Characteristics Of Big Data

Big data can be described by the following characteristics:

Volume
Variety
Velocity
Variability
(i) Volume – The name Big Data itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one characteristic which needs to be considered while dealing with Big Data solutions.

(ii) Variety – The next aspect of Big Data is its variety.

Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, 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.

(iii) Velocity – The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.

Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.

(iv) Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.

Advantages and Disadvantages of Big Data
The increase in the amount of data available presents both opportunities and problems. In general, having more data on customers (and potential customers) should allow companies to better tailor products and marketing efforts in order to create the highest level of satisfaction and repeat business. Companies that collect a large amount of data are provided with the opportunity to conduct deeper and richer analysis for the benefit of all stakeholders.

With the amount of personal data available on individuals today, it is crucial that companies take steps to protect this data; a topic which has become a hot debate in today's online world, particularly with the many data breaches companies have experienced in the last few years.

While better analysis is a positive, big data can also create overload and noise, reducing its usefulness. Companies must handle larger volumes of data and determine which data represents signals compared to noise. Deciding what makes the data relevant becomes a key factor.

Furthermore, the nature and format of the data can require special handling before it is acted upon. Structured data, consisting of numeric values, can be easily stored and sorted. Unstructured data, such as emails, videos, and mandalasystem.com text documents, may require more sophisticated techniques to be applied before it becomes useful.