What are the Techniques and Challenges of Big Data Visualization in Information Systems Essay? This study examined what big data means with its importance of it and its usage in each industry along with visual analytics to drive success in their organization. Various types of big data analytics tools such as Tableau, PowerBI, SAS, etc. along with the comparison of the tools to discover the best fit based on a profile of a company and its goals also cover. We tried to examine how data visualization tools helped big technological giants to achieve competitive advantage taking care of the challenges that big data brings into visualization.
Here is the article to explain, Techniques and Challenges of Big Data Visualization in Information Systems Essay!
By 2025, it predicts that the value of data will increase by 10-fold. Virtually, every branch of industry or business will generate a vast amount of data. Thus, the world will experience aggressive growth and data could be a missed opportunity when not being utilized. And to make matter worse, the rate of collecting and storing data is faster than the ability to use them as tangible decision-making. With the help of ever-growing technology, visionaries are creating visualization methods to help turn raw data with no value into informative data.
Big data has served a purpose for organizations to optimize their businesses. With an abundant amount of data that organizations generate every day, the ability to turn the data into a decision effectively and efficiently is crucial. Thus, the knowledge of analytics and visualization would come hand-in-hand to tackle the problem in big data. Hence, a new interdisciplinary research field of “Visual Analytics” is being established, in which its aims to make the best possible use of the information by combining intelligent data analysis with visual perception. The visual analytics knowledge has been quite useful to the two most common streams of the profession in the Big Data world, Data Scientists and Business Analytics.
Business Analytics;
Business Analytics (BA) define as a data-centric approach that relies heavily on the collection, extraction, and analysis tools to enable data to use as an insight as well as decision-maker; which in most disciplines, is being used by top-management people. Previously, BA existed used to report what has happened in the past, although nowadays, with the massive volume of data that can generate; BA can exploit them to predict the future and also make breakthroughs.
Data Science;
Through Big Data, the need to create a reliable source of information and a business support system has invented a new and widespread business application of Data Science. However, the art of data science is multifaceted, it combined the skills of computer science, advanced analytical and statistical skills, and knowledge of methods of visualizing data.
One of the main thing that visualization can help is projecting a model that data scientist has built to the reader. They usually play with data that has hundreds of dimensions that do not have the usual mapping point thus standard visualization such as bar chart, will not work.
Big Data Visualization Essay;
In the Visual Analytics Process above, the data that has been collected is being transformed according to the streams. For the Business Analytics (BA), the transformed data is mapping into a visualization for a user to process into knowledge, usually in a form of decisions; then the knowledge is feedbacking into the data for continuous improvement and to enable analysts to a better conclusion in the future.
For the Data Science (DS) stream, the transformed data is mining to build a model; that would help certain objectives, the overall approach of the data is problem-agnostic. When certain models have been built; they would need to visualize as well, or vice versa. There is a feedback loop in between models and visualization to get the right outcome for the objectives. Furthermore, the knowledge comes from either visualization or models themselves.
In general, visualization works as a better and faster way to identify patterns or trends; and any correlation that would otherwise remain undetected with a text or numbers figure. And visualization also helps to approach the problem in a new and creative way; that would tap into the human’s cognitive brain to understand the information hiding behind a huge number of data. The human can also interact with the visualization; which can utilize to find more insights or to find the right questions.
Techniques in Big Data Visualization;
According to user requirements, the visualization techniques decide. Conventional visualization makes use of tables, Venn diagrams, entity-relationship diagrams, bar charts, pie charts for data visualization. Below is the list of visualization techniques for visualizing large amounts of data and getting insight into it are:
- One-dimensional; It consists of one value per data item or variable. The histogram is the perfect example of it.
- Two-dimensional; As the name suggests, it has two variables. Bar charts, pie charts, scatter plots, maps are the type of 2D visualization.
- Three Dimensional; This visualization will give more information to the user in the form of slicing techniques, Iso-surface, 3D bar charts, etc.
- Multi-Dimensional; It will give a clearer picture of the visualization by analyzing the variables from a different perspective. Parallel coordinates, Auto graphics, etc. are the type of such visualization.
- TreeMap; Here the data neste in form of the rectangle which represents each branch of the tree.
- Temporal Technique; It has the scalability of displaying the data in a timeline, time series, and scatter plot.
- Network technique; It use when you want to present data collected from social media in the form of a network.
Challenges for Big Data Visualization or Visual Analytics;
The main challenge with visual analytics is to apply visual analytics to big data problems. Generally, technological challenges such as computation, algorithm, database, and storage, rendering along with human perception; such as visual representation, data summarization, and abstraction are some of the common challenges. “The top 5 challenges in extreme-scale visual analytics” as addressed in the publication by SAS analytics are as follows:
- Speed requirement; In-memory analysis and expanding memory should utilize to address this challenge.
- Data understanding; There must be proper tools and professionals; who are proficient in understanding the data underneath the sea to make proper insight.
- Information quality; One of the biggest challenges is managing large amounts of data and maintaining the quality of such data. The data needs to understand and presented in the proper format that increases its overall quality of it.
- Meaningful output; Using the proper visualization technique according to the data presented is necessary to bring meaningful output to the data.
- Managing outliers; While you cluster the data for favorable outcomes; it is obvious that an outlier will exist. Outliers cannot neglecte because they might reveal some valuable information and must treate separately in separate charts.
