The volume of data collected is key to deciding if it is considered big data. The larger data sets we can work with, the better diversified your sources will likely be. Larger data sets are able to produce more reliable analysis results on which your business can base strategic decisions. 

Velocity refers to how fast your data is being generated. Determining how much data is flowing in every minute/hour/day can help you ensure your processing methods are able to handle the throughput while still maintaining accurate outcomes. Aiming to have real-time data processing is key for strategic decision-making.

Data can be broken up into 3 different segments, structured, semi-structured and, unstructured.


Structured data: This data has the major advantage of being organised. It generally refers to data that has defined the length and format of data. This is the type of data most useful for analysis.

Semi-Structured data: Stepping down a level, this data is semi-organised. It is generally a form of data that does not conform to the formal structure. An example of this type of data is log files. Usually, this data can be made structured through additional processing before analysis.

Unstructured data: This type of data does not have any organisation properties. It generally refers to data that doesn’t fit neatly into the traditional row and column structure of the relational database. Texts, pictures, videos etc. are examples of unstructured data which can’t be stored in the form of rows and columns and is therefore difficult to process through conventional means.

How uncertain is your data? Real-world data usually isn't perfect and will need to be processed for anomalies. Big data is also variable due to the multitude of data segments resulting from multiple disparate data types and sources.

Data itself has no value or purpose to a business unless it is processed. Collecting data is a great idea for a business to adopt but processes need to be put in place to ensure this data is being analysed and used to provide value to the business. Common applications are market insight analysis, strategic decision-making, product market fit, etc.