2.0 COMMON FORMS OF DATA VISUALIZATION

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1 The Importance of Data Visualization to Business Decision Making by Rebeckah Blewett, Product Manager, Dundas Data Visualization, Inc.Sunday, June 12, INTRODUCTION Informed decision making is the foundation upon which successful businesses are built. As a decision maker for your business, you need access to highly visual business intelligence tools that can help you make the right decisions quickly. As your organization grows, so does the amount of collected information. If this data is delivered to you in spreadsheets or tabular reports, it becomes more and more challenging to find the patterns, trends and correlations necessary to perform your job well. Effective data visualization is an important tool in the decision making process. It allows business decision makers to quickly examine large amounts of data, expose trends and issues efficiently, exchange ideas with key players, and influence the decisions that will ultimately lead to success. The practice of representing information visually is nothing new. Scientists, students, and analysts have been using data visualization for centuries to track everything from astrological phenomena to stock prices. Only recently, with the adoption of more sophisticated BI technology in the corporate world and the ever-increasing practice of data collection and data mining activities, has data visualization in the form of dashboards been used as an important presentation tool in business analysis. As a result, the use of dashboards in making quick and accurate business decisions has become an essential requirement for remaining competitive. 2.0 COMMON FORMS OF DATA VISUALIZATION Basic Charts The most recognizable and utilized form of data visualization is the basic chart. Line, bar, area and pie charts represent the most common types of this form. The first function of a good chart is to allow decision makers to examine the data and reduce the time required to extract key information.

2 Figure 1 - Line/Area Combo chart comparing sales cycles for current and previous year to demonstrate patterns and trends Figure 2 - Pie Chart displaying the distribution of reasons that purchasers have made a decision to buy a particular product. Status Indicators In addition to basic charts that visualize a set or sets of data, status indicators are also a commonly used visualization to indicate the business condition of a particular measure or unit of data. These indicators can take on many forms, including gauges, traffic lights or symbols. Status indicators become even more effective when they incorporate contextual metrics, such as targets and thresholds, because they can provide quick feedback as to whether a specific measure is good or bad, high or low, below or above target. Advanced Data Visualizations More advanced examples of data visualization include scatter graphs, bubble charts, spark line charts, geographical maps, tree maps, Pareto charts, and many others. These more sophisticated visualizations are designed to display data in ways tailored to a specific function or industry.

3 Figure 4 A tree map that uses size and color to indicate distribution of sales (size) and performance against target (color). In this case, the color gold indicates best performance. 3.0 Quick Analysis Successful visuals that depict measurable, actionable data allow decision makers to easily pinpoint and examine outliers. They also allow quick analysis to expose patterns, correlations, business conditions and trends. In Figure 5, a sales manager for a museum gift shop is presented with typical traditional spreadsheets showing monthly sales revenues for the current year and the number of visitors for each monthly museum exhibit in separate spreadsheets. The numbers presented clearly show that sales for the months of January and June 2010 were significantly lower than target. However, it is very difficult and time consuming to pinpoint the possible patterns or trends that may shed light on these anomalies. Further analysis would be needed before a problem could be identified and any action is taken, if required.

4 Figure 5 - Traditional spreadsheets displaying monthly sales revenue and visitor traffic to the museum separately. In Figure 6, the sales manager is presented with the same information in a visual format which already includes the additional contextual data that is needed to make key observations and draw conclusions. In this example, poor sales in January and June is correlated with the number of visitors attending major exhibits as indicated by the inclusion of monthly major exhibit traffic as a contextual metric. Another observation that can be made is that the drop in revenue during these months may represent a pattern as similar drops occur in 2009.

5 Figure 6 - Line/bar combo chart showing the correlation between the number of exhibit visitors and gift shop revenues, as well as a possible trend and sales cycle based on last year s data. In the time it would take to locate, read, assimilate and process sales data located in a traditional spreadsheet, a manager receiving that same information in a visual format, with the appropriate context, complemented by additional related information (possibly from different sources) could have already started making decisions to improve performance. Figure 7, below, shows a gauge that indicates page hits for a particular website landing page. The position of the needle between the start and end points make the number itself look fairly high, or at least greater than the midpoint, which most people would instinctively associate with midway or so-so. In essence, the value represented in this gauge (425) looks better than average (250). Figure 7 A radial gauge showing monthly web hits. This Figure gauge 8 - Radial example gauge does showing monthly web hits are not hitting expected not trigger a call to action because feedback is not given target in regards prompting to a target a manager to investigate the problem and take action. or expected result. Analysts who do not know what the target should be or who do not have the background information to assist them, will interpret this gauge differently than someone who has additional knowledge of the situation. This leads to confusion, missed opportunities and loss of time. However, if you add context to the gauge in the form of a target and adjust the scale of the gauge so that the start and end points are more in line with that target, you can clearly see that the number of hits of this landing page is clearly lower than desired. Context allows a story to be told by the data without the risk of misinterpretation and allows everyone to come to the same conclusion.

6 4.0 Take Action Decision makers need to interact with their data to expose trends, highlight opportunities and raise red flags quickly and accurately. Their data should answer key questions and provide insight into issues that contribute directly to the decision making process. Presenting this data visually and adding contextual information to complement the analysis process not only makes it quicker and easier to pinpoint areas of opportunities and concern, but also enables decision makers to take action with their data. Successful data visualization provides the ability to expose problem areas and communicate those problems universally. Not being able to clearly identify and share your discoveries to back up your decisions can mean the difference between taking appropriate and decisive action and losing momentum or failing to act. Figure 9 Gauges showing the volume and distribution of support requests across different mediums. In this example, a manager can see a possible problem with support volume and respond in a timely manner. 5.0 Conclusion Using data visualization to display large amounts of data is nothing new. However, its value and use in making business decisions is often overlooked or poorly implemented. The key to success in using data visualization is ensuring that: the best and most appropriate types of visualizations are used; the data is always put into perspective with contextual information allowing for the information to be universally understood; and that the data being measured within the data visualization enables the user to take action based on the observations being made. With a good set of visuals that keep these key success factors in mind, decisions can be made more quickly and with more confidence so that your business can continue to grow.

ISSN 2319-8885 Vol.04,Issue.14, June-2015, Pages:2717-2721. www.ijsetr.com

ISSN 2319-8885 Vol.04,Issue.14, June-2015, Pages:2717-2721. www.ijsetr.com ISSN 2319-8885 Vol.04,Issue.14, June-2015, Pages:2717-2721 www.ijsetr.com SACHIN VAIDYA 1, AVINASH CHAVAN 2 1 Assistant Professor, Dept of MCA, A.C Patil College of Engineering, Navi Mumbai, India, E-mail:

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