Data Visualization in Business













Joseph E. Moreno




    Data Visualization in Business has been around since the early sixty’s.  We can go on and define it in many ways. Here are a few definitions: Data Visualization is an emerging market space that is not well defined and is a breed of products that feature a graphic component and a data component (Don Nachtwey).  Stewart Deck defines Data visualization as the graphical representation of a data collection, often in an interactive form.  Modern data visualization tools present data to users as charts, graphs or maps and let users sort, subdivide and combine groups of data in graphical form to help discover patterns and illustrate what they’ve discovered.  Data Visualization is the use of graphics to make sense of the reams of data that are available for analysis and decision making (Peter L. Brooks).  I will say that data visualization is a collection of data that is for information relative to the subject seeking shown through the use of graphic’s such as bar, line, and pie charts. 

We have used data visualization since computers were born.  Back then the use of collecting data was immanent, but they didn’t know what to do with it.  Finally, the hardware was available to make graphics and the results produced statistical graphics that could be used to analyze data.  For example, Chang (1970) explored rotations in 5-D to detect 2-D structures.  Since then we have fast computers and software to use real time graphs and make strategic business decisions which can help businesses cut costs and make profits.  With this in mind we will always be innovated and spend money for the tools in data visualization.

Background of Data Visualization

    Since the late 1960’s, research in statistical graphics and the invention of the computer has lead to the development of a basic form of data visualization.  It wasn’t even called this until they had a solid form of it.  For the first time moving pictures of data were displayed and a user was able to interact with a plot in real time.  For example Kurskal (1964) watched a multidimensional scaling algorithm converge to a stable configuration; and Fowlkes (1969) explored interactive probability plotting. In 1974 Fisherkeller, Friedman, and Tukey were the first to create seminal software that was the first dynamic multivariate data visualization system. This software was called PRIM which means Picturing, Rotation, Isolation, and Masking. It had tools for drawing plots, rotating variables into the plots, and conditionally masking points according to variable values.  The first data graphics required considerable low-level programming to make the graphics terminals draw. An example, as verbally communicated by Andrus Buja, is that John McDonald “programmed Orion (McDonald 1892) in Pascal on a Sun board and in Mortran on the IBM-360 emulator. The Sun board had no operating system, but it had some Pascal routines that acted as a raster display device driver.  This was a nightmare, but thankfully operating systems have developed considerably.  Some include MacOS, X11 for UNIX workstations, and Microsoft Windows.  Similarly, programming languages have also evolved such as, formula-based, like Fortran, to objected-oriented schemes for organizing complex systems, like C++.

Data Visualization differs from information visualization, scientific visualization, and cartographic visualization.  Information visualization is broader than data visualization.  It seeks to visualize more generally unstructured information-for example, visualizing lines code in software (Eick 1994).  Scientific visualization is primarily concerned with visualizing 3-D, or 3-D + time phenomena, such as for medical purposes, displaying molecular structure of drugs, or in construction projects, displaying architectural prototypes.  It involves more physical realism.  Cartographic visualization concerns visualizing maps, geography, and spatial domains.  But these types of visualizations are not mutually exclusive, and indeed it is common that data arise in conjunction with a geographic component, or from restructuring lines of code into counts of particular expressions, or from databases of chemical properties of molecules.  So it is common that data visualization needs to be done simultaneously with other types of visualizations (Peter Sutherland).

There are three basic fundamentals in forming data visuals.   One fundamental principle is that a display should be focused on data.  While this seems like an obvious concept, it is one that is often ignored.  Designers often clutter up icons and make the chart hard to understand, and what it is trying to visualize.  The second is the order of data can have a significant impact on a viewer’s ability to understand it.  When you design a graph, chart or user interface, resist the temptation to add extraneous graphical elements that don’t clarify or expose the deeper meaning of the data (Robert Craig). If you follow the basic principles, you will create data visuals which will insure your decisions.

The purpose for data visualizations is to help analyze data and put it into an easy to read graphic. It is for the purpose of everyday business user who relies on the examination of business statistics to keep ahead of the competition.  Visualization technology allows everyday users to easily see trends, determine patterns and spot anomalies in large amounts of data without requiring high-end workstations, statistical knowledge or specialized training.  Findings are displayed in easy-to-understand, animated graphical multidimensional reports and presentations, employing a combination of advanced video, digital imaging and animated multi-dimensional graphs (Humberto C. Gerola).  Data Visuals help make decisions on current information and they are as accurate as the information lets it be.  Overtime data visualization tools will cover more areas of business and thus increases the purpose for using it.  

Data Visualization in the Present

Our ability to understand data and make business decisions based on it is being undermined by the sheer growth in both the breadth and depth of data in most organizations (Peter Brooks). Another sector heralding the benefits of data visualization is the dealers and resellers, who can take advantage of its easy-to-use graphical interface in showing customers multitude of features and advantages this technology has compared to its predecessors (Humberto C. Gerola).  An analyst armed with access to data warehouse, knowledge of statistics, an understanding of the data, and a data visualization tool can find data relationships that would not be readily apparent using simpler tools that deliver only two dimensional bar, pie, or line charts.  Analysts use advanced data visualization capabilities to interrogate, explore, and display data using sophisticated charts, multidimensional images, and numerous screen controls (Peter L. Brooks).

Data Visualization in the Data Warehouse

Although you can use data visualization tools in a standalone environment, they are most effective when used to analyze information contained in a data warehouse.  By providing improved analysis capabilities to warehouse users, the data visualization tool increases the value that is obtained from the warehouse.  The data warehouse, by containing cleansed and consistent data, increases the effectiveness of data visualization tool to perform data scrubbing.  Data mining is one area in which the use of advanced data visualization products is growing.  Data visualization, by itself, can be an entire form of data mining application.  Following this approach, an analyst would build numerous data displays to determine the most meaningful graphics.  The differentiating factor in using data visualization rather then machine-based discovery data mining methods is that data visualization lets you directly incorporate human ingenuity and analytic capabilities into the data mining process.  Other data mining techniques—machine-based discovery approaches such as statistical regression, rules-based reasoning, and neural networks—use mathematical calculations to identify interesting data relationships (Peter L. Brooks).

Data Visualization Technology

  Two primary types of tools are used to develop advanced data visualization development applications: specialized programming languages and GUI exploration and development tools.  Development using data visualization programming languages, which sometimes work in concert with GUI tool, is performed using the following steps:

1.  Extract all data or a subset of data from its source into the data visualization tool environment.

2.  Explore the data with the data visualization explorer tool.

3.  Identify key visualization needs and user interactions.

4.  Use the programming language to develop customized graphics and user dialogs.

5.  Add the developed applications to the GUI tool menu or accessible library.

  Most advanced data visualization GUI tools let developers access and analyze data, select visualization graphics from a predefined set of templates, customize the graphics, and then add the graphics to a library for access by end users.  Data visualization development with these tools is usually performed iteratively using the following steps:

1.  Extract all data or a subset of data from its source into the data visualization tool environment.  Generally, once data is loaded, all graphics are available for browsing and exploration.

2.  Customize graphic templates that are of interest.

3.  Explore the data by looking at graphics, changing the color scheme, rotating the graphics, and/or zeroing in on interesting areas for further investigation.

4.  Add the selected graphics to the GUI tool menu or accessible library.

There are many forms of data visualization tools or software.  I am going to name a few in the following paragraph.  Visible Decisions’ Information Animation consists of the Discovery for Developers object oriented toolkit and Anna, a dynamic interpreted object-oriented programming language. Visible Decisions’ development methodology consists of the following steps:

1. Acquire data.   

2. Model data.

3. Attach views.

4. Create interactive controllers.

5. Create a landscape.

  Belmont Research offers two primary data visualizations products: CrossGraphs and CG++.  CrossGraphs lets you simultaneously explore data by displaying statistical graphics partitioned across selected dimensions.  The result a series of graphs displayed on one screen is used to understand data relationships that either would not be found by looking at single, simple charts or would require an excessive effort compared to CrossGraphs. See figure 1 (Appendix A) shows a CrossGraph preview window displaying bar graphs that show multidimensional retail sales by store, product category, week, and type of promotion. To develop an application, you perform the following steps:

1.     Create a new project that contains all of the application GUI and source code files.

2.     Define and build the application using the GUI Builder, Source Code Editor, and Browser.

3.     Test and modify the application using the Debugger Console.

4.     Optimize performance and generate workspaces and/or C++ for shared libraries.

SAS Institute positions its data visualization software, SAS/Spectra view, as a key component in the Explore step of its Sample-Explore-Manipulate-Model-Assess (SEMMA) data mining process. Spectra view is an interactive high-volume visualization tool for viewing, exploring, and analyzing large amounts of multidimensional data. (SAS's Insight product lets you perform interactive data visualization of histograms, scatter plots, box plots, and other statistical graphics against smaller amounts of data with under 10,000 observations.)  SAS/Spectra view consists of three major functions to produce advanced data visualization: data loading and filtering, image coloring, and volume visualization. All functions are performed using pull-down menu bar commands (see Appendix B).  You can customize the color of data, text, missing values, and other image attributes. The most interesting technique is specifying data value colors. Users map specific colors to specific data values by using a data ramp.  Once data is loaded into Spectra view, a bounding box (consisting of the outlines of the 3D data visualization surface that contains all variable values) is displayed and volume visualization can proceed. All data visualization manipulation is done within the confines of the bounding box.  You can fine-tune graphs by using rendering controls. "Rendering" is a term used to describe the conversion of a set of points to presentation graphics (Peter L. Books).

The latest edition of the Interactive data language makes programming easier and does not require the traditional edit-compile-link-debug cycle of other languages.  Previous versions of the language required builders to learn syntax and write code to create their applications.  IDL 5.0, however, features an objected-oriented prebuilt graphic user interface for direct access to common language functions.  Another added feature, IDL Object System, supports the language’s new interface and architecture, and helps provide users with a consistent set of abilities through a common syntax among objects.  The system supports encapsulation, polymorphism, multiple inheritance, and persistence (David Herman).

Welcome to the world of 3-D data visualization.  While 3-D technology has been around for several years, it is just beginning to appear in a enterprise-level business applications, from decision support to infrastructure management.  “We believe that 3-D will become the standard,” says Marc Sokol, senior vice president of advanced technology at Computer Associates Int’l Inc.  In part, Sokol says, the acceleration adoption of 3-D technology is being made possible by improvements in desktop computers.  Applications that previously could run on $100,000 Intergraph workstations can now be displayed on $3000 PCs, he explains.  Some other companies that implement 3-D technology in their Windows NT-based products include Research Systems Inc. (Boulder, Colo., with Environment for Visualizing Images, and image processing application for analyzing remote sensing data; Cartia Inc. (Redmond, Wash., with ThemeScape 1.0, which evaluates unstructured text-based data and creates an interactive topographical map of the information; and Interactive Network Technologies Inc. (Houston, with J/View3D toolkit, designed for building 3D visualization applications in Java (Michele Rosen).

The latest version, 5.0, features enhanced data visualization capabilities that will aid users in visually detecting data trends and anomalies, company officials said.  Greg Tuker-kellogg, a senior scientist at Millennium Predictive Medicine Inc. in Cambridge, said Spotfire’s added support for visual-trellis and split-plotting capabilities would help in comparing different data sets with one another.  “Up until now, you could only view the same data in different ways,” he said. “Now, you split the data adjacent visualizations and work with different subsets of the data, which is important when looking at data with many variables.”  Tariq Andrea, a senior researcher at Phamacopeia Inc., a $104 million chemical development and drug discovery firm in Princeton, N.J., said he plans to use’s visualization capabilities to help spot the degree of diversity among chemical combinations (Lee Copeland).  Spotfire’s software is the first to combine both “data visualization” and powerful querying flexibility.  Known as DecisionSite, the software isn’t cheap-installations start at $100,000.  Recently IBM’s life sciences division put its marketing muscle behind the product: Big Blue is combining its data-management software with Spotfire’s tools in a package aimed at drug companies that hope to speed up their R&D.  The magic in Spotfire’s software is that it lets users easily do what-if comparisons of data from different sources by moving sliders on a computer screen with a mouse.  In effect, it gives these data disherman infinitely variable nets to trawl with; they can search beneath the waves for fish no longer than three inches, say, and then, with a quick adjustment, separate the anchovies from the sardines for comparison.  The results appear as brightly colored bar graphs, pie charts, scatter plots, and even maps.  When Spotfire rolled out its software four years ago, it aimed first at the drug industry, where the data explosion has been immense (Stuart F. Brown). 5.0 Capabilites

·        More than 1 million records can be analyzed visually.

·        It provides access to relational databases such as Oracle, Microsoft Access, Sybase and Informix.

·        It offers support for visual-trellis plots and split-plots views (Lee Copeland).

Visualization tools can be useful in three areas, says Michael Embry, lead analyst for data warehousing at retailer AutoZone Inc. in Memphis.  “They can help extend statistical analysis, extend graphical presentation tools and be used as analytical applications by themselves,” says Embry, who has tested visualization tools from Ottwawa-based CogosInc.  Some of the top vendors:

·        Visual Insights

·        Silicon Graphics Inc.

·        Cognos Inc.

·        DataView Inc.

·        Epiphany Inc.

·        Quadstone Ltd.

·        MapInfo Corp.

·        Environment Systems Research Institute Inc.

·        MathSoft Inc.

·        Spotfire Inc.     (Stewart Deck)

Data Visualization technology has been making a splash in the risk management arena, and is expected to increase in visibility with the recent merger of two of the lending players in the field. Analyzing the flood of information that comes through financial services firms is a difficult task; therefore, being able to see a snapshot of the data in the form of a chart or graphic is becoming a welcomed accessory.  One area where data visualization has already proven its worth is risk management.  “Apart from spotting defective data, it’s a way of examining all sorts of relationships portfolio—some will mean nothing but suddenly you might spot an unsuspected correlation,” says Catherine Morley, principle consultant at TCA Consulting in London (Andy Webb).

Of course with all this new software being created we need new hardware to power the software.  I am about to talk about the latest advances in computer hardware as they relate to data visualization, modeling, and simulation.  The HP visualization center sv6 consist of a cluster of graphics-enabled workstations interconnected with both a high-speed LAN at the SPU side and a digital compositor at the graphics side.  The system includes three functional components; the master system, 3-D rendering pipelines, and the image compositor.  The master system runs the application and usually controls the Xserver and distributes the 3-D rendering to the multiple 3-D rendering pipelines.  The 3-D rendering pipelines are responsible for rending to a portion of the full application visible frame buffer.  These pipelines enable the user to define a screen space division for distribution of application tendering requests.  Finally, the image compositor takes the sub-screen rendering of each pipeline and recombines the multiple streams into a single screen image for presentation.  The Sun blade 1000 is designed for a number of applications, from software development and R&D to 2-D content creation, MCAD/MCAE and embedded systems.  It supports Sun Creator3D graphics, Sun Elite3D M6 Graphics, and Sun Expert 3D graphics, which provide high-performance graphics with texture-mapping acceleration for 3-D applications in geotechnical, high-end MCAD, digital content creation, visualization and simulation.  But given that collaboration is increasely necessary in today’s development environment, the company recently introduced Visual Area Networking, which allows users to interact with visualization supercomputers using any client device, individually or as a collaborative community.  It removes the requirement that either the data or the advanced visualization capability be local to the user and allows diverse teams of people to visualize and interact with data.  Apple’s recently-released Power Mac G4 with dual 1-GHz PowerPC G4 processor can be used for complex data visualization, particularly when partnered with Velocity Engine in Mac OS X clusters.  By providing high-powered systems that can be clustered into supercomputers, Apple insures that its products will gain wider use in scientific visualization (Kim Sekel).  With time passing by and the use of data visualization becomes more worthy, the software and hardware will adapt to the demands.

I would like to offer four consumer standards that make sense.  First, data visualization products are end user tools.  They don’t include products that are defined as data base management, data ware housing, data acquisition, etc.  Second data visualization products must have a graphic component that is tightly integrated with a data source.  Products that create lines charts and graphs from spreadsheets and other desktop tools are not data visualization products.  In addition changes to the data should be reflected in the graphic component.  Third, data visualization products are inherently analytical.  Products that render 3D imagery based on some specification data should be classified as design software and not data visualization.  Fourth, data visualization products should do more than simply plotting the data.  Plotting data is typical for historical analysis.  Data visualization should have some decision components (what if capabilities, parametric graphics, etc.) (Don Nachtwey).

Visualize the Future

    Data visualization graphics and techniques are being used to present information to users in new and novel ways. Advanced data visualization tools are designed to provide graphics beyond those of the simple business charts that can be created by Visual Basic, PowerBuilder, spreadsheets, and OLAP tools. 3D graphics, real-time animation, and intense user interaction and ability to customize graphics are some of the characteristics of these tools. Users of advanced data visualization tools generally require training in statistical analysis and should have a deep understanding of the data being analyzed. This is unlike tools with simpler visualization capabilities that are easily understood by the casual user. Strong statistical analysis skills and detailed data knowledge are required to build such graphics.  The performance of these tools is directly related to the size of the data being processed, the complexity of processing, and the hardware platform being used. Over time, performance will certainly increase, but currently most tools are constrained by the previously mentioned factors. Data sampling, simplifying graphics and using slower animation are techniques currently used to overcome the performance inhibitors.  As the volume of available data grows and business graphics become commonplace, an advantage will accrue to the organizations that are able to more quickly make sense of their data -- a capability that requires human involvement and interpretation. Even when using machine-based discovery data mining techniques that can process vast quantities of data, you must analyze the results -- the answer does not automatically appear. Advanced data visualization allows for the interactive interpretation and analysis of large amounts of data that cannot be derived from columns of numbers and that is not effective when displayed in simple charts (Peter L Brooks).


    In researching all this information the thing that I learned is data visualizing is a powerful and successful tool in business and any other fields who benefit from it.  From the research some ideas have pop in my head to make data visualization tools better for the future.  Imagine software that can take information from where ever and break it down to graphs in real time.  There is such software, but it can’t take data and do future predictions through sampling over the past quarters or years.  Have you ever heard of the saying “That history repeats itself”?  Now with that concept, apply it to the new futuristic data visualization software called JEMS Predictions, and do some sampling from previous quarters.  After that hit a button on the tool bar, boom there it is your future prediction of the company.  This prediction will do the calculations through multiple databases from your competitors and it will predict future sales, profits, expenses and so on with real time data from those databases.  It sounds good and can be possible with some research and development.  Currently all of this data now is visualized giving the users a where about in the company for the past or in the present.  My idea is to take the two and come up with a future prediction.  This is the future of data visualization.   








References , Sophisticated Graphic Visualization and Development Tools Tailored for Business Application. By Peter L. Brooks, Visualizing Data. , Defining Data Visualization, by Don Nachtwey - Thinx Software.

Kim Sekel. “Computer Hardware for Visualization”, Scientific Computing & Instrumentation, February 2002, pg. 20-24.     

Andy Webb. “Visualization Lends a Hand to Risk Arena”, Wall Street & Technology, Apr.2000 Vol.18 Issue 4, pg 40.

Stewart Deck. “Data Visualization: Hot Trends & Technologies in Brief Definition”, Computerworld, 10/19/99, Vol.33 Issue 41, pg 37.

Lee Copeland. “Tools Enables Data Visualization and Trend Analysis”, Computerworld, 8/14/2000, Vol. Issue 33, pg 55.

Stuart F. Brown. “Making Decisions in a flood of data”, Fortune, Aug.13 2001, Vol. 144 Issue 3, pg 148B.

David Herman. “Language for Data Visualization”, Mechanical Engineering, Jun 97, Vol. 119 Issue 6, pg 16.

Michelle Rosen. “Data Visualization in Three Dimensions”, ENT, 12/09/98, Vol. 3 Issue 20, pg 44.

Humberto C. Gerola. “Data Visualization Analysis Business Information”, Computer Dealer News, 3/12/99, Vol. 15 Issue 10, pg 28.

Robert Craig. “Data Visualization Principles”, ENT, 10/06/99, Vol. 4 Issue 17, pg 25.

Sutherland, Peter; Rossini, Anthony; Lumley, Thomas; Lewin-Koh, Nicholas; Dickerson, Julie; Cox, Zach; Cook, Dianne; “Orca: A Visualization Toolkit for High-Dimensional Data.” Computational & Graphical Statistics, Sep2000, Vol. 9 Issue 3, p509, 21p.




Appendix A.


Figure 1.

--A CrossGraphs preview window displaying bar graphs that show multidimensional retail sales data by store, product category, week, and type of promotion. (Courtesy Belmont Research Inc.)










Appendix B.

Figure 2.

--A SAS/Spectraview BarChart object with controls to visualize employee health data. The data includes observations for age, height, weight, fat, cholesterol, and blood pressure; at the bottom is the color selector object used to assign the colors. (Courtesy SAS Institute Inc.)