Data Analytics
Hello Everyone,
Data.Data.Data…..!!!!!
Wherever you go it may be in banks, schools, hotels, colleges,airports, etc. you deal with some information. So from where are you getting these information? Yes! What you are thinking is right,it is ‘data’. Because data can be found everywhere. This information is coming from data. In day-to-day life, each and everyone deals with data.
The amount of data is growing exponentially. According to the latest estimates, 328.77 million terabytes of data is produced everyday. You may be wondering, with this much amount of data how companies make use of these data to help its business. This is where analytics come into play. A company uses analytics to make better business decisions.
What is Data Analytics?
Data Analytics is the process of analyzing raw data to draw insightful conclusions about information. It helps businesses to optimize its performance, increase performance efficiency and maximize profit.
Understanding Analytics
It is the systematic computational analysis of data. Organizations use analytics in business to describe, predict, and improve business performance. You often come across these two terms ‘Analytics’ and ‘Analysis’. There is a slight difference between these two terms. Let’s understand the difference between analytics Vs analysis.
Data Analysis is a subset of data analytics. It focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation and deployment. It takes multiple analysis processes to focus on why an event happened and based on the previous data focuses on what may happen in the future whereas
The field of Data Analytics covers multiple disciplines. Data is used to extract meaningful information through analytics, which includes significant usage of computer expertise, mathematics, statistics, the use of descriptive approaches and models for predicting and forecasting. There is a phrase called ‘Advanced Analytics’ which is growing in popularity nowdays used to refer to the technical components of analytics, particularly in the newer areas like the implementation of machine learning methods like neural networks, decision trees, logistic regression,linear to multiple regression analysis, and classification to perform predictive modeling. Furthermore, unsupervised learning methods like clustering, PCA, association analysis are also included.
There are several kinds of data analytics. Let’s explore them one by one. It is broken down into four basic types:
- Descriptive Analytics: This describes what has happened over a given period of time. Ex: Are sales stronger this month than last?
- Diagnostic Analytics: This focuses more on why something happened. It includes a bit of hypothesizing. Ex: How did the latest marketing campaign impact sales?
- Predictive Analytics: This focuses on what is likely going to happen in the near term. Ex: Predicting buying behaviour in retail
- Prescriptive Analytics: It focuses on finding the ideal way forward or action necessary for a particular scenario based on data. Ex: Evaluate whether a local fire department should require residents to evacuate a particular place when a wildfire is burning nearby.
Why is Data Analytics important?
Applying data analytics into the business model means companies can help reduce costs by identifying more efficient ways of doing business.
Who is using Data Analytics?
Data Analytics has been used by several industries such as the travel and hospitality industry, healthcare, retail industry, finance services and banking sectors etc.,
Conclusion:
The future of data analytics looks bright with emerging technologies such as Artificial Intelligence, Machine Learning and Blockchain. These technologies have the potential to transform the way we analyze data, leading to new insights and discoveries that were previously impossible.