Data Analytics

What is Data Analytics?

Data analytics is the science of analyzing raw datasets in order to derive a conclusion regarding the information they hold. It enables us to discover patterns in the raw data and draw valuable information from them.

Data analytics processes and techniques may use applications incorporating machine learning algorithms, simulation, and automated systems. The systems and algorithms work on the unstructured data for human use.

These findings are interpreted and used to help organizations understand their clients better, analyze their promotional campaigns, customize content, create content strategies, and develop products. Data analytics help organizations to maximize market efficiency and improve their earnings.

Process of Data Analytics

Below are the common steps involved in the data analytics method:

Step 1: Determine the criteria for grouping the data

Data can be divided by a range of different criteria such as age, population, income, or sex. The values of the data can be numerical or categorical data.

Step 2: Collecting the data

Data can be collected through several sources, including online sources, computers, personnel, and sources from the community.

Step 3: Organizing the data

The data must be organized after it is collected so that it can be examined. Data organization can take place on a spreadsheet or other type of software that is capable of taking statistical data.

Step 4: Cleaning the data

The data is first cleaned up to ensure that there is no overlap or mistake. Then, it is reviewed to make sure that it is not incomplete. Cleaning the data helps to fix or eliminate any mistakes before the data goes to a data analyst for analysis.

Data Analytics Types

The following are the four fundamental types of data analytics:

4. Descriptive Analytics describes the happenings over time, such as whether the number of views increased or decreased and whether the current month’s sales are better than the last one.
5. Diagnostic Analytics focuses on the reason for the occurrence of any event. It requires hypothesizing and involves a much diverse dataset. It examines data to answer questions, such as “Did the weather impact the selling of beer?” or “Did the new ad strategy affect sales?”
6. Predictive Analytics focuses on the events that are expected to occur in the immediate future. Predictive analytics tries to find answers to questions like, what happened to the sales in the last hot summer season? How many weather forecasts expect this year’s hot summer?
7. Prescriptive Analytics indicates a plan of action. If the chance of a hot summer calculated as the average of the five weather models is above 58%, an evening shift can be added to the brewery, and an additional tank can be rented to maximize the production.

Benefits of Data Analytics

1. Decision making improves

Companies may use the information they obtain from data analytics to guide their decisions, leading to improved results. Data analytics removes a lot of guesswork from preparing marketing plans, deciding what material to make, creating goods, and more. With advanced data analytics technologies, new data can be constantly gathered and analyzed to enhance your understanding of changing circumstances.

2. Marketing becomes more effective

When businesses understand their customers better, they will be able to sell to them more efficiently. Data analytics also gives businesses invaluable insights into how their marketing campaigns work so that they can fine-tune them for better results.

3. Customer service improves

Data analytics provides businesses with deeper insight into their clients, helping them to customize customer experience to their needs, offer more customization, and create better relationships with them.

4. The efficiency of operations increases

Data analytics will help businesses streamline their operations, save resources, and improve the bottom line. When businesses obtain a better idea of what the audience needs, they spend less time producing advertisements that do not meet the desires of the audience.

Related Readings

To keep learning and developing your knowledge base, please explore the additional relevant resources below:

• Big Data in Finance
• Python Data Structures
• Tableau – Uses in Finance
• Transitioning from Excel to Python
• See all data science resources