For most modern businesses, data is everything. The term “big data” continues to appear in articles and technology news around the world, where writers praise its importance to businesses and hail it as the future of strategic decisions. Big data refers to the aggregation and analysis of data from multiple sources. It includes:
- Unstructured data sources like social network platforms, emails, blogs, tweets, digital images, digital audio and video, online data sources, mobile data, websites, and more
- Semi-structured data like XML files, system log files, and text files
- Structured data sources, including databases, OLTP, transaction data, and other structured data formats
Understanding the relationship between data science and big data
Big data technology enables users to pull and combine data from a number of sources and also store and analyze data sets of that size. It means that businesses aren’t limited in their data analysis and that creating, defining, and analyzing data takes less time than ever before. Creating meaning from the analysis of big data, however, is really only made possible by data science. Big data analysis can help identify trends, point out patterns, and process huge volumes of information that traditional databases can’t, but data science is able to answer the “why” or the “what’s next” after big data shares the “what” and the “how.”
What is data science?
The education course site EDUCBA defines data science as, “a specialized field that combines multiple areas such as statistics, mathematics, intelligent data capture techniques, data cleansing, mining and programming to prepare and align big data for intelligent analysis to extract insights and information. Though this it may sound simple, data science is quite a challenging area due to the complexities involved in combining and applying different methods, algorithms, and complex programming techniques to perform intelligent analysis in large volumes of data. Hence, the field of data science has evolved from big data, or big data and data science are inseparable.”
Is data science more important that big data?
Why is data science important? Essentially, data science is the engine that allows big data to work for businesses. As data science is the method that extracts meaning from big data sets, it’s easy to see exactly why data science is important. Here are some of the things that data science enables.
1. Provides meaning from potential
Every dataset has the potential for finding something, but data science is the key to extracting or realizing that potential. Insight is the reason that big data processes exist – people and businesses are looking for valuable information from raw data and data science are able to cut through the complexity of large swaths of often disparate-seeming data.
2. Goes deeper than analysis
Data science utilizes machine learning algorithms and statistical methods to train big data computers to learn without much programming to make predictions from big data.
3. More than a tech tool
Big data ultimate refers to the technology that allows for distributed computing, including programs like Hadoop, Java, and others. This software is what handles the actual data processing, while data science focuses on data dissemination using mathematics, statistics, and other methods to help businesses focus on strategy and decision making.
Much like a car is built of important parts, without roads or a driver, the car will never realize its potential or power. The relationship between big data and data science is similar. Big data can exist without data science, but it won’t be able to have the impact or power that it could have otherwise. Being able to use the power of big data as actionable insight is one of the main reasons in understanding why data science is important to businesses of every size and in every industry.
