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Demystifying Advanced Data Visualization

By   /  February 22, 2017  /  No Comments

Advanced Data Visualization gives a new meaning on how pictures can simplify information needed to comprehend complex questions.  Angela Hausman states that Big Data does not mean much if the people who control change can’t understand or have to spend too much time deciphering the Great Data that is presented. In addition, Big Data speeds across the Internet, captured from people and the Internet of Things (IoT) including items such as appliances, GPS, and building maintenance. This Big Data constantly updates, second by second, providing not a static picture, but a dynamic movie.

Organizations, need to find ways in keeping up with this Big Data in order to understand their customers better and to move much more quickly, smoothly, and efficiently. Take health insurance coverage in the United States. How has it changed over the course of time? An Excel line or bar graph may show the details that more Americans have gained health care coverage between 2008 and 2015. But the Advanced Data Visualization, provided by the US Census bureau, gives a clearer sense of the trends, through an animated U.S. map. Advanced Data Visualization provides a tool to keep up with and make sense of Big Data in timely manner.

What is Advanced Data Visualization?

The Gartner IT wraps Advanced Data Visualization into the term Advanced Analytics. Advanced Data Visualization refers to a sophisticated technique, typically beyond that of traditional Business Intelligence, that uses “the autonomous or semi-autonomous examination of data or content to discover deeper insights, make predictions, or generate recommendations.”

Advanced Data Visualization displays data through interactive data visualization, multiple dimension views, animation, and auto focus.

Advanced Data Visualization fills the need when 2-dimensional graphics and one screen just does not handle the information as well or results in slower comprehension of the data. For example, an interactive mind map of the Internet of Things, allows users to visualize what kinds of things, how IoT will be used, and what technologies are involved. Advanced Data Visualization becomes useful only when it is the simplest way to describe the business problem at hand and obtain Business Intelligence.

Advantages of Advanced Data Visualization

  • Users Interact with Data

Users need to present their business needs and to see what story Big Data tells. Advanced Data Visualization does this through interaction. For example, an Advanced Data Visualization tool, called Linkurious, helped the International Consortium of Investigative Journalists (ICIJ) investigators identify people who have been involved in tax fraud, by hiding their money in Switzerland. Anybody can go to the ICIJ site and explore countries or people who were involved with the Swiss leaks, through a simple interface. As a different example, Information is Beautiful presents an Advanced Data Visual map describing the major player in the IoT. People can click on different bubbles, in this map, to zoom into details about specific IoT businesses.  Advanced Data Visualization provides an interface for people to search through and integrate Big Data to get meaningful results.

  • See Multiple Big Data Points on One Screen

Evelson states in his blog that “Even with the smallest reasonably readable font, single line spacing and no grid, you can’t fit more than a few hundred numbers on the screen.” Advanced Data Visualization allows a person to fit more Big Data points by stacking the data. For example, DrasticData uses the MBO-Scanner to create an interactive site for the Dutch Ministry of Education. This tool allows user to “browse through a huge amount of data on educational institutes and the many different courses organised by these institutes.”

To see multiple data points on one screen corresponding to different geographical regions, Advanced Data Visualization, helps. Reuters is aware of this advantage and so partnered with Graphiq to create Open Media Express. An example, from Open Media Express shows the impact of the new drive to seek big budget, staff cuts at the Environmental Protection Agency (EPA), through an interactive map of the United States.

  • Handles Dynamic Data Well

A store wishes to track inventory to stock items, meeting customer demand, and reduce waste. At any given moment, store employees stock shelves with goods to be sold and shoppers pay for merchandise that they take: “Inventory Control is one of the more obvious advantages of the Internet of Things”.  Also, other businesses, from automotive to medical devices, collect an endless stream of data from devices. This kind of dynamic data works well with Advanced Data Visualization techniques, including interactive dashboards that update information in real time. The drive for dynamic data has been the impetus for Glassbeam to partner with Tableau 10, to provide data on the Internet of Things and for Space-Time Insights to provide real-time data through a virtual reality platform. Applications, like Space-Time Insights, use Advanced Data Visualization to help make sense of “large volumes of data, that occur frequently.”

Alternatives to Advanced Data Visualization

Advanced Data Visualization only works when helping users understand how Big Data addresses a business need. If done poorly, Advanced Data Visualization results in information overload, increased expense and unnecessary complexity. Edward Tufte, a leader in Information Visualization would agree.  Tufte states, “The minimum we should hope for with any display technology is that it should do no harm.”  Applying Advanced Data Visualization to some problems does more harm in presenting information. For example, a company needs to track pharmaceutical approvals by the Federal Drug Administration to figure out market potential. Since, it can take up to 10 months to approve a drug, an application providing multiple dashboards or that allows the user to zoom into multiple data points may be overdoing it. A simple Excel pie chart or pivot graph would present the information more simply.

Consider the following before investing in Advanced Data Visualization software:

  • Chart Junk

“Chart Junk” as described by Edward Tufte, refers to visual elements that muddle the presented information, or even misinform. Chart Junk is like the magician that executes a good coin trick by misdirection. Unfortunately, Chart Junk continues. Kaiser Fung’s Blog, Junk Charts provides great examples of chart junk, including issues with Big Data. For example, Fung demonstrates that a Wall Street Journal graphic claiming an IPO deal drought, in 2016, skews the reader towards the metric of performance after IPO.

  • Expense

It should be of no surprise that per Computerworld’s Forecast 2017 survey, organizations expect to increase spending on Advanced Data Visualization. Companies that make Advanced Data Visualization Tools have seen revenue grow. As stated by Ghosh, “Big Data Analytics Sales Will Reach $187 Billion by 2019.” To keep pricing reasonable, consider strategies such as defining clearly the Business Intelligence problem at hand and making great use of free software trials. Better still, look at open source options or every day applications. When a simple budget, addresses your Business Intelligence needs, a standard spreadsheet program like, Excel does the trick.

  • Expertise Needed

Some Advanced Data Visualization techniques and tools require an advanced skill set or a lot of training to put into use. As Lisa Charlotte Rost states, “We Still Live in an ‘Apps Are for the Easy Stuff, Code is for the Good Stuff’ world.” Rost provides a helpful diagram categorizing the learnability and flexibility of Advanced Data Visualization tools. As of 2014, more than over a billion people have learned to use a simple spreadsheet like Excel. That number is always increasing. The typical admin assistant knows how to create simple pie charts, displaying information easily for anyone to understand. Other Advanced Data Visualization techniques require advanced software programming . Presenting Advanced Data Visualization to a general audience will be best served with an intuitive application and more frustrating if it requires expert knowledge of data analysis.

Advanced Data Visualization techniques provide interaction, a way to see a large array of data, and a method of keeping track of fast changing Big Data collections. It has helped identify fraud and simplified business information pertaining to the Internet of Things. But, Advanced Data Visualization methods work better after understanding the data at hand and how it is to be used from asking good questions.

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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