ISSN Vol.04,Issue.14, June-2015, Pages:

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1 ISSN Vol.04,Issue.14, June-2015, Pages: SACHIN VAIDYA 1, AVINASH CHAVAN 2 1 Assistant Professor, Dept of MCA, A.C Patil College of Engineering, Navi Mumbai, India, smvaidya@acpce.co.in. 2 PG Scholar, Dept of MCA, A.C Patil College of Engineering, Navi Mumbai, India, avinash3192@gmail.com. Abstract: Data visualization is the presentation of data in a pictorial or graphical format. For centuries, people have depended on visual representations such as charts and maps to understand information more easily and quickly. As more and more data is collected and analyzed, decision makers at all levels welcome data visualization software that enables them to see analytical results presented visually, find relevance among the millions of variables, communicate concepts and hypotheses to others, and even predict the future. Because of the way the human brain processes information, it is faster for people to grasp the meaning of many data points when they are displayed in charts and graphs rather than poring over piles of spreadsheets or reading pages and pages of reports. Keywords: Data Visualization, Internet. I. INTRODUCTION Data visualization or data visualization is viewed by many disciplines as a modern equivalent of visual communication. It is not owned by any one field, but rather finds interpretation across many (e.g. it is viewed as a modern branch of descriptive statistics by some, but also as a grounded theory development tool by others). It involves the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". A primary goal of data visualization is to communicate information clearly and efficiently to users via the statistical graphics, plots, information graphics, tables, and charts selected. Effective visualization helps users in analyzing and reasoning about data and evidence. It makes complex data more accessible, understandable and usable. Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look-up a specific measure of a variable, while charts of various types are used to show patterns or relationships in the data for one or more variables. Interactive data visualization goes a step further moving beyond the display of static graphics and spreadsheets to using computers and mobile devices to drill down into charts and graphs for more details, and interactively (and immediately) changing what data you see and how it is processed. Interactive charts and graphs make it easier for decision maker across all organization to: Identify areas that need attention or improvement. Understand what factors influence your customers behavior. Know which products to place where. Predict sales volumes. Discover how to increase revenues or reduce expenses. II. WHY IS DATA VISUALIZATION IMPORTANT? Visualizations help people see things that were not obvious to them before. Even when data volumes are very large, patterns can be spotted quickly and easily. Visualizations convey information in a universal manner and make it simple to share ideas with others. It lets people ask others, Do you see what I see? And it can even answer questions like What would happen if we made an adjustment to that area? Consider the manufacturing director of product reliability for an international company that produces small vibrating cell phone motors. One of the director s principal responsibilities is to determine how reliable the cell phone motors will be with each year of age. If the product s reliability falls short of the standards set forth by the cell phone manufacturers who use the motors, his company could lose major contracts. Some of the issues which we generally come across while visualizing the data: A. Spreadsheets Are Hard To Visualize Because of the amount of data collected on the age and reliability of the cell phone motors, a traditional electronic spreadsheet cannot visually represent the information due to data presentation limitations. And, if printed out, the spreadsheets would be a humongous pile of paper on the director s desk. In both cases, the director would spend hours searching among thousands of rows and columns of data with still no concrete answer to the original question about the relationship between the motor s age and its reliability IJSETR. All rights reserved.

2 B. Data Visualization Makes Interpretation Easier Data visualization presents the data in a way that the director can easily interpret, saving time and energy. For example, the graph above shows the number of units that correspond to each age (represented by the color gradient) as well as the reliability as the age of a unit increases. In a matter of seconds, the director can see that units approaching 10 years of age are approximately 40 percent reliable. This visual simplifies the data, instantly clarifying the factors affecting the reliability of the cell phone motors. III. COMMON TECHNIQUES FOR DATA VISUALIZATION There are a few basic concepts that can help you generate the best visuals for displaying your data: Understand the data you are trying to visualize, including its size and cardinality (the uniqueness of data values in a column). Determine what you are trying to visualize and what kind of information you want to communicate. Know your audience and understand how it processes visual information. Use a visual that conveys the information in the best and simplest form for your audience. Data visualization is an art and a science unto itself, and there are many graphical techniques that can be used to help people understand the story their data is telling. IV. COMMON FORMS OF DATA VISUALIZATION A. 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. B. 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. C. 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. V. MAKING BUSINESS DECISION EASIER WITH DATA VISUALIZATION 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 SACHIN VAIDYA, AVINASH CHAVAN 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. We can make business decision easier by making quick analysis of the data and then taking the action according to the data A. 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. 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. B. 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. 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;

3 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. VI. DATA VISUALIZAION TECHNOLOGIES Technologies like High Charts, Pikyh uses Data as taken an innovative approach to addressing the challenges associated with the visualization of data. Prepare Data: manage data, load and join data and create calculated columns. Explore Data: perform ad hoc, interactive data exploration to discover new insights. Design Your Visuals: create reports and graphs that visually convey your discoveries. Deliver Visualizations To Mobile Devices: easily share visualizations with others via the web, pdfs or mobile devices. VII. PROBLEM IN DATA VISUALIZATION It s an age-old problem in business. A mishap occurs, like a flawed batch of products that are produced due to an as-yetunknown manufacturing glitch. Downtime to analyze and fix the malfunction could take hours or days, depending on the severity of the problem and whether defective manufacturing parts have to be ordered, shipped, and installed. A. Collecting the Data & Describing the Problem Depending on the nature of the business and the items being produced, even a temporary halt in production could cost a manufacturer thousands, perhaps even millions, of dollars. Where do decision makers turn? Data visualization tools are great first steps. Data visualization capabilities can strengthen decision making by enabling operational and business leaders to quickly identify the nature of a sudden problem, as well as the factors that are contributing to it. In the case of the aforementioned manufacturing glitch, data visualization tools can be used to gather all the pertinent information. That data includes equipment vibration levels, the age of the manufacturing equipment being used, temperature readings and other environmental conditions, maintenance history, and any recent changes in the quality or types of materials being used in production. Data visualization and predictive analytics tools can be used to assemble all of the applicable information and data inputs available, and illustrate the most likely factors that are contributing to the production errors. One example is excess friction during one phase of the manufacturing process. multistate outbreak of E. coli leads public health and agriculture officials to identify the source of the outburst and a means of responding to it. After gathering medical records and the ages, locations, and recent dietary patterns of patients who have been treated visualization tools can help bring to light that the majority of persons suffering from E. coli illnesses had eaten romaine lettuce that was sold primarily through the same grocery chain across different states. From there, public health and agriculture officials are able to identify the farm, or farms, where the lettuce was produced to ensure that farm workers, distributors, and grocery staff are educated on the proper produce handling and hygiene recommendations. Let s consider an altogether different scenario: Executives for a national clothing retailer discover that profits for a popular line of blouses have suddenly plummeted in the Midwest. Data discovery and data visualization techniques enable business leaders to quickly ascertain that a four-day-only discount on the blouses wasn t properly reset for nearly 250 stores in a six-state region. This forced store managers to accept the marked (discounted) price on the garments. By quickly identifying the nature of the problem and making the necessary price corrections in the company s point-of-sale system and across its affected inventory, the retailer is able to avert any additional losses. VIII. VISUALIZING BIG DATA Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale. Big data brings new challenges to visualization because large volumes, different varieties and varying velocities must be taken into account. And, in many cases today, data is just being generated faster than it can be digested. There are quite a few factors to consider. The cardinality of the columns you are trying to visualize is a factor. Cardinality is the uniqueness of data values contained in a column. High cardinality means there is a large percentage of totally unique values (e.g., bank account numbers, because each item should be unique). Low cardinality means a column of data contains a large percentage of repeat values (as might be seen in a gender column). Big data can be described by the following characteristics: Volume: the quantity of data that is generated is very important in this context. It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered big data or not. The name big data itself contains a term which is related to size and hence the characteristic. B. Solving the Tough Problem Visualization techniques can help executives see why the production problem occurred, enabling them to get to the root cause of the issue and take action swiftly. Of course, data visualization tools and techniques can be applied to any number of business issues as they arise. For instance, a Variety: the next aspect of big data is its variety. This means that the category to which big data belongs to is also a very essential fact that needs to be known by the data analysts. This helps the people, who are closely analyzing the data and are associated with it, to effectively use the data to their advantage and thus upholding the importance of the big data.

4 Velocity: the term velocity in the context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development. Variability: this is a factor which can be a problem for those who analyse the data. This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively. Veracity: the quality of the data being captured can vary greatly. Accuracy of analysis depends on the veracity of the source data. SACHIN VAIDYA, AVINASH CHAVAN shown in Fig.1. By taking their data and turning it into visual content, users are more likely to engage with and share it. An Example showing People accessing mobile through internet in infographics format as shown in Fig.2. Complexity: data management can become a very complex process, especially when large volumes of data come from multiple sources. These data need to be linked, connected and correlated in order to be able to grasp the information that is supposed to be conveyed by these data. This situation, is therefore, termed as the complexity of big data. IX. EVOLUTION OF BIG DATA Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. Although big data doesn't refer to any specific quantity, the term is often used when speaking about petabytes and exabytes of data. Data visualization is the method of consolidating data into one collective, illustrative graphic. Traditionally, data visualization has been used for quantitative work (info graphics are a popular example) but ways to represent qualitative work have shown to be equally as powerful. Everyday, our attention is being fought for. Data visualization excels in capturing a viewer s attention and holding it through storytelling. Fig.2. People accessing mobile through internet. Another simple example :What happens in an Internet Minute as shown in Fig.3? Fig.1. Data visualization excels in capturing a viewer s attention. It addresses a complex problem that could be easily looked over, and simplifies it using design. Naturally, a new market for business has emerged. Companies likevisual.ly have recognized the value in representing research data in an innovative way and have built a platform to connect designers, data experts, and marketing managers with businesses who want to share their research findings as Fig.3. What happens in an Internet Minute. Understanding this pictorial form of data will be much easier than churning through large amount of data in database. X. PROBLEM WITH BIG DATA AND SOLUTION TO IT A. Problems Handling large amount of data from large database all around the world in internet. Deriving meaning from this data. Converting it into human understandable format for advance business analysis. B. Solution A technology which can read large amount of data from database chunk by chunk and represent it as visual graphical or pictorial format. It may not need to be a traditional graphs and chart, it could be an infographics which understand the data set and represent in innovative format.

5 XI. CATAGORIES OF DATA VISULAITAION TECHNOLOGY There are two main categories of data visualization technology: visual reporting and visual analysis. A. Visual Reporting Visual reporting uses charts and graphics to depict business performance, usually defined by metrics and time-series information as shown in Fig.4. The primary type of visual report is a dashboard or scorecard, which gives users a visual snapshot of performance. The best dashboards and scorecards enable users to drill down one or more levels to view more detailed information about a metric. In essence, a dashboard is a visual exception report, highlighting performance anomalies using visualization techniques. For Example: XII. CONCLUSION Current Data Visualization Technology include HighCharts and pyikh which use data set to represent data in chart format But my propsition inculde representing data in infographic way which can fetch huge data from database and represent it in inforgahic way which can help even lay-man to understand the complex set of data. XIII. ACKNOWLEDGMENT I would gratefully and sincerely appreciate my supervisor: Prof. Sachin Vaidya. Their inspiring guidance, rich experience and sustained encouragement enabled me to develop an intensive understanding of my research area. Without the generous help of my supervisor, this work would not have been possible. I am honored to have Prof. Sachin Vaidya from A.C.Patil College as my opponent. I thank him for his kind support and helpful suggestions during the discussions in my MCA. Fig.4. Visual reporting. XIV. REFERENCES [1] Techniques. [2] [3] [4] [5] [6] [7] [8] [9] B. Visual Analysis Visual analysis, on the other hand, enables users to visually explore data to discover new insights as shown in Fig.5. While visual reporting structures the navigation of data around predefined metrics, visual analysis provides a much higher degree of data interactivity. With visual analysis, users can visually filter, compare, and correlate data at the speed of thought. Visual analysis tools also often incorporate forecasting, modeling, and statistical, what-if, and predictive analytics. For Example: Fig.5. Visual analysis.