Big Data Vs. Data Science

There may not be much a difference between big data and it's science yet, it has always instigated the minds of many and put them into a dilemma. In this blog, we will cover the real difference between these two terms in detail. We will start with what each of them mean.

Data Science

An evolutionary extension of statistics that deals with large volumes of information with the help of computer science technologies. It has played a significant role in the expansion of artificial intelligence, Machine learning, the internet of things (IoT), etc.

Big Data

Deals with the vast collection of assorted information from various sources and is also available in standard database formats. It helps to give better insights about decision making and strategic management.

Classification

Big data is classified as structured, semi-structured, and unstructured:

• Unstructured – Information that is not pre-defined such as, social networks, emails, blogs, digital images, and contents
• Semi-structured – Where there is no separation of information in the database model plus, the amount of structure depends on the purpose. Example: XML files, JSON files, NoSQL database, etc.
• Structured – Follows a reconcilable order and can be easily accessed and used by a person or a computer program. Example: names, addresses, etc.

While structured information is quite easy to understand, the unstructured form requires customized modeling techniques to extract information using various statistical tools and technologies.

Key differences – Big Data vs. Data Science

There is quite a bit of confusion between these two subjects. Though machine learning is a subset of data science, they are not the same.

• Big data analytics helps organizations to harness information efficiency to understand the untapped market, thereby enhance competitiveness and efficiency. On the other hand, data science is concentrated more towards providing modeling techniques and methods to evaluate information in a precise manner.
• The amount of raw information collected by companies is massive. The attempt to utilize this information to extract actionable insights is data science.
• The 3Vs guiding the use of big data are velocity, variety, and volume.
• Data Science uses theoretical as well as practical means to garner insights from large volumes of information. On the other hand, big data is a pool of unstructured information with no inherent value unless analyzed with deductive and inductive reasoning.
• Big data analysis involves an enormous amount of information that needs to be mined. Data science uses machine learning algorithms to design and develop statistical models to generate insights from the pile of erstwhile unstructured information.

Where the former relates more with technology, computer tools, and software, the latter focuses more on business decisions.

Conclusion

The current trend in the information segmentation industry tends to focus more on big data rather than the science. The use of one allows businesses to observe various customer patterns, trends, and behavior, while the other adds value to a business by assisting companies in better decision making.