Business Data Visualization

Jan 10, 2020 | Publisher: Jack Zheng | Category: Technology & Engineering |  | Collection: Data Visualization Lecture Notes | Views: 5488 | Likes: 17

Data Visualization for Analytics and Business Intelligence A Comprehensive Overview IT 7113 Data Visualization J.G. Zheng Spring 2020 This lecture notes file is hosted at Overview Topics: • What is (business) data visualization? – What are the types of visualization? What are the related terms and fields? How are they similar or different? • Data visualization in business intelligence and analytics – Basic data visualization elements and forms (types) • Data visualization and IT – Design, development, delivery – Applications and tools • Skills, jobs, and career • Learning resources 2 This lecture notes provides a high level overview of data visualization primarily used in business intelligence and analytics. This overview is comprehensive and covers as many aspects as possible, but it keeps them at a high level. More details are provided in additional lecture notes. Visualization • Visualization is related to vision (seeing through eyes), one of the major human senses to interact with the world • Visualization is the process of forming a visual image of things that can be seen through eyes (or imagined in human mind). • What 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 entity: data, information, idea, hierarchy, process, relationship • How to visualize them? – Types/features of visualization forms – 2D vs. 3D – Static vs. motion – Virtual vs. materialized – Realistic vs. abstract • The visualization process (and the result) may change the original form of things (or create a new form) for better understanding and communication. 3 A visualization of “visualization” 4 I see … (or I can imagine) Invisible reality Abstract entity Visualized forms: 2D vs. 3D Static vs. motion Virtual vs. materialized Realistic vs. abstract Visible reality Hidden reality Visualized forms may be modified from their original forms or completely new (for invisible or abstract things). Data Visualization • Data visualization is the graphical representation and presentation of data for the purpose of perception and understanding • Data are typically numerical values (can be qualitative) that describe its associated entity or activity. • Data itself is abstract. The visualization process will create visible forms to represent the meaning of these abstract data. – Utilizing a combination of visual elements (shapes and symbols) and visual variables/properties like size, color, positions, etc. • In modern systems, the process is more interactive. 5 A Visualization of “Data Visualization” 6 The cognitive visualization process in human brain and elements of visual mapping is covered with more details in another module Key readings • Visual Mapping – The Elements of Information Visualization: elements-of-information-visualization • The Properties of Human Memory and Their Importance for Information Visualization: https://www.interaction- Data Visualized data Data Visualization Purposes • 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 and cognition - comprehension of abstract ideas and processes – * Information seeking - browsing, navigation, exploration, discovery – * Data analysis and insight generation – * Decision support – * Event or operation monitoring – Communication, presentation, story telling, impression/persuasion – Artistic (beauty) expression and appreciation – Entertaining and for fun 7 * The focus of this course Visualization Needs and Usages • Visualization is needed in many cases. Depending on the use case, we may need different tools with different features. • Major use cases – Presentation: static presentations in meetings – PowerPoint – Reporting • Regular/seasonal reports for casual business users - reports • Real-time or near real-time reporting - dashboard • Interactive reporting and exploration by power users – interactive reports or dashboard. • Executive reporting and decision making - dashboard – Analytical • Used in the process of analysis, accompanying queries and calculations - Excel • Advanced visual driven analysis, often used for research – Power BI/Tableau – Monitoring: real-time operational monitoring (driving, manufacturing) - dashboard – Public communication/journalism • Tell a story to the public gerrymander-map - web – Demonstration/simulation: interactive demonstration for complex scenarios - 8 Related Terms and Fields • Data presentation • Information design, information visualization (including infographic), illustration • Computer graphics, reality visualization (VR, AR), scientific visualization • Business data visualization • Big data visualization 9 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: • • 10 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 Design and Visualization • Information design is the practice of presenting information in a way that fosters an efficient and effective understanding of the information. – These include elements like layout, flow, use of text style, bullets, spacing, etc. – • 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 – • Infographics is a specific type of information visualization that are 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). – – – Often used in mass communication (e.g. journalism) and marketing • 60000-words-01126256 • • See more resource about information visualization – 11 Information design Information visualization infographics Infographics vs. Data Visualization • Major differences vs. data visualization – 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 to communicate or impress casual viewers – 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 – – Infographic-and-a-Data-Visualisation 12 Illustration of an Idea/Concept These can be considered as information visualization but NOT data visualization. 13 More examples of information visualization can be found at • • • • • recognized-for-great-infographics/ • • Computer Graphics (CG) • Computer graphics is computer generated graphics created using computers, mainly based on computing logic and algorithms. • Applications – Virtual Reality or Augmented Reality – Game, movie – Science 14 Scientific Visualization • Physical science 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.” – Visualization (simulation) of reality (universe, sun, explosion, atom, climate, etc.) – • Mathematical model/algorithm visualization – the visualization created based on math calculations and models – 15 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 (simulating) a real world phenomenon as close as possible, but just representing abstractly an activity, patterns, trends, clusters, outliers, and gaps – 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 – following standard practices – Utilizes data binding techniques to generate visualizations in an automated way (as part of an analytics software application) • 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 16 [Yes] Business Data Visualization • Periodical reports – • Performance dashboards – • Visual data exploration and seeking – – – • Visual analytics – • Real time monitoring – esults-dashboard-live.html 17 [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 – • Mathematical visuals – – • Scientific visualization – • Reality simulation – • Infographics/data graphics – – (not all but many are) – (not all but many are) – (not all but many are) • Too much artistic (visual embellishment) – 18 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 19 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.” visualization-outing-hype.html • What’s the purpose of big data visualization? And what’s the effective way to use them? – – 20 Why Data Visualization? • Visualizing is basically a human physiological and psychological capability, and plays an important role in human information behavior and decision making – Recall or memorize data more effectively – Enable fast perception based on instinct (see the figure on the right) – Helps data comprehension and enhance problem solving capabilities (cognition) – Extract/provoke additional (implicit) perspectives and meanings – Ease the cognitive load of information processing and exploration – Help to shape the attention and focus – 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 Identify structures or relationships – More comprehendible with familiar visual context – Identify structures and relationships that are hard to express in words 21 A picture is worth 1000 words (clicks) Source: How Data Visualization Empowers Decision Making 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? 22 Monthly average temperature Monthly average precipitation Quickly Focus on Area of Interests • Which stock performed different from others? 23 More Comprehendible with Familiar Visual Context 24 Text (non visual) Visual Geo map is the recognizable familiar visual context 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 25 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. us/2018/06/08/the-state-of-business- intelligence-2018/#b2fca2878289 Basic Visual Forms/Styles used in BI and Analytics 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 – 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. – • Examples – – – 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 – • 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 charts and design in details in three other modules. Choose a Chart – Figure from or – Online chooser with templates: 31 Summary of Categorizations and Lists Versions Reference Comments Andrew's version 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 on-chart-types/ Influenced by Andrew’s version, and added two more categories: evolution (like trend) and profiling. Juice Analytics chooser/ Provided as an interactive online chooser with templates. Categorized similarly but added an “trend” category. Ferdio An interactive resource with a lot of examples. Included diagrams and maps. Categorization by function is similar to the two above. Charlr 313773/which-chart-type-works- best-for-your-data Similar categories; added “deviation”. An interactive tool is provided with simple descriptions and examples. Data catalog An interactive catalog with very detailed description for each chart. Added many more smaller and specific categories. From Data to Viz 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: • Reference/dp/0195135326 • 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. – – • Common examples used in business information visualization – Flow chart: – Network graph: – Tree diagram: – Time line: – Structure diagram: – More: • Examples of those with quantitative data – Sankey diagram 33 Maps (Location based Visualizations) • Location intelligence (LI) is a business intelligence (BI) tool capability that relates geographic contexts (usually as a dimension) to business data. – • Location based visualization (map) is the base for location intelligence and plays an important role in business intelligence. – involves layering multiple data sets spatially, for easy reference on a map – Maps provide context … Quickly associate data with familiar position/location – added familiarity increase comprehension. (Tableau “The Power of Where” – Maps are widely recognized and understood—even by nontechnical professionals—which helps make the data represented more accessible and understood. (Forbes “The Power of Place” • Like business intelligence, location intelligence supports analysis and decision making. But for the past 20 years, these two data-centric disciplines have forged independent but parallel paths. Only now are they beginning to converge. The explosion of mobile and IoT devices facilitates the integration of business and location intelligence. – The first step toward converging location and business intelligence is plotting business metrics on a map. – The next step is the interactive process of location driven visual analytics, utilizing more sophisticated mapping layers and data presentation, even on three-dimensional surface, with the help of VR/AR technologies. – Major Map Types • Geo map – Visualize geo location related data directly on real world maps – Data represented as points, areas, paths – – • 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: your-state-gets-its-money/ – Geo chart: election-night/ – Road/path map: • 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. – studies/ The details of maps are covered in the mapping module Dashboard • Elements of a dashboard – Data/information: the most important element – Visual: data visuals (charts, etc.) provide an high level at-a-glance view – User interface • a clean UI that unifies all elements to work together as a whole • supporting interactions as needed • The Values of Dashboard – Dashboards are a data visualization tool that allow all users to understand the analytics. For non- technical users, dashboards allow them to participate and understand the analytics process by compiling data and visualizing trends and occurrences. – Provides a one-place presentation of critical information – Allow decision makers to see a variety of data that affects their divisions or departments • This allows decision makers to focus only on the items over which they have control • The dashboard is generally customized for each user – Quickly understand data and respond quickly at one place • Save time over running multiple reports – More 36 The two dashboard modules provide more details: Dashboard = data/information + visual + UI A dashboard is a visual-oriented display of the most important data and information needed to achieve defined goals and objectives; consolidated and arranged on a single screen so the information can be viewed at a glance. Adapted from: Dashboard Confusion, Stephen Few, Dashboard vs. Report • 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. – It is geared towards people who need data rather than a direct understanding or interpretation of data. – Its purpose is mainly for printing (with styling) or exporting (raw 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. – A report style “dashboard” (or more like a visual intensive interactive report): – Magic Quadrant report vs. – Dashboard or report? 37 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? – Enable multiple perspectives • Static visuals can offer only pre-composed “views” of data, so multiple static views are needed to present a variety of perspectives on the same information. A fixed image is ideal when alternate views are neither needed nor desired, and required when publishing to a static medium, such as print. - Quotes from chapter 1 of the book “Interactive Data Visualization for the Web” by Scott Murray. – Reduce complexity • The number of views can grow significantly in many cases because of the multi-dimensionality of the data. Presenting all of them is impossible. Even presenting multiple of them maybe cluttered and crowded. – Enables exploration • Dynamic, interactive visualizations can empower people to explore the data for themselves. – Encourage engagement with the data • With animated transitions and well-crafted interfaces, some visualizations can make exploring data feel more like playing a game or telling a story. 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: visualization-tells-tale/ 38 Levels of Interaction • We can view interactivity in three levels Level Description Examples Lower level: UI system Interaction is an operational feature provided by the underlying system. These are usually determined by the hardware (and some software part). • Mouse and keyboard actions: click, drag, drop, hover, scrolling, etc. • Touch oriented: tap, swipe, pinch, etc. • Touch gestures Mid level: sensory enhancement Interaction supports the general purpose of sensory enhancement to ease the cognition load. These are the most generic and fundamental interaction actions. Most of these needs the support of the software application tools used for data visualization. • Highlighting • View switching • Zooming • Pop-up • Motion control Higher level: application/task Interactivity directly supports a user centered process toward a goal (such as decision making). These are more complex and usually consists of multiple steps and lower level interaction features. • Searching and browsing • Data exploration • Inquiry or query • Analytical: relate, compare, connect, etc. We will cover this topic in details in module 9. 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 (https://www.interaction- 21st-century-history) – The field of information (data) visualization has broadened to encompass many new forms of data, data structure and indeed problem solving. – 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 – 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. 40 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 (, • 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. 41 We will cover these topics in details in the tools module. Data Visualization Tools • Enterprise reporting tools (usually as a part of the complete BI system) – SSRS, SAP Crystal, etc. • Standalone visualization tool (desktop) – Tableau: – Power BI – QlikView, Dundas, Spotfire, SAP Lumira, etc. • Cloud (web) based – – • Embedded tools – Microsoft Excel, Visio – Google Docs Spreadsheet ynamic-dashboard-in-google- spreadsheets/ • Developer oriented libraries and APIs – Programming library:, D3, dotNetCharting, Telerik, Nevron, amCharts, etc. – Web API: Google Charts ( • Casual charting tools – Google Chart creators: ator.php – Other free online charting tools • • (for kids) • More – – visualization – tools/data-visualization-712402 – 20/business-intelligence/chart-and-image- gallery-30-free-tools-for-data-visualization- and-analysis.html – 20-best-big-data-visualization-tools/ 42 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 reports/data- visualization-applications- market-future-of-decision- making-industry 43 Skills in Data Visualization Dev. • Data visualization draws knowledge and experience from multiple fields including: computing, business, and design. • Most important – Visualization design: charts, diagrams, maps, etc. – UI and interaction design – Business domain knowledge • Highly useful – Programming/scripting – Familiarity of the tool – Data models – Data preparation – Analytics methods • Very helpful – Artistic design – Communication, story telling – Information behavior 44 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. 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. 45 Data Visualization Trends • Public communication with intensive visualizations - used creatively in many public media like – Journalism (US News Election coverage) – Government report (, • Visualization intensive stories (narrative with creative and interactive data visualizations) – gerrymander-map – theres-hope/ • Dashboards and visualizations in more types of display media and interfaces – Mobile friendly visualizations – Interactive super big displays – VR/AR environments 46 Interesting read from Elijah Meeks visualization-hit-the-mainstream-d97685856ec Learning Data Visualization • IT 7113 Data Visualization – – Open educational resources at – An elective course in the KSU MSIT and “certificate on data management and analytics” • Other good courses – UC Davis on Coursera visualization 47 Key Readings and Resources • Zheng (2017) Book Chapter Data Visualization in Business Intelligence (PDF downloadable): n_for_Business_Intelligence • Data Visualization Lecture Notes – • Data Visualization for Human Perception (by Stephen Few): human-computer-interaction-2nd-ed/data-visualization-for-human- perception • Tegarden (1999) CAIS Business Information Visualization (a bit aged but still classic): • How Data Visualization Empowers Decision Making: content/uploads/2015/04/BlueHill_HowDataVisualizationDrivesDecision Making_Dec14.pdf 48 General Resources • Influencers – Stephen Few – Edward Tufte – – – – Mike Bostock – Scott Murray – Mark Jackson – Jeffrey Shaffer – Ryan Sleeper – • Communities and organizations – – – – TED videos: _too_much_data • Company resources – visualization – visualization.html – – • News and magazines – – ces/Data-visualization-tools – https://www.interaction- – data – – – – • Books – Stephen Few, Show Me the Numbers, – “Information Dashboard Design” 2nd, by Stephen Few, 2013, – “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 – Comprehensive-Illustrated- Reference/dp/0195135326 – Designing Data Visualizations, by Julie Steele, Noah Iliinsky, O’Reilly, 2011 49

Lecture notes for IT 7113 data visualization updated in spring 2020.

About Jack Zheng

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