The what, when, where and why of the five main data types
5 Data types explained
Data. It is an intangible aspect of digital technology. It’s also somewhat of a thorny subject. For a start, we’re not even sure how we should refer to “data”, since data is the plural of datum (but you’ll hardly hear someone using that term – after all, language evolves as much as technology does).
So, strictly speaking, we should be referring to data that “are” available rather than “is” available. Of the numerous data types that exist, can we group them into distinct types, categories, varieties, and classifications? In a time of ongoing technological transformation, it would be useful to understand the what, when, where, and why of data in order to appreciate the “how”.
Big data is one of the core favorite data types. It can be defined as a large amount of data that will not fit into a standard (or relational) database for analysis and processing.
While the definitions of big data might differ slightly, at the root of each are large and diverse data sets that include structured, semi-structured, and unstructured data from different sources in different volumes. Big data is one of the major data types to fuel machine learning, which forms the building blocks of artificial intelligence (AI). By analyzing big data, you’ll be able to discover patterns in consumer behavior and better understand why these patterns occur. You can also use big data to predict what could happen by using these same patterns.
Real-time data can be used to help with anything from deploying emergency resources during a natural disaster, to providing a stronger and better link between consumers and brands. You can use this data to send clients the perfect promotions at just the right time, building brand trust and loyalty. Real-time data is a true powerhouse and its potential is only just being realized.
Time-stamped data is just that – data with a concept of time ordering to define the sequence in which each data point was collected and captured. It’s usually used when collecting behavioral data, such as user interactions on a website. It can be understood as a representation of these actions over time.
Having this dataset is invaluable, especially to data scientists who are working on a customer journey analysis. You’ll be able to use this data in business to look at which next steps to take if a customer adds items to their cart but does not finish their purchase or what to do if a customer leaves a website page after only a few seconds. It’ll help you learn over time and respond in the best way possible.
While this might sound like the name of a spy in a James Bond film, dark data is actually data or information assets that a company collects, processes and stores but generally does not find another use for.
It includes video streams, photographs, and ingress-egress data from security kiosks. Tapping into this data is not easy. There’s a lot of it out there, and you’ll have to wade through it all to reach what can help you the most. This could be because almost 80 percent of data is unstructured, and more is on the way. It would take a mammoth amount of computing to find the right data for your needs, but it is not an impossible task.
Fast data allows you to quickly analyze specific data, such as a customer’s personal preferences when shopping. While these data types are still large, their value revolves around the fact that they allow you to deliver an answer now.
For example, a somewhat accurate traffic forecast now is better than a completely perfect analysis in an hour. One company that is making use of fast data is the West Japan Railway. They’ve installed cameras to notice signs of intoxication to keep people from falling onto the tracks. IBM is also building their company around these data types. In reality, it is one of the Big Data types that people will use most once it has been fine-tuned.
Don’t lose out on data
Data in business is highly useful. The five main types of data can be used to analyze customer interactions and make snap decisions, predict how consumers will act, and look at this data as it is being produced and collected.