Data is a related raw fact, collated and processed into information that can be used to aid decision-making. Data can be seen as value, facts, and figures. These data can be generated through primary or secondary sources, in structured and unstructured form, and stored for future reference and analysis purposes. Data can be presented according to its nature/type.
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is data with so large a size and complexity that none of the traditional data management tools can store it or process it efficiently.
Types of Big Data
Structured: Any data that can be stored, accessed, and processed in the form of a fixed format is termed structured data. Over the period, talent in computer science has achieved greater success in developing techniques for working with such kinds of data and also deriving value out of it.
Unstructured: Any data with an unknown form or structure is classified as unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos, etc. Today organizations have wealth of data available to the but unfortunately, they don’t know how to derive value from it since this data is in it raw form or unstructured format.
Semi-structured: Semi-structured data can contain both structured and unstructured data. We can see semi-structured data as a structured form but it is not defined.
Characteristics of Big Data
- Volume: The name Big Data itself is related to its enormous size. The size of data plays a very crucial role in determining its value of data. Also, whether a particular piece of data can be considered Big Data or not, is dependent upon the volume of data.
- Variety: Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. Before now, spreadsheets and databases were the only sources of data considered by most of the applications but today, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining, and analyzing data.
- Velocity: The term ‘velocity’ refers to the speed of the generation of data. How fast the data is generated and processed to meet the demands, determines the real potential of the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, social media sites, sensors, mobile devices, etc. The flow of data is massive and continuous.
- Variability: This refers to the inconsistency or lack of fixed pattern of big data which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
Advantages of Big Data Processing
- Businesses can utilize outside intelligence while taking decisions. Access to social data from search engines and sites like Facebook, and Twitter, is enabling organizations to fine-tune their business strategies.
- Improved customer service. Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
- Early identification of risk to the product/services with feedback from consumers.
- Better operational efficiency. Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data.