According to an article published in Forbes, data is growing at a faster rate than ever before. Almost every commercial sector is currently witnessing a rapid growth of data generated through varied sources. By 2020, more than 1.7 MB of new data will be created for every individual worldwide every second. Although this huge amount of data has become a challenge for many companies to manage, it has also opened doors to new opportunities for businesses by offering them as a valuable customer and sales insights.
When we discuss data and its commercial applications, there are two terms that inevitably arise – Big Data and Data Science. Although each has a unique meaning, they are often confused with one another. There significant differences between big data and data science -let's explore how these concepts correlate and what their application areas are:
What is Big Data?
Big data is the large volume of data, present in both structured and unstructured form, which can be mined or processed to get valuable customer insights and improve business processes. Unlike conventional data, big data cannot be handled using traditional database programming. This heterogeneous data, available in all types and formats, can come from a myriad of sources such as customer databases, social media accounts, emails, tweets, blogs, web pages, customer signups, system logs, online discussion forums, transaction data and more. When the data is aggregated from all these diverse sources for further processing, it becomes big data. Big data is characterized by volume, variety and sources.
What is Data Science?
Data Science is an interdisciplinary field that involves the processing of big data to find insightful patterns and trends using various tools, mathematical algorithms, machine learning principles and statistical techniques. The professionals who utilize the techniques of data science to mine insights from big data in order to aid business decisions are called data scientists.
Big Data Vs. Data Science
Both big data and data science contribute to the field of data technology while being different conceptually. Following are a few key differences between big data and data science:
1. While big data refers to the huge volume of data, data science is an approach to process that huge volume of data.
2. Organizations need big data to analyze customer preferences, understand the latest trends, and enhance competitiveness. Data science, on the other hand, provides the tools and methods to utilize the potential of big data.
3. There can be uncountable sources to collect big data and big data itself is not a challenge for organizations. However, extracting valuable insights from that big data by means of data science is complex and challenging.
4. Data science and big data analytics are also confused with each other. While big data analytics is the extraction of useful information from large volumes of datasets, data science involves applying machine learning algorithms and statistical methods to train the computer to make predictions from big data without much programming.
Application Areas of Big Data and Data Science:
Healthcare Sector: Big data allows healthcare providers to improve the quality of care, reinforce the doctor-patient relationship and decrease costs. By aggregating worldwide data, physicians can understand which drugs are least likely to cause side effects, what procedures are most cost-effective for treating a particular disease and which doctors have the record of best outcomes.
Retail Sector: Big data analytics applies to every stage of the retail process. For example, by predicting market trends, retailers can identify what popular products would be and where the demand would be for those products. They can also identify customers who are likely to be interested in them. Big data also helps retailers optimize pricing for a competitive advantage.
Financial Services: Before the arrival of big data analytics, banking and financial sectors faced business challenges associated with meeting regulatory objectives, improving customer intelligence and reducing risks. Big Data Analytics has allowed financial service providers to develop data-driven products, give a more personalized service to their customers, detect and prevent fraud, retain customers and better comply with regulations and increase sales.
Internet Searches: Data science is used by search engine algorithms to deliver accurate results according to users’ queries. The tools and methods of data science are utilized to process a significant number of queries and convert them into relevant results.
Digital Advertisements: Data Science algorithms have given a whole new depth to the digital advertising world. From display banners to digital billboards and computer bulletins, each utilizes data science techniques and approaches.
Search Recommenders: The search recommender systems utilize data science to help users find relevant products from billions of products available on the Internet. These systems help companies promote their products by providing relevant suggestions to the users in accordance with their demand and search history.
The Bottom Line: Big Data vs. Data Science
Big data is unlikely to go away any time soon, due to the increasing amount of data on disparate platforms. The same is also true for data science. It is evolving rapidly to efficiently manage big data by devising new techniques and methodologies. JCU Online's Master of Data Science helps professionals leverage this great opportunity to improve their expertise and develop their career in the ever-evolving field of data science.
Shivam Arora. (2019). Data Science vs. Big Data vs. Data Analytics
EDUCBA. Big Data vs Data Science – How Are They Different?
MindMajix. (2019). Big Data Vs Data Science Vs Data Analytics