What is Data Analytics and its Future Scope in 2023

The use of data analytics in the current era is considered very important. It is not surprising that many small and large companies now choose to use Indonesian analytic software to analyze their data.

Data analytics allows customers to reduce the possibility of fraud by irresponsible parties.

In addition, companies can make decisions more quickly in the business they run thanks to the right software to analyze data in the company.

What is Data Analytics?

Data analytics is a form of business intelligence. It means that organizations use this data to solve specific problems and challenges.

The process is related to finding patterns among data sets, then providing knowledge about something valuable and relevant to business improvement efforts.

Later, data analytics will help you evaluate past experiences and predict trends that may occur in the future. Instead of basing business and strategic decisions on guesswork, you can make data-driven decisions.

When you become interested in studying the field of data analytics, you might come across several other terms, such as business intelligence, data engineering, data science, or machine learning.

Why Data Analytics So Important for Business?

After understanding the meaning of data analytics, we will discuss the benefits of data analytics and how important data analytics is for companies.

Key Performance Indicator (KPI) is no longer a foreign term in the business world. The problem is, determining KPI takes work. With so much data spread across various organizational lines, getting the data you want will take a long time and a lot of effort, especially if the team needs more analytical skills.

Even though the data is available, it is useless if no tools and skills can interpret the data quickly. The process of collecting and analyzing data becomes much more difficult.

This is where the importance of knowledge of data analytics is. This knowledge can improve the results of business decisions and make collecting and analyzing data faster while simplifying information, so stakeholders more readily accept it.

To give a clearer picture, let’s use a case study on data analytics in companies.

For example, an airline company wants to prioritize customers and emerging market trends. This company has millions of big data that offer business intelligence. If left unchecked, the data stack will not result in any improvement.

Finally, the company is carrying out data-driven initiatives that can improve airline operations. As a result, they get various benefits, such as:

1. More competent aircraft maintenance : Big data helps airlines perform better aircraft maintenance, from analyzing fuel efficiency each trip to data for measuring wind speed and temperature.

2. Aviation becomes safer : With a lot of data about aviation incidents, as much as possible, regulators can improve safety by analyzing problems that may arise during flights so that they can immediately minimize the risks.

3. Improved service: Collecting customer data enables analysts to obtain helpful information to improve operations, efficiency, and services.

Data Analytics Stages

According to Investopedia, data analytics involves several different processes, including:

1. Define Your Goals

First, you need to define your goals as clearly as possible to get to the root of the problem. Take particular time to think about business problems that you have to solve.

From there, you’ll create a structured series of questions that need to be answered.

For example, your company wants to launch a product. Then you have to analyze what products are most suitable for production, where these products will be sold, and so on.

2. Collecting Data

After you identify the various questions that need to be answered, you can start collecting data and find out which data best suits your business. The data obtained can be either qualitative or quantitative.

Data collection can be done in various ways, such as by monitoring social media, surveys, website analysis, etc.

3. Data Cleaning

The data that you have collected is data in raw format. That is, the data needs to be organized and checked for errors. Data cleaning or data cleaning is required to make it easier to analyze.

Data cleaning also includes removing errors, duplicates, and outliers and deleting data that doesn’t suit your business.

4. Analyze Data

If the data set is clean and tidy, it’s time to do the analysis. There are several types of data analysis, such as descriptive, diagnostic, predictive, and prescriptive analysis. This step becomes the initial process to decide how the data will be followed up and what strategy will be developed

Methods of Data Analytics

There are four main analytics types: descriptive, diagnostic, predictive, and prescriptive.These four types of analytics are usually applied in stages, and no class is said to be better than the other.

Each type is related to one another. The following is a further explanation of the four types of data analytics.

1. Descriptive Analytics

Descriptive analytics is the simplest type of analytics and forms the foundation for other types of analytics. These analytics allow you to pull raw data and briefly describe what has happened.

Descriptive explanations are very suitable combined with data visualization to be conveyed to stakeholders. Because graphs, charts, and maps can show trends, dips, and spikes in an easy-to-understand way.

Simply put, descriptive analytics answers the question, “What happened?”

For example, imagine that you are analyzing your clothing business data and find that your products consistently experience a significant increase in sales. This analysis explains in which month the increase occurred. Let’s say the increase occurred in April, May, and June.

2. Diagnostic Analytics

Diagnostic analytics is examining data to understand why an event occurred and why it happened. This type of analytics typically uses search, data discovery, and mining techniques.

Diagnostic analytics is used to answer the question, “Why did this happen?” as well as helpful in finding the root cause of an organization.

Continuing the example above, after digging into demographic data, you find that the average age that is suitable for wearing these clothes ranges from 13 to 18 years. However, the fact is that customers who buy your clothes are aged 25 to 32 years. Customer survey analysis data revealed that they bought clothes for their children, nephews, and younger siblings.

3. Predictive Analytics

Predictive analytics is used to predict trends or events that may occur in the future and answer the question, “What might happen in the future?”

This analysis is carried out by analyzing historical data in balance with current industry trends.

For example, you get data that the clothing sales business surged in April, May, and June. This month is close to Eid al-Fitr. Thus, next year’s clothing sales will also increase in the months approaching Eid al-Fitr, in line with the increase in previous years.

4. Prescriptive Analytics

Perspective analytics considers all possible factors in a business scenario and suggests things that must be followed up. This type of analytics is especially useful in making data-driven decisions.

In short, perspective analytics answers the question, “What should we do next?”

Finishing the clothing business example, the data analyst team should consider what steps to take with this trend of increasing seasonality. Can I run A/B testing with two ads focused on teens and parents? Or should the company improve its marketing strategy on other religious holidays?

Later, the data obtained from the decisions can be applied in future steps.

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