The term Big Data is self-explanatory in itself.
If we split the term and consider its meaning in literal sense, it is exactly what it sounds – data that is ‘large’ in size or not countable.
Big Data is basically the ginormous amount structured and unstructured data that is really complex to process with the help of traditional data management tools. Organisations generate this kind of volume on a daily basis, usually in terabytes or more. This kind of data produced is not ‘all’ important for companies because they retain only that data which matters the most to their business.
Types of Big Data
Big Data is generally identified into three categories, namely:
- Structured data – This type of data can be stored, accessed and processed in a fixed format in a database. Example: Organised employee data table consisting information like the employee id, name, gender, department, salary, etc.
- Unstructured data – This is any unknown form or structure of data that is large sized and stored in an unorganised manner. Example: A heterogeneous data source consisting various forms of text, video, image files.
- Semi-structured data – This is that data which is not organised into a particular repository, like a database. However, it may include information associated with it that allows elements contained to be addressed. Example: Metadata tagging, or data represented in an XML file.
Characteristics of Big Data
There are four main attributes of Big Data, namely volume, variety, variability, and velocity.
- Volume – As mentioned earlier, the name Big Data reflects its literal meaning. The size of data plays an important role in ascertaining the value out of data.
- Variety – Variety in Big Data denotes irrelevant and multiple sources and the nature of data. This data comes from various digital sources like log files, sensors, devices, transactional applications, web, and social media; most data is generated in real-time and that too on a very large scale.
- Variability – There are times when the data shows inconsistency, which makes it difficult to process or handle the data effectively.
- Velocity – Velocity here means the speed at which the data is generated. The real potential of data is determined by its speed of generation and the speed at which it is processed to meet the demands. In terms of Big Data, velocity means the speed at which the data flows in from multiple sources at an unprecedented rate. This continuous unstoppable flow of data is always colossal in size.
Importance of Big Data
The importance of Big Data is not determined by how much data an organisation possesses, but how it processes and uses the gathered data. Every company has its own methodology of using data – the more proficiently it uses the data, more are the chances for it to grow. Any organisation can be benefited by its data only if it analyses the data properly.
It can save costs: Incorporating Big Data tools like Cloud-based analytics can benefit an organisation in terms of cost savings. Tools like Apache Spark, Hadoop, Samza, Flink, etc., can help identify efficient ways of running a business.
Saves time: High-speed of Big Data tools and in-memory analytics can easily detect new sources of data that helps businesses analyse the data quickly and make faster decisions based on learning.
New product development: New products can be created as per the liking of customers. This can be achieved by identifying the trends of customer needs and satisfaction by means of analytics.
Understanding market conditions: One can get a better understanding of current market conditions by analysing big data. For instance, a company can find out the products that are sold the most in the market by analysing customers’ purchasing behaviours. This is one of the effective ways to stay ahead of a competitor.