Data Analyst Course in Delhi - GK Institute

 Data Analytics: What It Is, How It's Used, and 4 Basic Techniques

What Is Data Analytics?

The term data analytics refers to the science of analyzing raw data to make conclusions about information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics can be used by different entities, such as businesses, to optimize their performance and maximize their profits. This is done by using software and other tools to gather and analyze raw data.

Key Takeaways

Data analytics is the science of analyzing raw data to make conclusions about that information.

Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions.

The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. 

Various approaches to data analytics include descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Data analytics relies on a variety of software tools including spreadsheets, data visualization, reporting tools, data mining programs, and open-source languages.

Understanding Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business.

Steps in Data Analysis

The process involved in data analysis involves several steps:

Determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.

Collect the data. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.

Organize the data after it's collected so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.

Clean up the data before it is analyzed. This is done by scrubbing it and ensuring there's no duplication or error and that it is not incomplete. This step helps correct any errors before the data goes on to a data analyst to be analyzed.

Types of Data Analytics

Data analytics is broken down into four basic types:

Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?

Diagnostic analytics: This focuses more on why something happened. It involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?

Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?

Prescriptive analytics: This suggests a course of action. For example, we should add an evening shift to the brewery and rent an additional tank to increase output if the likelihood of a hot summer is measured as an average of these five weather models and the average is above 58%,

Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. It's nearly impossible to optimize something if you aren’t properly measuring it, whether it's your weight or the number of defects per million in a production line.

The sectors that have adopted the use of data analytics include the travel and hospitality industry where turnarounds can be quick. This industry can collect customer data and figure out where problems, if any, lie and how to fix them.

Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information that retailers collect and analyze can help them identify trends, recommend products, and increase profits.