Visualization Tools for Data Analysis
What Is Data Analysis? Data analysis is the process by which cleaning, analyzing, and interpreting quantitative data reveal valuable insights that lead to more effective and wiser business decisions. Analysis of data has been called the heart of statistics since it allows business professionals to make informed decisions about business practices, customer demands, and product innovations. Companies rely on data analysis to improve management, decision making, product development, marketing, operations, and financial health.
What Is Data Mining? Another use of data analysis is data mining, which is the process by which users mine information for predictions or discovering specific patterns from the massive amounts of data available. Predictive and descriptive features of data mining are two different methods of mining information for patterns and trends. In one case, users mines data for patterns such as language, age, occupation, hobbies, geographic location, and so on. In another case, users mines for descriptive features, like the popularity of chocolate candy or the number one cause of death.
Data mining and data analysis have both positive and negative impacts on business intelligence (BI) capabilities. It can open the door for intelligence. However, these techniques may not be enough if business analysts lack creativity and they don’t possess the talent and experience for identifying the right analytical questions to ask. The main key to success in business intelligence is the ability to balance the need for analytic skills with the creative capacity to look for patterns and relationships that haven’t yet been revealed through the more traditional forms of business analysis.
What Are the Benefits of Data Visualization?
Data visualization, or the process by which data is visualized, can be seen as the most significant part of the data analysis process. Many analysts find the process of data visualization to be the most interesting. Not only do the results look good, but the process itself may also provide a business analyst with a new perspective on the problem that they are attempting to solve, because they are no longer concentrating on only the lower level analysis.
But what is the main article about data analytics and data visualization? Why should someone care how their business is being analyzed? In this main article we will cover the main points.
Data mining and analysis using basic machine learning techniques is the first step toward being able to provide business intelligence (BI) solutions through data mining. Unfortunately, for business analysts this means either spending thousands of dollars on in-house programmers, or outsourcing the analysis process to some other company. Data visualization makes it possible for analysts to use the cheaper but equally effective, process of data mining without the high cost programming models. This is important because businesses need to make reliable profits while still staying competitive.
Data visualization allows you to visualize the data mined from a data mining project in a way that makes sense. Instead of trying to figure out which variables are important to a business, an analyst can analyze data in terms of its predictive value. For instance, if there is high-level risk in one area, then you can predict that other areas with low risk will also see drops in value. It is the same if you are looking for trends in customer satisfaction. You might want to consider analyzing the customer satisfaction levels over time, versus the overall average in order to understand the ups and downs of customer satisfaction. This lets you identify trends in customer behavior.
Data visualization is so powerful that many analysts actually prefer to analyze the big data using simple regression analysis graphical models instead. The big data visualization allows you to visualize the data without the tons of unnecessary information clutter, which can be overwhelming to a novice analyst. Regression analysis statistical models are great tools for quick and easy visualizations of large data sets. Just by choosing a regression model, you can plot the data and visualize it quickly. In order to get the most out of your data analysis, it is best to stick to the basics and start with a simple model and then as you gain more experience, move to the more complicated models.