Data Analytics or Data Processing is the procedure of probing data sets to draw inferences about the information they comprise with the help of expert systems and software. Commercial industries widely use Data Analytics tools and techniques to make more informed business decisions.
Data Analytics or Data Processing is a detection of extracting meaning from the raw data with the help of specialized system. These systems help companies identify patterns.
Data Analytics system and software help businesses improve efficiency, generate revenues and enhance marketing campaigns. With the help of Data Analytics, companies can respond more efficiently to market trends.
Type of Data Analytics
- Exploratory Data Analytics (EDA) – The basic aim of EDA is to identify the patterns and relationships in data. EDA is basically known as the spy and detective work.
Data analytics can also be classified into Qualitative and Quantitative Data analytics.
- Quantitative Data Analytics – It involves numeric data with quantifiable values that can be easily measured.
- Qualitative Data Analytics – It focuses more on non-numeric items like understanding audios, videos and images.
Data analytics is most commonly used by business where large sample size data is involved such as Banks, Financial Institutions, E-commerce and M-Commerce companies.
Tools used in Data Analytics are as follows:
- Microsoft Excel – It is the basic yet very effective tool which can help in managing data analytics. Functions like sort and filter help you display records in a desired manner. Formatting, charts and tables can display data in more powerful way than a sheet full of numbers. Excel gives you the function of formulas which not only ease the business but also save huge amount of time.
- SPSS – SPSS Statistics is a combined group of products that aids to address the complete analytical process. Planning, data collection, analysis, reporting and deployment are the basic features of this tool. There are custom-made modules from which you can derive your business needs. It helps in outperforming players and makes improved decisions.
- SAS – SAS is a leader in advanced analytics tool. SAS helps in keeping the high-quality data at its core. It’s accuracy and perfect quality are its USP. This tool is extremely fast and delivers the information remarkably fast. This tool has the capability to suit all business needs.
Reason for customer churn and slow down processing can be easily accounted with SAS. It can provide expertise in strategic focus areas.
When the Data Analytics work begins, Data Scientist performs various functions like data profiling and data cleansing to make sure the used data is correct and authentic. It also helps in eliminating errors.
Big data and analytics have moved to the top to-do list of today’s corporate agenda. The idea is to create embedded analytics with the help of simple tools for the front-liners. The objective of Data analytic tools is to provide easy and user friendly interfaces.