Analysts have been processing data for centuries in order to extract useful information from it. With the advent of modern computers, data processing has been revolutionized.
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DBMS systems can efficiently store, manipulate, edit, and extract useful information from data. However, with the advent of social media, online marketing, and digitalization of manual data entry systems, the amount of data has grown exponentially. Big data is so huge that existing systems are unable to process such huge data. Here comes the term big data.
Industry analyst, Doug Laney says that data having the following three characteristics, known as 3 Vs, is considered big data.
The data is so large that it can’t be handled by existing relational DBMS systems. For example, the data obtained from twitter posts and other social media platforms.
Data is generated at such a fast speed that it can’t be processed at runtime.
Data that doesn’t fulfill all of the above three characteristics aren’t considered big data. For instance, the data which is not huge, being generated at a pace that can be handled by traditional DBMS and is well structured and relational isn’t considered big data. Data that fulfills one of the three Vs is Big data.
We have looked at what Big Data is, and what it is not. Here are the types of Big Data.
Structured big data refers to any data whose storage, access, retrieval, and processing is a fixed format. Such data is usually highly organized in databases that search engine algorithms can access and process easily and seamlessly.
Although organized, structured big data has the potential to grow exponentially to reach multiple zetabytes – equal to 1 billion terabytes. An example of structured data is that which is stored in database management systems (DBMS). A simple database such as an employee record is ‘structured’ data.
Any data that has a format or structure that is basically unknown falls under the unstructured data category. Such data is very challenging when it comes to processing and analyzing, meaning it consumes a lot of time and the most storage resources. Simple formats of unstructured data will consist of text files, images, videos, which have increasingly become the norm today. Look at your typical email as one such example.
Semi-structured data is one that has both of the above types of data. It may not be defined as either. However, the semi-structured data still contains critical data that needs processing and from which businesses can derive value.
Though anyone with specialized software and hardware products can collect big data but primarily big data is collected by researchers for research purposes and business analysis for extracting useful information from the data which leverages them to take business decisions.
Great strides in the Internet of Things (IoT), AI, and machine learning technologies have given major companies and business conglomerates the power to collect troves of data. Now tools like Google Analytics, social media, transactional data, and maps are all methods anyone with the knowhow and tools can collect Big Data.
For instance, with over 2 billion users across its many social media platforms, Facebook can be one of the largest sources of Big Data for companies. While Facebook doesn’t sell user data directly, companies can leverage Facebook Pixel to get access to targeted customer details.
Acxiom and Oracle are two of the largest companies that collect, analyze, and sell customers, including from e-commerce platforms and government websites.
Numerically speaking, data which consist of petabytes(1024 terabytes) or more is considered as big data. However, different analysts have different definitions regarding the magnitude of big data. It is also said that data that cannot be processed by a single machine, or if the data processing requires specialized tools for storing and processing, the data is called big data. Also, if you need to hire a team of data-scientists just for manipulating data, consider your data, Big data.
Big Data is now big business and so many industries are in the race to have as much data as possible. Here are five of the biggest users of Big Data today.
Big Data and AI are changing the health industry, with advancements in predictive analytics, telemedicine, and smart wearables all key to saving lives and transforming healthcare provision.
The IT sector is one of the largest consumers of Big Data, with IT companies looking to Big Data for automation, optimization, and risk management in their businesses. Big Data, AI, and ML are all being deployed to power the innovation revolution being witnessed in the IT sector.
The education sector and wider Academia are also making the biggest investments in Big Data. Apart from researchers, Big Data is powering the digitization that is transferring the sector.
The main applications Big Data in the banking sector revolve around fraud detection, with systems relying on this information to provide real-time monitoring of potential fraudulence, e.g. in credit card fraud.
The manufacturing sector uses Big Data to automate operations, increase production, and improve supply strategies. Predictive analytics is also helping promote lean strategies as companies look to maximize production as they reduce waste.
There are different types of Big Data, but what makes it different from just “data” is that the former is so large or voluminous that it’s practically impossible for conventional databases to store, manage, and process. If it can be processed the traditional DBMS, it’s not big data.