Introduction
Data analytics (DA) is the process of slicing and dicing large data sets to analyze the data and infer conclusions from it. DA is the crux of Business Intelligence (BI). BI uses DA to formulate business decisions and achieve the business goals. DA is in turn dependent on Data Science. Data Science deals with the scientific methods and methodologies to extract meaningful conclusions from a data set.
How is DA done?
The first stage of the DA is the data collection. You need to identify the data which is required or to be extracted. The raw data can be in any format and from any sources. Data is pulled or filtered out from huge data sets or stream of data without disturbing the entire system of data. Then it needs to be transformed into an understandable format. Data has to be refined and analysed. Finally it is segregated into various groups/sections and then integrated as per the requirements.
Big data analytics involves data mining, machine learning, and predictive analysis. For a better understanding of the data, it should be viewed in different ways. DA correlates the data in different ways and identifies the patterns of recurrence if any or provides an insight into the unknown or unobserved information. The DA can be quantitative as well as qualitative, the ultimate goal being the business performance. The quantitative DA usually deals with the numerical data analysis. Qualitative DA deals with non-numeric data like text, images, audio, and video, including common phrases, themes etc.
Data or data sets can be visualized in different ways using DA. You can visualize data as charts, pie diagrams, histograms, or any other graphical formats. Also, you can generate reports based on different criteria of your requirement.
Highly sophisticated and automated algorithms are developed by the engineers to extract, refine, analyze, and visualize the data. Specialized software solutions are used to do the DA. Using DA you can even predict the possible outputs. DA is used as an advanced analytics technique used to achieve BI. DA is one of the basic strategy followed and considered while developing a BI tool.
Areas of DA Implementation
In a data-driven environment like IT industry, the application of DA is increasing day-by-day.
DA is applicable in the areas of scientific researches, theories, hypothesis etc. Whether it is a historical data or marketing trends or streaming data or any other kind of big data, DA plays a crucial role as reliable data interpretation is achieved. Competition being high in the current world scenario, DA is a must in each and every field in which you deal with large data sets.
DA enables any large organization to get an overall idea of the current scenario in any industry in various ways. The terms DA, business analytics, BI, analytical processing, data science etc are used in the industry where they work with large data set. All these terms are interconnected in some or other way as they are dependent on each other and basically works for the enhancement of business performance.
Scope of DA
The huge demands and rising competitions in the IT industry paved the way for the BI tools. Conventional data analysis takes a large amount of time and it is very difficult to draw conclusions in a specific format.
Real-time big data analysis is a tedious task using conventional methods. Conceptual data can be derived from large data sets using DA which is widely used for BI. There are various applications that are used to achieve the DA. DA can be used for small data sets also. However, the major use is in high-level industries like stock exchanges, research centers, marketing etc. Applications are designed and developed in such a way as to satisfy the specific needs of a particular industry. Commercial industries gain a large revenue using the DA and they use DA widely to increase their performance.
DA helps to create the BI tools matching with the trends in the industry. BI tools boost the revenue of the organization. Hence DA can optimize the manual efforts and improve the overall efficiency of the business system.