What are Data Analytics?

Data analytics is the technique of analysing raw data in order to make conclusions about that information. Data analytics technologies and techniques are mainly used in business enterprises to enable organizations to make more informed business decisions.

Data is extracted from multiple sources and from different organization department which need to be cleaned and categorized to analyse different behavioural patterns.

In this article we will discuss more about Data Analytics Types and discuss why Data analytics is so important in Organization.

Why is Data Analytics important?

As the organization has many sources of data, but only the sophisticated data is used to get accurate information in the business venture. Data Analytics has a key role in improving your business. Below are the few points:

Improving Efficiency:

The data collected by the business are not only related to the individuals external to the organization. Most of the data collected from the businesses are analysed internally. By applying technology and technique it become very convenient to collect data that helps to employees and business.

Perform Market Analysis:

Market analysis is done to understand the strength and weakness of the competitor. In this competitive world everyone is concentrating on moving one step forward from their competitors. They are ready to know which technology and techniques their competitors use. Therefore, market analysis is a major tool for tracking all these activities.

Cost Reduction:

Now a lot of progress is going on in technology. Many companies use this tool to analyze full workflow. Not only do you save money in terms of infrastructure, but you also save on the cost of developing a product, which will be a complete market fit.

Quick and Better Decision-Making:

Analysis of data allows quick correction business for customer requirements and experience. It also allows to make better decisions in the event.

Different types of Data Analytics

There are basic 4 types of data analytics. Let’s discuss with each other one by one and proceed with more sophisticated.

Descriptive Analytics:

Descriptive analysis is an early stage of data processing. At this stage, all historical and useful information is collected for further analysis. Descriptive analysis describes the past using a series of data to compare. Most commonly reported financial metrics are descriptive analytics, such as year-to-year pricing changes, month-to-month sales growth, number of users or a product of total revenue per customer.

Descriptive helps describe and present the data in a format that can easily be understood by different types of business readers. A common example of descriptive analytics is the company’s reports that provide a historical review of the organization’s operations, sales, financials, customers and stakeholders.

Diagnostic Analytics:

Diagnostic Analytics is a form of advanced analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining and correlations.

In this phase, the company can get in-depth information about a particular issue. At the same time, a company should have detailed information at their disposal or otherwise the data collection may vary for each issue and time consuming.

Predictive Analytics:

Predictive analytics is the branch of advanced analytics, which is used to make predictions about the events of the unknown future. Predictive analysis uses both new and historical data to predict trend, behavior and activity.

Why predictive analytics is important from others:

• Detecting fraud
• Optimizing marketing campaigns
• Improving operations
• Reducing risk

Prescriptive Analytics:

The primary purpose of the Prescriptive analytics is to determine what action to take to eliminate the future problem. Prescriptive Analytics is the application of logic and mathematics for data to specify a preferred course. Although all types of analytics ultimately support better decision-making, but the Prescriptive analytics report gives output of judgment rather than estimates of statistic, probability or future results.

Epilogue

The importance of data analytics is actually changing the world and its used to develop artificial intelligence, track diseases, understand consumer behavior. This is a new era of data and has unlimited capability.