What is Data Analytics? Definition, Stage, and Examples

Data analytics is a critical component in a company that extracts information from all aspects of business operations. 

The results of data processing are not only used to make sure that the data is correct but they are also used to help the company solve many questions and issues it faces.

Companies that use analytical data can improve operations, reduce risks, and make more focused and strategic decisions.

Let's learn more about what is data analytics, its functions, types, stages, tools, and examples of data science applications in the following article!

What Is Data Analytics?

According to Harvard Business School, data analytics is the process of examining data to answer questions, identify trends, and discover information.

Analytical data is a type of business intelligence that organizations use to solve specific problems. 

The process entails identifying patterns of big data and generating useful and relevant information to improve business performance.

You can use analytical data to evaluate previous experiences and predict trends in the future. 

As a result, you can make data-driven business decisions and strategies rather than relying on intuition.
 

Functions of Data Analysis

The main goal of data analysis is to help businesses make decisions based on sets of data. Discover more in the following:

1. Makes Statistical Data Processing Easier

The process of processing statistical data becomes simpler and more efficient, reducing your workload in dealing with complex statistical data.

2. More Accurate Calculations

Analytical data also lets you handle complicated statistical data more precisely and lowers the chance of making mistakes when calculating.

3. Time Efficiency

You can save time in your business by processing data automatically and more quickly. This approach allows your company to process information more quickly and efficiently than traditional methods.

4. Business Solutions

The data that is gathered and analyzed serves as the basis for overcoming business challenges. The data-driven conclusions can assist you in making better decisions for the future growth of your business.

5. Market Research

Companies can create effective marketing strategies by gathering and analyzing relevant market data. This statement helps businesses to understand the current market's needs and trends.
 

Data Analytics Types

Analytical data can be divided into several types. These are data analytics types that you can use:

1. Descriptive Analytics

Description analytics is one of the simplest and most basic data analytics types. This kind of analytics seeks to identify trends from raw data by describing what has occurred or is happening.

For example, assume you are analyzing company data and discover an increase in game console sales.

In this case, descriptive analytics can reveal whether your product's sales increased in October, November, and early December each year.

Data visualization is an excellent feature for conveying descriptive analysis. It uses graphs, diagrams, and maps to clearly and easily show trends in data rise and fall.

To put it simply, descriptive analytics is all about finding out "What happened?"

2. Diagnostic Analytics

In this type of analysis, you will go a step further by comparing trends to identify correlations between variables and causal relationships.

For example, you can find demographic data for console game users aged 8 to 18. Customers are typically between the ages of 35 and 55.

According to this case study of customer data, one of the main reasons people buy game consoles is to give them to their kids.

In simple terms, diagnostic analytics helps answer the question, "Why is this happening?"

3. Predictive Analytics

Predictive analytics is the process of predicting future trends or events. By analyzing historical data on industry trends, you can make accurate predictions about what the future holds for your business.

For example, you are aware that console game sales have increased in October, November, and early December of each year over the last decade. 

This substantial amount of data leads to predict that the same trend will likely continue next year.

Using predictive analytics, you can identify opportunities and risks and take the necessary steps to improve your company's performance in the future.

Basically, predictive analytics provides an answer to the query, "What might happen in the future?”

4. Prescriptive Analytics

This type of analytics is extremely useful for making data-driven decisions. Prescriptive analytics considers all factors in a scenario as suggestions that can be put forward.

For example, if console game sales increase during the holiday season, prescriptive analytics can suggest more effective advertising strategies.

This type of analysis is typically performed using A/B testing with different ad targets, such as ad one targeting end users of the product (children) and ad two targeting customers (their parents).

The trial's data can help identify how to capitalize on fluctuations in the seasons and their causes.

Prescriptive analytics answers the fundamental question, "What actions should we take next?”

Stages of Data Analysis

The stages of analytical data include steps that require technical knowledge and an understanding of the problem being addressed. Here's a full explanation of the stages:

1. Determining Data Needs

Analytical data begins with ensuring that everyone understands what data they require. 

You must identify the specific business objectives that will lead to the resolution of the problem at hand. 

Understanding what you need to achieve in your business will take time and careful consideration.

2. Collecting Data

There are several ways to collect data. One approach is to keep an eye on customer sentiment and trends on social media. 

Another is to run surveys to gather direct feedback from your target audience. Finally, you can analyze website data to gain insight into online user behavior.

3. Managing Data

Spreadsheets and other statistical data management software are useful tools for this task. 

To facilitate the analysis process, ensure that all collected information is organized, clear, and structured.

4. Data Cleaning

Data cleaning needs to be done as well to make the data easier to analyze. Data cleaning entails a variety of tasks, including removing data errors, duplicate data, outliers, and irrelevant data.

5. Analyzing Data

The final step is to perform data analysis. As previously pointed out, data analysis can be divided into four types: descriptive, diagnostic, predictive, and prescriptive. 

Accurate data analysis can provide information to help determine the strategy to be developed.

Data Analysis Tools

Do you know that several tools can make the process of analyzing data easier? Here are the most commonly used data analytics tools:
 

• SAS.
• Microsoft Excel. 
• Power BI.
• Google Charts.
• Data Wrapper.
• Infogram.
• Tableau.
• Zoho Analytics.
   

Data Analytics Examples

Many different kinds of businesses and fields use data analytics in different ways. Let's look at some data analytics examples:
 

• Expedition services: Use analytical data to boost operational efficiency, lower costs, and increase customer satisfaction.
• Finance: Helps to reduce the risk of fraud, which is frequently overlooked by human observation.
• Transportation Service Provide: Estimate the number of passengers to determine the aircraft's flight route.
• Planning Cities: Involves analyzing traffic patterns, forecasting mass movements, and assessing alternative location and toll road designs.

This concludes the full explanation of data analytics. For businesses in many sectors, it is very useful because it can turn data into information that can be used

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