Differentiating Big Data from Data Science
As many companies are now starting to utilize data to make data-driven decision making, many misconceptions regarding the difference between big data and data science have become inevitable. Big data is a scientific field that deals with data sets that are too large or too complex for conventional data processing software, and therefore, cannot be analyzed using traditional statistical methods. Data Science is the combination of statistics, mathematics, programming, problem-solving and innovative data processing, with the ability to find patterns, by cleansing, preparing, and aligning the data. Understanding the difference between Big Data and Data Science, businesses can manage their time and resources more effectively and help leaders make long-term wise decisions.
Big data handles large data, data science analyzes data
The amounts of data that are gathered today is massive. Based on International Data Corp. by 2020, each person on earth will generate an average of about 1.7 MB of data per second. Big Data operates with voluminous information that is distinguished by velocity, variety, and volume. Nevertheless, it takes data science to derive useful knowledge from Big Data. Data Science is an area which includes working with and using a large amount of data to create predictive analytical models.
Big data process huge volumes of data and generate insights, data science understand pattern within data and make decisions
In order to process data, extract information and interpret findings, Big Data uses analytical processing that helps generate insights. Big data adoption is very important for the growth and survival of any organization. It can be used to predict products or services that might be trending in the market, to reduce operational risks, and even to influence customers’ purchasing behavior. In order to get the right information from Big Data, companies need Data Science to understand the pattern within data to make data-driven decisions. Data Science is about gathering, processing, analyzing and using data for various business purposes.
Big data relates more on technology, data science focuses on strategies
Using conventional data analysis methods, implementing Big Data may not be possible. It requires special models, techniques, softwares, and systems to extract insights and information as needed by businesses. Meanwhile, Data Science is a scientific method that applies mathematical and statistical ideas in a computer software to process big data by using data cleansing and data mining techniques to prepare and align big data for intelligent analysis to extract insights and information. Big Data relates more to technology, analytics tools and software, while Data Science focuses on strategies for business decisions.
Big Data and Data Science are inseparable. Understanding the key differences between the two can help your organization to utilize data and information better. Companies can use Big Data and Data Science to get a better understanding of what customers want. No matter what your industry is, implementing Big Data Indonesia can help you make sense of all the information. Practically every industry nowadays requires some form of Big Data, and Data Science education is becoming increasingly profitable. As companies grow bigger, they need precise Big Data analytics to win the market.