What is Data Analytics?

Every day we generate gigabytes of data, which is growing exponentially. Data and analytics are everywhere, and it is getting increasingly important to understand them. This blog post will give you an overview of data analytics.

What is Data Analytics?

Data analytics is a process that reveals insights from raw data. By collecting and analyzing data, organizations can better serve customers, improve operations, increase profitability, and optimize internal operations. 

The goal of any business is to make more money or add value to its customers within a decided workstream. Data analytics gives companies the ability to do both at once by providing them with actionable data to make strategic decisions about their products, services, and marketing strategies, among other processes.

Types Of Data Analytics

Each type of analytics can give you different insights, and they fit varied bespoke business use cases. So, it's essential to choose the right analytics for your business needs:

Descriptive Analytics:

Descriptive analytics helps you determine the process of what has happened to date. When using descriptive analytics, you look at the specific patterns within the data to help you make decisions.

Diagnostic Analytics:

Diagnostic analytics  provides insights for identifying the root cause of a problem. This type of analytics helps a company implement a solution to resolve the issue and take necessary preventive measures to avoid future issues.

Predictive Analytics:

Predictive analytics is the process of collecting, analyzing, and applying information from past events to make predictions about future or current events. It uses statistical analysis and machine-learning techniques to study various data sets, including behavior patterns and trends. 

Prescriptive Analytics:

Prescriptive analytics is the next step up from Predictive analytics. It analyzes the data and gives you instant suggestions/recommendations. Google’s self-driving car is the best example of prescriptive analysis. Prescriptive analytics uses machine learning and algorithms to determine which data inputs will lead to outputs. It also incorporates human intelligence, along with simulations and customer feedback.

Why Is Data Analytics Important?

According to Gartner, data and analytics are essential for all businesses. Today's enterprises use data analytics to align their strategies better with the market demands and reduce costs by finding more efficient ways to do business.

Data analytics gives a clear picture of what techniques are working and how effective they are. 

Businesses also use data analytics to make better business decisions and analyze trends and customer satisfaction, leading to new and better products and services. Specifically, 55% of companies use data to improve efficiency and predict changes and outcomes.47% use it to improve customer interactions with their customers.

Summing Up

Data analytics is an umbrella term for various techniques to get insights from data. By collecting data, gaining insights from it, and using those insights to make decisions, you can improve your business in multiple ways.

To read more articles like this, visit Cogent Infotech.