Data analytics – what is it and how it is used?

What Is Data Analytics?

Data analytics is the science of analyzing raw data to draw conclusions about that information. Many data analysis techniques and processes have been automated into mechanical processes and algorithms for processing raw data for human consumption.

Key points

• Data analysis is the science of analyzing raw data to draw conclusions about that information

• The technology and processes of data analysis have been automated into mechanical processes and algorithms for processing raw data for human consumption

• Data analysis helps companies optimize their performance

More detailed explanation

Data analysis is a broad term that encompasses many different types of data analysis. Any type of information can gain insights that can be used to improve things through data analysis techniques. Data analysis techniques can reveal trends and indicators, otherwise these trends and indicators will be lost in a lot of information. This information can then be used to optimize processes to improve the overall efficiency of the business or system.

For example, a manufacturing company usually records the running time, downtime, and work queue of various machines, and then analyzes the data to better plan the workload and make the machine run closer to the peak capacity.

Data analysis can do more than point out bottlenecks in production. Game companies use data analysis to set up reward programs for players to keep most players active in the game. Content companies use many of the same data analytics to let you click, watch, or reorganize content to get another view or another click.

Data analysis is important because it can help companies optimize performance. Implementing it into a business model means that companies can help reduce costs by identifying more efficient business methods and storing large amounts of data. Companies can also use data analysis to make better business decisions and help analyze customer trends and satisfaction, which can lead to new-better-products and services.

Data Analysis Steps

The process involved in data analysis includes several different steps:

1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values ​​can be numbers or divided by categories.

2. The second step of data analysis is the process of collecting it. This can be done through various sources, such as computers, online sources, cameras, environmental sources, or through people.

3. Once the data is collected, it must be organized for analysis. This may happen to spreadsheets or other forms of software that can obtain statistical data.

4. Then clean up the data before analysis. This means that it will be cleaned and checked to ensure that there are no duplications or errors, and that it is not incomplete. This step helps correct any errors before handing them over to a data analyst for analysis.

Basic types

Data analytics is divided into four basic types:

1. Descriptive analysis: This describes what happened in a given time period. Has the number of views increased? Are sales this month stronger than last month?

2. Diagnostic analysis: This is more focused on why something happened. This involves more diverse data input and some assumptions. Does the weather affect beer sales? Has the latest marketing campaign affected sales?

3. Predictive analysis: This will turn to what may happen in the near future. What was the sales situation last time we had a hot summer? How many weather models predict that this year will be a hot summer?

4. Normative analysis: This indicates a course of action. If based on the average of these five weather models, the possibility of summer heat is above 58%, we should add a night shift to the brewery and rent additional water tanks to increase production.

Data analysis is the foundation of many quality control systems in the financial world, including the popular Six Sigma program. If you don’t measure something correctly—whether it’s your weight or the number of defects per million in your production line—it’s almost impossible to optimize it.

Some industries that use data analysis include tourism and hospitality, which have fast turnover. The industry can collect customer data and find out where the problems (if any) are and how to solve them.

Healthcare combines the use of large amounts of structured and unstructured data, and uses data analysis to make quick decisions. Similarly, the retail industry uses large amounts of data to meet the changing needs of shoppers. The information collected and analyzed by retailers can help them identify trends, recommend products and increase profits.

Data analytics FAQ

Why is data analytics important?

Data analytics is important because it can help companies optimize performance. Implementing it into a business model means that companies can help reduce costs by identifying more effective ways of operating. Companies can also use data analysis to make better business decisions and help analyze customer trends and satisfaction, which can lead to new-better-products and services.

What are the 4 types of data analysis?

Data analysis is divided into four basic types. Descriptive analysis describes what happened in a given period of time. Diagnostic analysis focuses more on the reasons why certain things happen. Predictive analysis turns to what may happen in the near future. Finally, the normative analysis recommends action.

Who Is Using data analytics?

Data analysis has been adopted by many industries, such as tourism and hospitality, where the turnover rate of these industries is very fast. The industry can collect customer data and find out where the problems (if any) are and how to solve them. Healthcare is another industry that uses a combination of large amounts of structured and unstructured data, and data analysis helps make decisions quickly. Similarly, the retail industry uses large amounts of data to meet the changing needs of shoppers.

What analytics tools can be used for the business?

EPAM provides analytics tools for different purposes, here are listed some of them:

• InfoNgen is an AI-based text analytics software. It leverages the power of NLP and machine learning to help small, medium, and large businesses search, collect and analyze updates critical to staying competitive and driving effective marketing decisions.

• iSwarm is an online consumer & patient engagement and activation solution for enterprises seeking to increase the effectiveness of their marketing campaigns.

• MarketMaker is an institutional-grade trading system used for automated market making, hedging and arbitrage of digital assets

• Real-Time Math is used for numerical calculations and time-series data analysis