Data analysis is a vital part of any enterprise’s operations in today’s data-driven environment. Data analytics is the study of data and how to extract insights from it. It includes tools, techniques, processes, and techniques that assist in data management and analysis. This includes the collection, storage, organisation, and organization of data. Data analytics’ primary purpose is to apply statistical analysis and use different technologies on existing data in order to find patterns and solve problems. Data analytics is essential to the creation of business strategies and daily operations.
Data analytics is a combination of skills and functionalities in a variety of disciplines, including mathematics and computer programming. These skills are used by data analysts to perform analyses that help improve business results, predict and predict future events, and other tasks. A variety of data management techniques, including data cleansing and data mining, data modeling, data transformation, and data modelling, can guarantee strong results.
Now that you are familiar with data analytics, let’s take a look at the different types.
Types of data analytics:
There are four main types data analytics. Each type of data analytics has a different purpose and occurs at different times.
1. Analytics with descriptive data:
Descriptive analytics answers questions about the specific events. This technique reduces large amounts of data and summarizes them to provide a clear explanation to stakeholders. These strategies create KPIs (key performance indicator) that can track successes and failures. Industry-specific metrics like ROI (return on investment) are used. Some industries have their own metrics that track performance and outcomes. Analysts must collect relevant data, analyze it and visualize the data. This gives you a deeper understanding of past and current performance.
2. Diagnostic Analytics:
This type of analytics aims to identify the reasons why events occur. This technique is an additional to descriptive analytics. This technique takes the insights and lessons from descriptive analytics and goes one step further to find the cause. Further analysis of the KPIs is done to determine if there are any reasons for an improvement or decrease in quality. This is done in three steps.
Identifying patterns and abnormal behaviour in data. These could be unexpected changes in markets or metrics.
Collecting data about this unusual behaviour.
This behaviour change can be explained by statistical techniques.
3. Predictive Analytics
This type of analytics can help predict what might happen in the future. Predictive analytics uses historical data in order to identify trends and predict if they will continue to occur. Predictive analytics gives deep insight into the future of markets, products, and customer behaviour patterns. Tec