Data is everywhere, and the amount of digital data we create is still growing exponentially. It feels like ages ago when data was manually handled by workers. The Internet came, and other technological advancements that many didn’t believe were possible automatically input and generate data for themselves. Terms like Big Data and Data Science are used very often, especially in businesses and research, but what do they mean?
What is Big Data?
Big Data refers to a significant volume of data that cannot be processed effectively or with traditional methods. The limitations in the computational techniques before now use highly advanced tools and technologies to extract, analyze and manage a large amount of data. Big Data can be identified with 5Vs: Velocity, Volume, Value, Variety, and Veracity. Some tools used to process Big Data are Apache, Hadoop, Shark, Flink, etc.
What is Data Science?
Data science assists companies in making strategic decisions, and it is one of the most sought-after fields today. Data Science deals with structured, unstructured data, and semi-structured data. It involves data cleansing, preparation, analysis, and more. Data Science combines statistics, mathematics, programming, and problem-solving to analyze the data innovatively. Essentially, it studies data and finds patterns through in-depth analysis.
How are these technologies impacting the economy?
Everything now revolves around data; it has become the fuel in every activity and industry. Business orientation has also evolved from a product-focused model to a data-focused one. Data pushes for advancements in every field, enabling companies to explore new strategies in scientific discoveries, medical advancements, digital advertisement, and anything else you can imagine.
Big Data vs. Data Science
Now that these two terms are defined, let’s look at stark differences that better pinpoint what they mean. Big Data only stores, handles, and manages data, while Data Science is about analyzing it scientifically. Big Data is limited to storing and managing data, but new features in frameworks like Hadoop also facilitate the analysis. The role of these two terms differs as well. A Data Scientist is required to analyze, draw insights from data and communicate results through robust storytelling. A Big Data Specialist, on the other hand, develops, maintains, and administers Big Data clusters in massive amounts.
While Big Data is more about giving insights through data, it can come to good use for businesses in various industries. Let’s go over how Big Data is applied in the real world.
Big Data for financial services: Credit card companies, retail banks, insurance companies, private wealth management advisors, venture funds, and investment banks all use Big Data. The mountainous amount of multi-structured data is handled by big data, which can later be used in customer, compliance, fraud, and operational analytics.
Big Data in communications: Telecommunication service providers also benefit from Big Data to combine and analyze the massive customer-generated and machine-generated data created daily from expanding the customer or subscriber base.
Big Data for retail: To up the business game for either physical or online stores, retail companies need to make use of everyday data from transactions, weblogs, loyalty programs, social media, and so on to understand their customers better. Doing so will allow companies to take more suitable actions based on customer analytics.
Application of Data Science
Data Science is also applied everywhere. But to understand how it is used in programs we interact with daily, let’s look at where Data Science is used.
Internet searches: Searching for information online has never been easier. With one question or related phrases, you almost always find your answers on the first page of the search queries. That is Data Science in work, learning from the millions of search patterns before you.
Digital advertisements: The entire digital marketing spectrum uses Data Science algorithms as ads are placed strategically. Digital banners and billboards have a much higher click-through rate than traditional advertisements because the placement and timing are optimized with Data Science.
Personalized recommendation system: Youtube, Spotify, Netflix, and other streaming services collect your data using these platforms. They analyze if when you press skip, how long your mouse hovers or scrolling stops for a specific section, and other actions to influence the recommendation system that keeps you engaged. Data Science is used to provide suggestions in accordance with the user’s demands and the relevance of information.
Big Data is a part of and shares many similarities with Data Science, which could be why they’re often confused. This article highlights the differences between the two and where they are used in real-world applications.
At Dirox, we build AI and machine learning programs to help businesses better understand their data and offer better products and services to their customers. Please get in touch with our expert consultants to learn more about how you can step up your game with data.
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