In today's world, data is growing at an unimaginable speed. It is all around us and has become a part of our daily lives. Digital data is set to double every two years and has been shaping the future world for us! With data growing in such incredible amounts, the issues arising with its storage and processing have to be sorted. As data processing comes into picture, confusing terms like Data Science vs Big Data vs Data Analytics come into play! This article is all about sorting this confusion by briefing about each.
What is Data Science?
Data Science deals with structured and unstructured data by discovering their hidden patterns with the help of various tools, algorithms, and machine learning principles.
It involves problem-solving mechanisms and constructs new processes for data modeling and production using prototypes, algorithms, predictive models and custom analysis that extracts insights and information from data. In simple words, it cleanses, prepares and aligns data.
The Digital Marketing spectrum is totally dependent on Data Science for its digital advertisements. Search engines run on data science algorithms to deliver personalised results for search queries, enhancing the user experience by manifolds.
A Data Scientist performs data analysis to discover informational insights from the generated data by using various advanced machine learning algorithms to identify the occurrence of a particular event in the future.
These data make up extremely important business information.
What is Big Data?
As the name suggests, Big Data refers to huge volumes of data that can't be processed effectively using traditional data processing applications within a given time and value. The analysis of Big Data poses many challenges in sampling, capturing data, data storage, data analysis, sharing, transfer, Querying, visualization etc. Big Data Analysis hence involves predictive analytics, user behaviour analytics, and other data analytics methods to extract information from big data.
This information extracted from big data can be used for better decision making and strategic business moves.
Big Data is used by governments, health managements, insurance firms, retail banks, credit card companies and many other institutions which deal with huge amounts of data that is difficult to store in one computer.
Big Data helps in gaining new subscribers or retaining customers by combining the data and analysing the masses of customer generated data and machine generated data that is being created every second.
Some of its applications also includes analysing weblogs, transaction data, customer transaction data, social media, store brand credit card data and loyalty program data.
What is Data Analytics?
Data Analysis is used in Data cleansing, transforming and modelling data. It is the science of examining raw data to reach certain conclusions about the information. With the help of data analytics, we can discover useful information from the raw data to support our decision making and business strategy. Data Analytics is also very helpful in identifying existing business theories and models. If required, this technique verifies and disproves them.
Data Analytics can help optimize the purchasing experience for customers through weblog or social media data analysis. With the help of data analytics, businesses can view insights into customer's preferences. With the help of Data Analytics, companies can get a fair idea of what their customer's likes and dislikes are.
Data Analytics is centered around controlling and monitoring of network devices and dispatch crews. It also manages service outages. It integrates millions of generated data points in the network performance and helps businesses to monitor the network.
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