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What are 4 types of data analytics

Data analytics is a discipline that involves analyzing data to obtain information that can help solve problems in various fields. It uses multiple disciplines such as computer programming, mathematics, and statistics to provide accurate analysis.

The purpose of data analysis may be to explain, predict, or improve organizational performance. This is done through data visualization, data mining, data transformation, etc. to identify, predict and solve current and future problems. They achieve this by using data management techniques.

These goals distinguish data analytics from science, which is similar to business analytics and data science. Business analytics is a method of analyzing data used solely by businesses. Data science and analytics solve problems through deep learning and strategic control. Here we described 4 types of data analytics.

4 Basics of Data Analysis

Descriptive Analysis

Descriptive analysis is the simplest type of analysis and the foundation on which other types are built. This allows you to extract key information trends and clearly explain what happened or is happening now.

Descriptive analysis "What happened?" answers the question.

Imagine analyzing your company's data and discovering an increase in sales of one of your products: video games. A descriptive analysis here might tell you: “Sales of these video games increase every year in October, November, and December.”

Data visualization is a common way to communicate descriptive analysis because tables, graphs, and maps can show data trends as well as ups and downs in a clear, easy-to-understand format.

This type of analysis uses simple numerical and statistical tools, such as spreadsheets, rather than complex calculations to create visual elements such as graphs or line charts to describe the data set. Descriptive analytics is used by many companies and is key to daily reporting, especially through dashboards.

 4 Types of Data Analytics
4 Types of Data Analytics

Diagnostic Analysis

Having asked the main question: 'What happened', the next step is to dig deeper and ask: Why did it happen? This is where analysis comes into play.

Descriptive analytics takes the information obtained from descriptive analytics and then trains it to identify the reasons for these results. Organizations use this type of analysis because it establishes greater connections between data and reveals patterns of behavior.

An important aspect of disease analysis is generating detailed information. When new problems arise, you may have already collected some information about the problem. If you have the knowledge you already have, you will have to redo the work and tie all the threads together.

Business applications of analytics include:

  • A shipping company is investigating the reason for shipping to a particular location.

  • A SaaS company takes a deep dive into identifying marketing campaigns that drive testing.

Continuing the example above, you might research a video game using user demographics and discover that these people are between the ages of eight and eighteen. However, customers are generally between 35 and 55 years old. Analysis of customer survey data shows that one thing is central to customers' video game purchases. The increase in sales during the fall and winter season can be attributed to the holidays when gift giving is also part of the season.

Diagnostic analysis is useful for finding the root cause of an organizational problem

Predictive  Analysis

As the organization becomes more analytical and begins to analyze events, the focus shifts from understanding historical events to generating insight into current or future conditions. Predictive analytics is based on the combination of classical statistical analysis and modern artificial intelligence (AI).

Using analytics, organizations identify possible outcomes that can guide them to the best course of action. Predictive analytics is used in various  industries, such as the aviation industry to determine the impact of fuel efficiency measures and the manufacturing industry to plan future demand and replenish inventory. Simple forecasting models can be created using tools such as spreadsheets or spreadsheets. When you analyze historical data of an industry, you can make better predictions about the future of your business.

For example, knowing that video game console sales have increased in October, November, and December each year for the past decade gives you enough information to predict that the same trend will occur next year. This is a reasonable estimate, given the increasing popularity of video games in general. Planning for the Future can help your family develop strategies for what might happen.

Descriptive Analysis

Descriptive analysis is a powerful type of analysis. It combines internal data, external sources and machine learning methods  to make better recommendations for business decisions. The decision-making process in text analysis is used both descriptively and predictively. This leads to an investigation of current conditions and decisions that are likely to have the most impact in the future. This process is complex and time-consuming, but if done correctly it can add great value to the family.

In addition to defining and planning each decision, scientists also develop written procedures to prevent errors in order to guarantee all possible outcomes. After implementing the system, they should test these models regularly to ensure they provide realistic recommendations.

In short, when we move from descriptive analysis to written analysis, each model adds value to the organization. But at the same time the analysis becomes more problematic.

Artificial intelligence (AI) is a great example of analytics. AI systems use large amounts of data to constantly learn and use this information to make informed decisions. A well-designed AI system can inform and implement these decisions. Business activities can be carried out and developed every day, without anyone needing to have any technical knowledge.

Currently, many mobile and information companies (Apple, Facebook, Netflix, etc.) are using text analytics and artificial intelligence to improve decision making. For other organizations, the shift to forecasting and analysis may not be over yet. As technology continues to evolve and more professionals become trained in data, we will see more companies enter the information-driven industry.

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