Big data refers to large volumes of complex data that continue to grow over time. It is commonly described in terms of the three V’s: 

• Volume: Big Data is massive in terms of the volume of data, which can range from terabytes to petabytes.  
• Velocity: Big Data is being generated in real-time and the rate of data generation continues to rise. 
• Variety: Big Data can originate from a wide range of data sources in a variety of formats.  

• Structured data: Data that is organized and stored in databases and spreadsheets. It can be easily analyzed using analytics and data science tools and techniques. Structured data can be explored with a basic understanding of the data, which makes it easy to interpret.  
• Unstructured data: Raw data that lacks any clearly defined organizational structure or categorization. Unstructured data needs to be converted into structured data before it can be analyzed, which is a tedious process. Unstructured data requires expertise concerning the context of the data so that it can be properly interpreted. 
• Semi-structured: Data that possesses some degree of organizational structure or categorization. Semi-structured data still needs to be converted to structured data before it can be analyzed 

Big Data has the potential to give businesses immense insights into their customers: Businesses can design better products and services, improve customer engagement and retention, drive customer acquisition and sales growth, boost customer satisfaction, and much more. 

Presently, businesses can store vast amounts of data using storage solutions such as cloud storage, data warehouses, and data lakes. However, the vast amount of data also comes with increased responsibility to maintain the privacy and security of customer data. In theory, more data means deeper analysis and more accurate customer insights but big data’s intrinsic ambiguity and complexity make this a challenge. Big data is inherently difficult to fully comprehend using traditional processes. The main challenge is robustly processing big data within relatively short intervals of time (days, hours, real-time, etc.). Therefore, Artificial Intelligence (AI) and Machine Learning (ML) are essential for businesses seeking to understand their data.