The big data market is valued at a whopping US $103 billion. Analyzing infinite data streams allows businesses to make informed, logical decisions. A holistic approach toward it entails combining sophisticated Artificial Intelligence (AI) and traditional analysis tools. So let’s delve into what is big data.
At a time when over 97 per cent of companies around the world are investing in machine learning, one thing is certain: something deep is brewing in the seething data cauldrons of industries. But, we must first learn about its history and importance and walk the chronology to know how it came to be!
- Alan Turing invented the first massive data-processing machine at the helm of the Second World War in 1943 to decipher Nazi codes.
- The USA launched its first digital data center in 1965.
- In 1997, Google launched its first domain, highlighting the climb of industries catering solely to collecting and processing data.
- Major businesses kept track of increasing web traffic through click rates, search histories and logs, location, and IP tracking, resulting in a host of possibilities.
- Roger Mougalas coined the term ‘Big data’ in 2005. That year also saw the invention of Hadoop, a Nutch-based open-source software that later merged with MapReduce (processes information parallelly on multiple nodes).
What Exactly is Big Data?
It can be defined as extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations. Big data is stored in a secure system but can be easily accessed and analyzed to help answer questions, provide valuable insights, and give confidence in making strategic business moves.
Now that we know ‘what is big data?’, let us learn more about its importance. The last two decades witnessed massive social changes in people’s perspectives. For example, it has introduced customized online shopping, advanced models of fighting and predicting crimes, and accentuated manufacturing processes. But how does it manage such a feat?
Define the Three V’s of Big Data
- Volume: This refers to the colossal amount of data in the servers of internet giants. It is one of the key concepts that depend on the number of users of a platform. For instance, Facebook has a 250-billion-image repository which increases every single day. Twitter handles about 500 – 700 million tweets daily on average. Volume is a defining characteristic.
- Velocity: It means how quickly data comes to the existing servers. Taking the example of Meta, the 350 million images added to its servers each day determine the velocity. Sensor efficiency for the Internet of Things(IoT) also depends on velocity, as the efficiency of devices depends on how much information is transmitted every second.
- Variety: Different kinds of information, such as PDFs, images, audio, and video, define the variety. Take examples of multimedia posts that use videos, audio, reels, and GIFs. They are all encrypted and unstructured.
What are the 3 Types?
- Structured Data: It is tabular in the form of relational databases where all rows have an equal number of columns. SQL (Structured Programming Query Language) is used to process structured information.
- Unstructured Data: It can either mean there is no pre-defined model or there is no particular way in which large sets are organized. They usually include videos, audio, and binary files devoid of a specific structure.
What are Some Use Cases?
It ensures strong predictive models that help industries identify hidden market trends and customer choices and streamline business operations with robust analytics. Understanding what it is, entails moving hand-in-hand with the tumultuous rise of AI and machine learning. These exterior analytical elements aid data scientists in rapid management & structuring.
Identification of Risks: With its predictive algorithms, analytics help us evade different forms of unexpected threats and provide effective risk management solutions.
Innovation: Data from various customer bases enable distinguishing between what is desired and what is necessary. Keeping track of current marketing practices and fusing them with insights helps maneuver buying trends and track customer behavior.
Customer Retention & Acquisition: Observing consumer behavior is central to a customized shopping experience. Amazon has made the best use of customers’ digital footprints and practices laser targeting.
Streamlining Company Costs: Finally, storing and systematically computing reduces company costs and drives efficiency.
Frequently Asked Questions
#1: What Industries are Known to Use Big Data Analytics?
- Media & communications
- Banks & security services
- Governance & administration
- Retail and wholesale trade
#2: What are the Risks of Big Data?
With great power comes great responsibility. There are many risks that companies need to be aware of, including:
- Malevolent usage of the data in organized crime
- Data Usage Transparency by companies
- High potential for breach of privacy of individuals
- Unintentional damage by third-party sharing of private information
#3: Where Can Big Data be Stored?
Data warehouses and lakes. While the former can hold only structured data, the latter can hold all forms, including semi-structured information. Data stored in a data warehouse has been cleaned and processed, ready for strategic analysis, while data stored in a data lake may lack consistency and structure.
#4: How is Big Data Collected?
- Online monitoring (caches & cookies)
- Surveys & Interviews
- Transactional tracking
- Online forms
#5: What are the Four Big Data Models?
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By Bishwadeep Mitra
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