Big data is basically vast volumes of data that cannot be stored or processed using traditional data storage methods. With big data, we are talking of gigabytes, terabytes, petabytes, exabytes, etc.
This, however, is not the only definition of big data. Depending on the context of use, small data may also be referred to as big data. A perfect example is when you are attempting to attach a file that is one hundred megabytes to an email. The email system does not support such a file.
Below are seven types of big data that companies should be tracking:
1. Structured Data
Structured data refers to data you can process, store and retrieve in a fixed format. Humans and machines are both sources of this type of data. This type of big data is usually highly organized and can be stored and accessed through a database using a simple search engine algorithm. In a company’s database, information on the employee table would be structured as employee details, their salaries, their job positions, and whatever other information is relevant.
2. Unstructured Data
Unstructured data is a term used to define data that does not have a specific structure or form of any sort. Due to the unstructured nature of this information, it is quite hard to process and analyze. Unstructured data is usually classified depending on whether it came from machines or humans. Unstructured data generated by humans can be found in abundance across the internet since it includes mobile data, social media data and website content.
A few examples of unstructured data generated by machines include scientific data obtained from different experiments, satellite images, and radar data obtained from various facets of technology.
3. Semi-Structured Data
This type of data is what stands in the gap between structured and unstructured data. Semi-structured data and unstructured data may sometimes appear to be similar since, although it is unstructured, it still contains some aspects of organized data that makes it easier to process and analyze. A good example of data that can be considered semi-structured is No SQL documents. This is because these documents contain keywords that can be utilized to process them efficiently.
4. Fast Data
The value of fast data revolves around the ability to provide a near-accurate answer now. To put it in context, fast data is what allows you to get a traffic forecast that is somewhat accurate in real time. This is better than having to find out for yourself that there is traffic an hour later. Another excellent example of where fast data is being utilized is the West Japan Railway. Cameras were installed using fast data to detect signs of an intoxicated person and take necessary measures to keep them from stumbling onto the railway tracks.
This is the type of big data that would help you analyze a customer’s personal preferences the moment they stop by a kiosk.
5. Lost Data
These types of big data are also known as operational data. You get lost data from chemical boilers, manufacturing equipment, industrial machinery, and basically any other item you would find in a commercial building or industrial plant. This type of data is not technically lost; it is known as lost data because most of the times it is locked in operational systems. Operational data can also include information about the company’s creditors, suppliers, direct competitors, workforce and customer information.
6. Dark Data
Dark data includes ingress-egress data, handwritten comment cards, photographs and video streams from security kiosks. Tapping into these types of big data is not an easy task since the amount of dark data out there is enormous quantities, and it takes a lot of computing to identify scenes that are relevant to your business.
Some companies such as Lytx have found a way of avoiding the unnecessary buildup of dark data. Lytx provides a video streaming service, especially to long-haul trackers, designed to start recording only when the driver swerves, or they suddenly step on the emergency brakes.
7. New Data
New data refers to the type of information one would want to get, and could get, but not at the moment. This is the type of data used to obtain approximations. For example, new data has been used to arrive at the conclusion that the amount of water that gets lost due to leaks in pipes is approximately 8.6 trillion. This is literally enough to fill up the hoover dam. This type of big data can be used to analyze indoor traffic patterns.
Big data analysis is critical to any business since it helps an organization reduce the cost of doing business and consequently bolster growth. There are tons of types of big data you can track in almost every sector, profession, and industry. The beauty of tracking such data is that it helps you identify trends and as such tweak your systems and processes to achieve maximum efficiency.