The Big surge in Data Analytics

Expounding Data Analytics:

In the age of a rapid technological development, data has become quite a valuable commodity. Managing large amounts of data and the ability of drawing conclusions from it is a particularly useful skill to have. This is where data analytics step forward. Data Analytics (DA) is primarily the process of examining and analyzing data sets to form conclusions from the information they contain. This involves the use of several specialized systems and software as it is too magnanimous task for a human being to undertake. Data analytics is often confused with another term of Business analytics. Business analytics involve analytics of data in order to improve business performances. On the other hand, Data analytics offers a wide scope of fields to refine and process data. Data analytics now find applications in many sectors like defense, scientific research and software development.

Types of data analytics:

At the basic level, data analytics can be classified as qualitative and quantitative analytics. Quantitative analytics generally involve viewing the data from a numerical perspective. The numbers, statistics and other numerical data are tracked via the proper channels and results are presented respectively. Quantitative analytics involve analytics of non-numerical data like text, images, videos etc. Analyzing a bit deeper, data analytics can be divided into four types:

1. Descriptive analytics:

It deals with describing the data in the datasets as a function of time. Descriptive analytics gives a general picture of the processes which takes place in the data considered.

1. Diagnostic analytics:

Diagnostic analytics are particularly helpful in troubleshooting. It can be used to identify error patterns and thus, facilitate better quality control.

1. Predictive analytics:

Predictive analytics are basically used to predict the uncertainties which may occur in a particular process from previously collected data. This type of analytics plays a major role in modern development process.

1. Prescriptive analytics:

Prescriptive analytics are used again in debugging and troubleshooting procedures. Prescriptive analytics can be used to suggest measures for preventing further errors by analyzing error patterns in previous data.

Higher level data analytics involve EDA (Exploratory Data Analysis) and CDA (Confirmatory Data Analysis). EDA aims to find patterns and relationships exist within the given data set. The CDA applies several statistical techniques to prove or disprove hypotheses regarding a given data set.

How companies use Data Analytics:

Many multinational companies and organizations have invested a lot in data analytics development. Companies like Netflix and YouTube are reaping the benefits of investing in Big Data analytics. Both these companies have a highly developed data acquisition and analytics systems. Thus, they form conclusions regarding the tastes of their customers and can offer better services to individual customers. And they perform this for the several millions of customers they have. Similar data analytics systems are set up in companies like Coca-Cola and PepsiCo to help to increase their revenue manifold. During an interview, a Coca-Cola representative remarked that, “Data collected from different users helps us to cater to their needs better. Thus, we adapt ourselves to varying consumer requirements”. The companies which are ready to embrace the power of Data analytics and foray in the world of Big Data Analytics will have a better chance.

Data analytics is utmost importance for any company. Many companies have already taken Data analytics into consideration to improve and change according to the changing environment. These companies require professionals who are well versed in data analytics techniques. Thus, knowledge of data analytics provides better opportunities for anyone aspiring to be a part these companies.