent.net/jquery.sparkline/ – http://www.klipfolio.com/blog/table-component-overview – https://trumpexcel.com/sparklines/ 29 Sparkline Chart • A chart is a graphical representation of data – Chart is a unique combination of symbols (visual elements) with visual properties which directly represents quantitative values – http://en.wikipedia.org/wiki/Chart • Chart vs. Diagram – These two terms are very similar; they are often used together or interchangeably in daily life. – Chart is more abstractly presented and focuses more on quantitative values. – Diagrams also cover a lot of qualitative information like process, concepts, ideas, structures, etc.; they also intergrade more real world contexts like maps. – Diagram is sometimes considered to include chart. 30 We will cover these topics in details in module 4 and 5. Choose a Chart – Figure from http://extremepresentation.com/design/7-charts/ or http://extremepresentation.typepad.com/blog/2008/06/visualization-taxonomies.html – Online chooser with templates: http://labs.juiceanalytics.com/chartchooser/ 31 Summary of Categorizations and Lists Versions Reference Comments Andrew's version https://extremepresentation.com/d esign/7-charts/ This is most often referred version with a simple visual itself. Categorized by four purposes: compositions, comparison, distribution, and relationship. Jorge’s version https://excelcharts.com/classificati on-chart-types/ Influenced by Andrew’s version, and added two more categories: evolution (like trend) and profiling. Juice Analytics http://labs.juiceanalytics.com/chart chooser/ Provided as an interactive online chooser with templates. Categorized similarly but added an “trend” category. Ferdio http://datavizproject.com An interactive resource with a lot of examples. Included diagrams and maps. Categorization by function is similar to the two above. Charlr http://blog.chartlr.com/post/120607 313773/which-chart-type-works- best-for-your-data https://chartlr.com/chart-types Similar categories; added “deviation”. An interactive tool is provided with simple descriptions and examples. Data catalog http://www.datavizcatalogue.com An interactive catalog with very detailed description for each chart. Added many more smaller and specific categories. From Data to Viz https://www.data-to-viz.com A classification of chart types based on input data format. It comes in the form of a decision tree. Chart make directory http://chartmaker.visualisingdata.c om Has a difference perspective on categorization by the kinds of data. Also provides references for products support. Others • A periodic table of visual methods: http://www.visual-literacy.org/periodic_table/periodic_table.html • http://www.amazon.com/Information-Graphics-Comprehensive-Illustrated- Reference/dp/0195135326 • https://queue.acm.org/detail.cfm?id=1805128 32 Illustrational Diagrams • Illustrational diagrams – Mainly to visualize quantitative as well as qualitative data to illustrate their features, relationships, sequences, etc. – Also includes position as a dimension in a logical (virtual) structure, such as network diagram, process diagram, hierarchy diagram, etc. – http://en.wikipedia.org/wiki/Diagram – https://en.wikipedia.org/wiki/Illustration • Common examples used in business information visualization – Flow chart: http://en.wikipedia.org/wiki/Flowchart – Network graph: https://flowingdata.com/charttype/network-graph/ – Tree diagram: http://en.wikipedia.org/wiki/Tree_structure – Time line: https://datavizcatalogue.com/methods/timeline.html – Structure diagram: http://en.wikipedia.org/wiki/Data_structure_diagram – More: https://datavizproject.com/family/diagram/ • Examples of those with quantitative data – Sankey diagram https://en.wikipedia.org/wiki/Sankey_diagram 33 Location Intelligence (Mapping) • Location intelligence (LI) is a business intelligence (BI) tool capability that relates geographic contexts to business data. – http://searchbusinessanalytics.techtarget.com/definition/location-intelligence-LI • Data that involves location – Geo location: country, state, city, zip, region (park, air port), road, path – Contextual location: building, room, shelf, stadium, court, fields, body – Abstract location: geo chart/diagram, process, network • Forbes report “The Power of Place” https://www.forbes.com/forbesinsights/pitney_bowes_power_of_place/ – Location intelligence (LI) can be described as the process of deriving meaningful insight from geospatial data relationships to solve a particular business problem. It involves layering multiple data sets spatially and/or chronologically, for easy reference on a map, and its applications span industries, categories and organizations. – Location helps us visualize business data in familiar ways—typically with maps. Unlike traditional business analytics, which tends to present results in spreadsheets, pie charts and bar graphs, maps allow businesses to see the underlying proximity relationships and trends in their data. – Maps are widely recognized and understood—even by nontechnical professionals—which helps make the data represented more accessible and understood by those who might make use of it. • Tableau reports “The Power of Where” https://www.tableau.com/learn/whitepapers/government- mapping – Quickly associate data with familiar position/location – added familiarity increase comprehension. – Mapping lets you see the implications of your data in ways not detectable on a standard spreadsheet, linear graph or pie chart. – Maps provide context that leads to better ways to prioritize, plan and execute your mission. Location Based Visual Types • Geo map – Visualize geo location related data directly on real world maps – Data represented as points, areas, paths – http://luminocity3d.org/WorldCity/ – https://maps.google.com/gallery/ • Abstract illustrated map: these are conceptually related to geo location but presented in an abstract way – an illustration, rather than accurate geo locations – Tile grid map: https://fivethirtyeight.com/features/where-your-state-gets-its- money/ – Geo chart: https://googletrends.github.io/search-election-election-night/ – Road/path map: http://fatalities.safer63and881.com/#highway • Contextual map – Any data relevant to the positioning in a particular context or space, e.g., building, campus, mall, stadium, a just a space (like a hitting area) etc. – https://www.facilityquest.com/occupancy-utilization-studies/ We will cover these topics in details in module 6. Digital Dashboard • A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. – Dashboard Confusion, Stephen Few, http://www.perceptualedge.com/articles/ie/dashboard_confusion.pdf • A set of visualization or presentation of data views organized in a single screen/page – The data is generally KPIs and shows trends, breakdowns, and comparisons against a forecast or historical data – A dashboard generally contains a variety of different views of data: charts, diagrams, tables, standalone numbers, interactive controls (such a filters) • Dashboard vs. report, visual analysis tool, and scorecard – http://www.dashboardinsight.com/articles/digital-dashboards/fundamentals/what-is-a- dashboard.aspx • The Values of Dashboard – Allow decision makers to see a variety of relevant data that affects their divisions or departments – Quickly understand data and respond quickly at one place; save time over running multiple reports – More: http://www.bidashboard.org/benefits.html 36 We will cover these topics in details in module 7 and 9. Types/Levels of Dashboard Monitoring: Operational real time view of important indicators. Analysis/data interaction: Provides optimized UI and information seeking and navigation feature among data and metrics to support analysis. Summary/overview: As a summary high level report of operating status; may be at the top executive level (strategic) or departmental (tactical) levels. 37 Image from http://prezi.com/qvhyfup5z7yz/dashboard-design-making-reports-pop/ More about types: • http://www.bidashboard.org/types.html • http://www.klipfolio.com/resources/articles/operational-analytical-bi-dashboards Each of them share common attributes of dashboards (data + visualization + UI), but each of them has some different purpose, data, and design best practices. Dashboard Uses and Examples • Where is it used? – Business management • Performance dashboard • Scorecard • Strategy map – Government report: The IT Dashboard https://itdashboard.gov/ • http://www.youtube.com/watch?v=4fe39dh6xFQ – Election: http://elections.nytimes.com/2012/results/president – Sports: http://games.idashboards.com/winter2010/?guestuser=vancouver – Operational performance monitoring: http://www.google.com/appsstatus • More real examples – http://www.klipfolio.com/resources/dashboard-examples/ – http://www.idashboards.com/solutions/ – http://dashboardsbyexample.com – http://www.dashboardzone.com 38 Reports • Reports – A report is the presentation of detailed data arranged in defined layouts and formats – Based on simple and direct queries: usually involves simple analysis and transformation of data (sorting, calculating, filtering, filtering, grouping, formatting, etc.) • Traditional reports contain detailed data in a tabular format and typically display numbers and text only. – Its purpose is mainly for printing (with styling) or exporting (raw data). – It is geared towards people who need data rather than a direct understanding of data. • Modern reports can be interactive and visual but the focus is still on detailed data. Sometimes the distinction is a bit blurred with dashboards in some practical cases. – http://www.crazybikes.com/mrc/CRAZYBIKES.R00090s – A report style “dashboard” (or more like a visual intensive interactive report): https://www.itdashboard.gov/drupal/summary/006 39 Visual Analysis Tools • A visual analysis/exploration tool is similar to a dashboard in that it uses visualizations intensively to drive data exploration or analysis. – Some consider it a kind of dashboard – Some consider it a bit deferent - http://www.dashboardinsight.com/articles/digital- dashboards/fundamentals/what-is-a-dashboard.aspx • Key characteristics of a visual analysis tool: – The visualization is usually a single (or very few) component that occupies a big portion of the screen as the main UI component, with a large number of data points visualized. – It is highly interactive and usually provides abundant settings and configurations (for adjusting factors and parameters) including filtering or sorting options. In fact, the number of setting combinations can be quite big. – It is not to visual key metrics, but to visualize patterns, trends, and other complex relationships among data. – It fits on one screen, but there may be scroll bars and zooming options. – It is primarily used for intensive data exploration or analysis, used by data analysts and researchers. • Example – http://luminocity3d.org/WorldCity – https://www.productchart.com – https://immersion.media.mit.edu/demo 40 Scorecards • A scorecard is special type of dashboard typically with a tabular visualization of measures and their respective targets with visual indicators to see how each measure is performing against their targets at a glance. – Scorecards are mainly used for performance monitoring with data directly related to measure, target, status etc. – Because of its uniqueness, it is viewed by some as special type of dashboard when used independently. – http://www.dashboardinsight.com/articles/digital-dashboards/fundamentals/what-is-a-dashboard.aspx • Key characteristics – Scorecards are mainly used for performance monitoring with data directly related to measure, target, status etc. – It mainly uses a tabular layout with embedded visuals like conditional formatting, sparklines (line, bar, or bullet graphs). – Limited interactivity (mainly on filtering and sorting to find score items) – It is commonly used in another tools like dashboards and visual analysis tools. • More information – http://www.dashboardinsight.com/articles/digital-dashboards/fundamentals/a-closer-look-at-scorecards- and-dashboards.aspx 41 Composition of Multiple Elements/Properties • More complex visual elements (such as icons and symbols) can be built based on the basic elements and properties discussed above. • Combinations of these properties can be used to represent multi-dimensional data in the same visualization. • Motions and animations (such as blinking, movement, spinning, etc.) are based on some dynamic changes of these properties, and they can be used for richer meaning and grab greater attention. 42 Interactivity • Interactivity is the functionality provided by the (visualization) system to let users interact with the system (visualization) through a user interface – So the visualization itself becomes dynamic based on user actions, providing different views of data. – It is an important aspect of data exploration and analytics, as both are interactive processes. – Interactivity is also essential in visual analytics where discoveries are driven by intensive interactions. • Why? – Quotes from chapter 1 of the book “Interactive Data Visualization for the Web” by Scott Murray • Static visuals can offer only pre-composed “views” of data, so multiple static views are needed to present a variety of perspectives (or stories) on the same information. • Representing multidimensional datasets fairly in static images is notoriously difficult. The number of dimensions of data are limited, when all visual elements must be present on the same surface at the same time. • A fixed image is ideal when alternate views are neither needed nor desired, and required when publishing to a static medium, such as print. • Dynamic, interactive visualizations can empower people to explore the data for themselves. • An interactive visualization that offers an overview of the data alongside tools for “drilling down” into the details may successfully fulfill many roles at once, addressing the different concerns of different audiences, from those new to the subject matter to those already deeply familiar with the data. • Interactivity can also encourage engagement with the data in ways that static images cannot. With animated transitions and well-crafted interfaces, some visualizations can make exploring data feel more like playing a game. Interactive visualization can be a great medium for engaging an audience who might not otherwise care about the topic or data at hand. – Make visualizations smart or tell a story: http://www.dataversity.net/fact-fiction-smart-data-visualization- tells-tale/ 43 Levels of Interaction • Lower UI system level: interaction is an operational method to support a functionality – Mouse and keyboard actions: click, drag, drop, hover, etc. – Touch gestures: tap, swipe, pinch, etc. • Higher application/behavior level: interaction is a user centered process toward a goal – Information seeking/exploration: • Visual information seeking mantra: overview, zoom/filter, details on demand • Browsing, highlighting, etc. – Analytical: inquiry or query • Relate, compare, connect, • Visual query • Interaction in a single visualization (a chart for example) vs. a system (a dashboard for example). Data Visualization and IT • Modern advanced and interactive visualizations are driven by the need for technology and tool support (design, development/programming, automation, delivery, and administration). • Michael Friendly offers these key points where information technology drives the modern data visualization: – Highly interactive statistical computing systems have been developed and are in common use. This is compared to early command-driven systems which used compiled, batch processing. – There are new methods for visual data analysis that have been implemented such as linking, brushing, selection, focusing, etc. that can be applied to interactive data models. – Data with large volumes of dimensions can be better analyzed thanks to the development of tools to do this such as the grand tour, the scatterplot matrix, parallel coordinate plots, etc. – There have been new techniques developed for graphing discrete, categorical data. – The field of information visualization has broadened to encompass many new forms of data, data structure and indeed problem solving. – The information visualization field has begun to implement understanding of the cognitive and perceptual aspects of displaying data in addition to delivering simple static visualizations which were aesthetically pleasing. – could have been created manually – in practice they require computing power to be manageable to develop cost-effectively. – https://www.interaction-design.org/literature/article/information-visualization-a-brief-20th- and-21st-century-history 45 Data Visualization Tools • Visualization products have been evolving fast, and there is increasing overlap. But they generally fall into three major categories. • Standalone tools – They are specifically designed to produce stunning visualizations, and can work with multiple platforms and data sources. – Some of them are growing to more full stack analytics tools. – Examples include Tableau, Power BI, Qlik, SpotFire, and others. They can be desktop based or cloud based (http://idashboards.com, http://www.klipfolio.com). • Embedded tools – Broader analytics, business intelligence, and reporting platforms that often incorporate visualization capabilities. These products can address more complex data platform needs and often provide wide-ranging capabilities but may require more training in order to exploit their full potential. In some cases, IT may need to be looped in to assist in integrating these tools with underlying data and related applications. – Examples like SSRS, IBM, Oracle, MicroStrategy, SAP Crystal, and others. • Visualization libraries or services – These tools are offered as programming libraries or services for general applications (web, mobile, etc.). – These tools can be useful when the visualization requires complete customization, substantial interactivity, or for developing a framework that allows you to reuse code. – Examples include D3.js, Google Charts, dotNetCharting, Telerik, Nevron, amCharts, etc. 46 We will cover these topics in details in module 3. The Industry • The data visualization market was valued at USD 4.51 billion in 2017, and is expected to reach a value of USD 7.76 billion by 2023 at a CAGR of 9.47% over the forecast period (2018- 2023). – https://www.mordorintellig ence.com/industry- reports/data- visualization-applications- market-future-of-decision- making-industry 47 Gartner Magic Quadrant for Analytics and Business Intelligence and Platforms 2018/2019 https://www.atscale.com/blog/magic-quadrant-for-analytics- and-business-intelligence-platforms-2018 Data Visualization (Dashboard) Tools • Enterprise reporting tools (usually as a part of the complete BI system) – SSRS, SAP Crystal, etc. • Standalone visualization tool (desktop) – Tableau: http://www.tableausoftware.com/public/ – Power BI – QlikView, Dundas, Spotfire, SAP Lumira, etc. • Cloud (web) based – http://idashboards.com – http://www.klipfolio.com/ • Embedded tools – Microsoft Excel, Visio – Google Docs Spreadsheet http://www.benlcollins.com/spreadsheets/d ynamic-dashboard-in-google- spreadsheets/ • Developer oriented libraries and APIs – Programming library:, D3, dotNetCharting, Telerik, Nevron, amCharts, etc. – Web API: Google Charts (https://developers.google.com/chart/) • Casual charting tools – Google Chart creators: http://dexautomation.com/googlechartgener ator.php – Other free online charting tools • http://www.onlinecharttool.com/ • http://nces.ed.gov/nceskids/createagraph (for kids) • More – http://selection.datavisualization.ch – https://www.g2crowd.com/categories/data- visualization – http://www.creativebloq.com/design- tools/data-visualization-712402 – http://www.computerworld.com/article/25068 20/business-intelligence/chart-and-image- gallery-30-free-tools-for-data-visualization- and-analysis.html – https://bigdata-madesimple.com/review-of- 20-best-big-data-visualization-tools/ 48 Notes on Excel • Excel is the best tool to learn and apply sound data visualization principles and best practices • “Many individuals and small business users will discover that MS Excel offers much of what they need without the need.” – Data Visualization - HorizonWatch 2015 Trend Report http://www.slideshare.net/HorizonWatching/data-visualization-horizon- watch-2015-trend-report-client-version-28jan2015 • Excel is the best tool for executive dashboard prototyping, because of its flexibility and development costs. – http://www.excelcharts.com/blog/prototype-executive-dashboard-excel/ • Main visualization features provided by Excel (practice these following the lab instructions) – Conditional formatting – Chart (Pivot Chart) – Slicer (visual filter) – Sparkline 49 Data Visualization Trends • Public communication with intensive visualizations - used creatively in many public media like – Journalism (US News Election coverage), – Government report (https://www.usaspending.gov/#/explorer, https://itdashboard.gov) • Mobile friendly visualizations • Interactive big displays 50 Career and Jobs • Data visualization has become a fast growing career and job, partly attributed to the rise of data science – https://blog.udacity.com/2014/11/data-science- job-skills.html – On the way to a data scientist https://www.datacamp.com/community/tutorials/l earn-data-science-infographic 51 https://mywebvault.wordpress.com/2017/05/18/modern- data-scientist-skill-set-marketing-distillery/ Skills in Data Visualization Dev. • Most important – Visualization design: charts, diagrams, etc. – UI and interaction design – Business domain knowledge, analysis • Highly useful – Programming – Data models – Data preparation – Analytics methods • Very helpful – Artistic design – Communication, story telling – Information behavior 52 Data Visualization: Sample Real Jobs The Data Visualization Analyst will be responsible for understanding the strategic needs of the business and translating high-level objectives into the development of visual data analysis and dashboards to support the category management and product strategy teams. The candidate will need to need to understand how to create and manipulate large data sets and use various visualization tools to meet the needs of needs of their customers. To ensure adoption by the business, this position will be required to ensure the quality of each dashboard release, data refresh and adhere to a regular refresh and dashboard publishing schedule. https://www.linkedin.com/jobs2/view/12915000 Data Visualization Analyst (originally posted on LinkedIn): • Responsible for the management of database analysis projects in support of business initiatives. • Data visualization (DV) expertise to design, develop and implement clear, interactive and succinct visualizations by processing and analyzing large quantities of (un)structured data. • Candidate should have ability to turn raw data into compelling, lively stories, enriched with powerful, clear visualizations. • These visualizations would also provide end-users an ability to discover relationships within related data in fresh and innovative ways. • Updates visualization items as defined by department, in accordance with system protocol and requests from relevant departments. • Serves as a liaison between business stakeholders and technology resources to optimize processes and designed visualization functionality. • Assists with user acceptance testing for new information dashboards and/or analytical systems. 53 Learning Data Visualization • IT 7113 Data Visualization – http://jackzheng.net/teaching/it7113/ – http://idi.kennesaw.edu/it7113/ – An elective course in the certificate on data management and analytics http://ccse.kennesaw.edu/it/programs/cert- dm.php • https://www.coursera.org/specializations/dat a-visualization – An online course provided by UC Davis on Coursera 54 Key Readings • Zheng (2017) Book Chapter Data Visualization in Business Intelligence (PDF downloadable): https://www.researchgate.net/publication/321804138_Data_Visualization_for_B usiness_Intelligence • Data Visualization for Human Perception (by Stephen Few): https://www.interaction-design.org/literature/book/the-encyclopedia-of-human- computer-interaction-2nd-ed/data-visualization-for-human-perception • Tegarden (1999) CAIS Business Information Visualization (a bit aged but still classic): http://aisel.aisnet.org/cgi/viewcontent.cgi?article=2483&context=cais • How Data Visualization Empowers Decision Making: https://community.watsonanalytics.com/wp- content/uploads/2015/04/BlueHill_HowDataVisualizationDrivesDecisionMaking_ Dec14.pdf • https://www.tableau.com/learn/articles/data-visualization • Friendly (2006) A Brief History of Data Visualization http://www.datavis.ca/papers/hbook.pdf 55 General Resources • Influencers – http://www.perceptualedge.com/ Stephen Few – https://www.edwardtufte.com/tufte/ Edward Tufte – https://en.wikipedia.org/wiki/Ben_Shneiderman – http://alignedleft.com Scott Murray – https://www.dataplusscience.com/insights.html Jeffrey Shaffer – http://people.ischool.berkeley.edu/~hearst/ – https://homes.cs.washington.edu/~jheer/ – https://bost.ocks.org/mike/ • Communities and organizations – http://www.visualizing.org/ – http://www.interaction-design.org/ – http://flowingdata.com/ – https://plus.google.com/111053008130113715119 – https://plus.google.com/112388869729541404591 – https://www.linkedin.com/topic/data-presentation – https://www.darkhorseanalytics.com/blog/ – TED videos: https://www.ted.com/playlists/56/making_sense_of _too_much_data • Company resources – https://www.tableau.com/learn/articles/data- visualization – https://www.sas.com/en_us/insights/big-data/data- visualization.html – http://blog.visual.ly/ – https://www.darkhorseanalytics.com • News, blog, magazines – http://mashable.com/category/data-visualization/ – http://searchbusinessanalytics.techtarget.com/resour ces/Data-visualization-tools – https://www.interaction- design.org/literature/topics/information-visualization – http://hbr.org/special-collections/insight/visualizing- data – http://apandre.wordpress.com/ – http://nbremer.blogspot.com/ – http://understandinggraphics.com/ – https://plus.google.com/photos/+AndreiPandre/albu ms/5481981245951662737?banner=pwa – https://visage.co/blog/ – https://informationisbeautiful.net/ – http://www.storytellingwithdata.com • Books – Stephen Few, Show Me the Numbers, https://www.amazon.com/dp/0970601972/ – “Information Dashboard Design” 2nd, by Stephen Few, 2013, http://www.amazon.com/gp/product/1938377001/ – “Introduction to Information Visualization”, by Riccardo Mazza, Springer, 2009, ISBN 1848002181 – “Business Dashboards: A Visual Catalog for Design and Deployment”, by Nils Rasmussen, et al. ,Wiley, 2009, ISBN 0470413476 – http://www.amazon.com/Information-Graphics- Comprehensive-Illustrated- Reference/dp/0195135326 – Designing Data Visualizations, by Julie Steele, Noah Iliinsky, O’Reilly, 2011 56

Lecture notes for IT 7113 data visualization updated in fall 2019.

About Jack Zheng

Associate Professor at Kennesaw.edu

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