Data is everywhere, in many forms. Companies around the globe, generate vast volumes of data daily, in the form of log files, web servers, transactional data, and various customer-related data. In addition to this, social media websites also generate enormous amounts of data. New research on "The State of Data Science and Analytics" has confirmed that "data is the lifeblood of digital transformation with over 80% of organizations leveraging data across multiple organizational processes." There are so many data that the questions we ask of it are limited only by our own imaginations.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it.
Data analytics is the process of exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations, and derive valuable insights to make business predictions. It improves the speed and efficiency of your business. Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems.
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. Data analysts and data scientists rise to meet the demands of everyday business by exploring with deeper kinds of analysis, enabling them to answer questions about what happened, but how, where why and what will happen in future. The ability to ask any question of your data can be incredibly inspiring. The true first step begins at the base of the analytics process: how an analyst preps and blends their data. How they prep and blend their data not only determines the speed at which they can answer questions, but also the kinds of questions they can answer at all.
Types of Data Analysis:
There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:
Text Analysis is also referred to as Data Mining. Data mining began in the 1990s and is the process of discovering patterns within large data sets. It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall, it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.
Statistical analysis answers the question, “What happened?” by using past data in the form of dashboards. This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:
Descriptive analysis works with either complete or selections of summarized numerical data. It utilizes means, deviations, quartiles, percentages and frequencies in data.
Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.
Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis , analysts use diagnostic analysis to identify behavior patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, may have chances to use similar prescriptions for the new problems.
Predictive Analysis shows “what is likely to happen” by using previous data. By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly. So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.
Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
In addition, we could expand to Big Data Analytics, Cognitive Analytics, Enterprise Decision Management, Retail Analytics, Augmented Analytics, Web Analytics, Call Analytics, HR Analytics, Portfolio Analytics, Customer Journey Analytics.
Let's Know about the various tools, currently used to analyse data in upcoming blogs.