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- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
Today, it is practically impossible to lead our lives without data. This is for the sole reason that data has become one of the most critical aspects of businesses. Thus, there is a constant need for technologies that stand the potential to transform the data available so that the business objectives are met. In this magical world of data, there seems to be a lot of confusion between data science, data analytics, and big data. Keeping this in mind, we will talk about Data science vs data analytics vs big data: What does your business need?
Data science is that multidisciplinary approach that deals with analyzing complex data, creating new analytics algorithms and tools for data processing and purification, and even building powerful, useful visualizations. Everything from cleaning data, preparing it to its analysis – all of this is what data science is all about. Data science is the implementation of various techniques that are used to extract insights and information from the data available.
Data science has catered to a wide range of applications – recommendation systems, internet search, and digital advertisements, to name a few.
Data analytics is nothing but the process of analyzing data in order to extract meaningful data from a given data set. The main objective of data analytics is to help individuals or organizations to make informed decisions based on patterns, behaviors, trends, preferences, or any type of meaningful data extracted from a collection of data. Basically, data analytics includes applying an algorithmic and/or mechanical process to be able to derive insights and run through several data sets. The whole process involves everything from inspecting, cleansing, transforming to modeling data. The job role of a data analyst includes performing exploratory data analysis to visualize the data. Following this, they filter and clean the data by checking the reports generated with the help of various data analytics tools such as R, Python, etc.
There are numerous applications of data analytics. Some of the major industries include – the healthcare sector where data analytics is used to track and optimize patient flow, treatment, and equipment used in hospitals, gaming industry where the job is collecting data to optimize and spend within and across games, travel industry where the travel companies can gain insights into the customer's preferences.
Big data, as evident as the name, is nothing but a large volume of data. This data is so large that it cannot be processed effectively with the traditional applications. As traditional data management tools and techniques cannot handle such a large volume of data, data warehouses and data lakes have emerged to be the go-to solutions. A point worth making a note of is that big data is used to analyze insights, which can lead to better decisions and strategic business moves.
Numerous industries generate significant data. The financial sector relies heavily on big data for fraud detection, operational analytics, compliance analytics, and a lot more. Additionally, the retail sector also makes use of big data to understand the customers better. Apart from these two, countless other industries make better-informed decisions based on the big data gathered.
