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Over the years we have been hearing about multiple professions and skills. Life has been made very easy with the Data Magic 101 of technology and freelancing. But with that, there is another profession that has emerged which is data analytics. Many people are talking about Data Magic 101. It is something very new for us non-techs, but what is it? Let’s dive into it.
What Is Data Analytics?
#WhatIsDataAnalytics
For beginners, data analytics is the science of analyzing raw data to make conclusions about information. So, to make it easy for you data analytics is the use of raw data to find trends and answers to questions. It has a broad scope across the field. It can shift from industry to industry, but if there is one thing, then it is that it can be used in every industry!
To put it very simply, data analytics is the process of examining, cleaning and then transforming data. After this useful data is extracted from it and that useful information is then used.
It involves various things in which we have to firstly collect data, then the next step includes the cleaning of that data. After that the data is transformed and then that data is modelled. The last step includes the reporting and communication of that data.
Understanding Data Magic 101
#UnderstandingDataMagic101
To know what data analytics we first need to understand it. Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of Data Magic 101 technique 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 process and increase the overall efficiency of a business or a system.
To make it simple, we take the example of manufacturing companies that often record runtime, downtime and work queue for various machines and then analyze the data to better plan the workloads so that machines can operate closer to peak capacity.
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 clicking, watching or re-organizing content to get another view or another click.
Why Is Data Analytics Important?
#WhyIsDataAnalyticsImportant
Data analytics is very important because it helps businesses optimize their performances. Implementing it into a business model means companies can help reduce costs by identifying more efficient ways of doing business and storing large amounts. Data analytics is very important in today’s world as it helps in different things as well this can also be;
- Informed decision making
- More efficiency
- Improved and enhanced customer service
- Competitive and market advantage
- Risk management
- Innovation and development
- More scalability and growth
Types Of Data Analytics
#TypesOfDataAnalytics
There are various types of data analytics;
- Descriptive analytics: In this the the historical data is summarised to understand to understand and examine what happened in the past.
- Diagnostic analytics: In this, we understand what happened in the past by identifying patterns and relationships.
- Predictive analytics: Once we are done with diagnostic analytics we use the same statistical data and data to predict future events. This involves machine learning and regression.
- Prescriptive analytics: This one will provide recommendations for the actions that are based on data analysis.
The Bottomline
Data Magic 101 helps individuals and organizations make sure of their data in a world that’s increasingly becoming reliant on information and gathering statistics. A set of raw numbers can be transformed using a variety of tools, and techniques, resulting in informative, educational insights that drive decision-making and thoughtful management.
