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Data Analytics

Data Analytics (DA) examines data sets to find trends and draw conclusions about the information contained in them. Increasingly, data analysis is performed with specialized systems and software. Data analytics techniques and technologies are widely used across business industries to enable organizations to make more informed business decisions. Scientists and researchers also use analytical tools to confirm or refute scientific models, theories, and hypotheses.

Why is Data Analytics Important?

Data analytics is important as it helps businesses to improve their performance. Applying this to business models means that companies can help decrease costs by identifying more efficient ways of doing business. A corporation can also use data analytics to help make better business decisions and analyze customer trends and satisfaction, which can lead to new – and better – products and services.

Types of Data Analytics

  • Descriptive Analytics: Describes what happened over a certain period of time. Has the number of views increased? Are this month’s sales higher than last month’s?
  • Diagnostic Analytics: This focuses more on why something happened. This includes more varied data inputs and some hypotheticals. Did the weather affect beer sales? Did this latest marketing campaign affect sales?
  • Predictive Analytics: It moves towards what is likely to happen in the near term. What happened to sales the last time we had extreme heat? How many climate models predict a hot summer this year?
  • Prescriptive Analytics: It suggests a course of action. If the average of these five climate models measures a hot summer more than 58% likely, we should add an evening shift to the brewery and rent an extra tank to increase production.

Who is Using Data Analytics?

Data analytics has been adopted by many sectors, such as the travel and hospitality industry, where change can be rapid. This industry can collect customer data and find out where the problems are if any, and how to solve them. Healthcare is another sector that combines the use of large sums of structured and unstructured data and data analytics can help in quick decision-making. Similarly, the retail industry uses a lot of data to meet the changing demands of consumers.

Data Analytics Vs. Data Science

As automation increases, data scientists will focus more on business needs, strategic oversight, and deep learning. Data analysts working in Business Intelligence will focus more on model building and other routine tasks. In general, data scientists focus efforts on generating broad insights, while data analysts focus on answering specific questions. In terms of technical skills, data scientists of the future will need to focus more on machine learning operating procedures, also known as MLOps.

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