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Data Analytics

Reviewed by Sujaini | Updated on Oct 05, 2020


What is Data Analytics?

Data analytics is the science of analysing raw data to draw conclusions on that information. Some of the data analytics methods and systems have transformed into mechanical systems and algorithms that operate for human use over the raw data.

Techniques of data analytics will expose patterns and metrics, which would otherwise be lost in the mass of knowledge. This information can be used to automate processes in order to increase a company or system's overall performance.

Breaking Down Data Analytics

Data analytics is a broad concept covering several different forms of data analysis. To gain insight that can be used to improve stuff, any kind of knowledge can be subjected to data analytics techniques.

Manufacturing firms, for example, frequently monitor latency, downtime, and job queue for specific machines and then analyse the data to better schedule the workloads so that the machines function closer to full ability.

Data analytics can do a lot more than just point out output bottlenecks. Gaming companies use data analytics to create player reward schedules that keep most players involved in the game. Similarly, data analytics is used by many advertising providers to keep you browsing, viewing, or re-organising advertisments and get another view or another hit on the like button.

Why Does Data Analytics Matter?

Analytics of data is relevant because it allows businesses to maximise their results. Implementing it into the business model ensures that companies can help cut costs by finding more effective ways to do business and storing vast volumes of knowledge.

An organisation may also use data analytics to make better strategic decisions and help understand consumer patterns and feedback, which can lead to new and better goods and services.

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