Data Analytics

A set of tools and technologies that help manage qualitative and quantitative data with the object of enabling discovery, simplifying organization, supporting governance, and generating insights for a business.


Benefits of Data Analytics in Business

These days, most businesses use data analytics to examine present situations and predict future scenarios. The results of these actions can bring many benefits and advantages. These benefits include:

Better Decision-Making

Rather than relying on intuition alone, companies are better equipped to make accurate predictions.

More Accessible Analytics

With self-service, intelligent tools, organizations can gain complete visibility into their operations across all departments.


Businesses can automate 69% of time spent on data processing, which stands to increase business effectiveness while reducing costs.

Predictive Modeling

Predictive modeling allows organizations to understand the root causes behind problems and predict future outcomes.

Types of Data Analytics

There are six types of data analytics. One can employ them in making a complete data analysis depending on the problem. The kinds of insights you get from your data depend on the type of analysis you perform.

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The successful businesses

are those that constantly learn and adapt.

1. Descriptive data analytics

This type of data analytics examines past data to explain what had happened. Companies use statistical analysis techniques to perform descriptive data analysis. It helps them compare past results, identify anomalies, distinguish strengths and weaknesses, etc.

2. Diagnostic data analytics

Diagnostic data analytics examines past data to explain the cause of an anomaly. This type of analytics aims to answer “why did this happen?” from a descriptive analytics result. It helps companies draft accurate solutions to problems instead of relying on guesswork.

3. Predictive data analytics

Predictive data analytics involves using current or historical data to predict future actions. These help them to quickly locate patterns and predict risks and opportunities in the future.

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Big Data analytics

combined with statistical algorithms and historical data gives marketers the ability to predict consumer behaviors and outcomes more accurately.

4. Prescriptive data analytics

Prescriptive data analytics involves selecting the best solution for a problem from available options. It examines results from other analytics and gives guidance on how to reach a specific answer. Companies can use it to automate decision-making and hasten complex approvals.

5. Real-time data analytics

Real-time data analytics involves using data immediately when entered into the database. Companies use it to identify trends and benchmarks faster than their competitors. They can also track and analyze their competitor’s operations instantaneously.

6. Augmented data analytics

Uses machine language (ML) and natural language processing (NLP) to analyze data. It helps automate the tedious task of code-based data exploration and makes it available to business users. This reduces the chances of errors and enables the data analyst more time to do other actionable tasks.