Data analytics is the science of analyzing raw data to make conclusions about that 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 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.
Understanding Data Analytics
Data analysis is a broad term that covers many different types of data analysis. Any type of information can use data analysis techniques to gain insights that can be used to improve the situation.
For example, manufacturing companies usually record the running time, downtime, and work queues of various machines, and then analyze the data to better plan the workload, so that the machine runs closer to the peak capacity.
Data analysis can do more than point out bottlenecks in production. Game companies use data analysis to set reward schedules for players so that most players are 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.
The process involved in data analysis involves several different steps:
The first step is to determine the data requirements or how to group the data. Data can be separated by age, demographics, income, or gender. Data values can be numeric or divided into categories.
The second step of data analysis is the process of collecting data. This can be done through various sources (such as computers, online sources, cameras, environmental sources, or people).
Once the data is collected, it must be organized so that it can be analyzed. The organization can be done on spreadsheets or other forms of software that can obtain statistical data.
Then clean up the data before analysis. This means that it will be cleaned up and checked to ensure that there are no duplications or errors and that it is not incomplete. This step helps correct any errors before continuing to analyze the data analyst.
Why Data Analytics Matters
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 ways to conduct business and storing large amounts of data.
Companies can also use data analysis to make better business decisions and help analyze customer trends and satisfaction, so that they can develop better new products and services.
Who’s Using Data Analytics?
Some industries that have adopted data analysis include travel and hospitality, and their turnover is fast. 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.