How to Deal with the Big Data Variety Issue

Big Data is the latest big thing that is having a major impact on business. Enterprises and organizations are embracing Big Data with the expectations to reap huge benefits. But Big Data is not entirely free from issues.

As the market for Big Data continues to expand rapidly, enterprises and organizations are discovering the issues that Big Data brings along with it, specially the Big Data variety problem.

Big Data

Big Data. Source Venturebeat.com

What ails Big Data?

Big Data is mainly ailed by issues of volume, velocity and variety.

Big Data deals with massive volume of data. Handling this enormous volume of data is already causing problems for a lot of enterprises which have embraced Big Data.

Big Data velocity is the issue that is concerned with the speed of data processing. It deals with the rate at which data flows in from different sources. As the flow of data is huge and continuous, it is proving to be a big issue for enterprises to analyse it at an equally rapid pace.

The third V-factor that is causing anxiety for enterprises using Big Data is variety. Big Data involves a variety of data and data sources. Coping with the varied data and numerous data sources is becoming an issue of great concern for enterprises.

Main Factors of Big Data Variety Problem

Many enterprises are facing challenges when it comes to the issue of Big Data and the variety of the enormous volume of data. The issue is more widespread in case of large enterprises as they have to deal with a huge amount of data on a constant basis. This further aggravates the problem for enterprises.

There are three main factors that are behind the Big Data variety problem. They are:

Heterogeneity: First, enterprises get engaged in a highly heterogeneous environment as a result of growth and technical innovations. The level of heterogeneity keeps on escalating with time. Enterprises need to sort through the innumerable types of systems and find ways to manage the massive number of data types.

Enterprises also need to deal with the issue of the same data being represented in various diverse formats and using different names.

Sifting data: Second, systems in an enterprise receive various kinds of data, some of which are relevant and some are irrelevant to the problems dealt by the enterprise. As such, enterprises need to identify data that is relevant to them and discard any data that can be safely declared to be irrelevant for their purposes.

This means, identification of reliable information is necessary for the enterprises dealing with huge variety of data.

Unpredictability: Third, the constant change or unpredictability in the environment of an enterprise is also an aspect of the variety issue. An enterprise often has to deal with issues like system upgradation, introduction of new systems, etc.  This results in new data types to be added and new nomenclature to be introduced. Such things enhance the data variety challenge.

Ways to Address the Big Data Variety Issue

To address the variety issue of Big Data, enterprises will have to proceed in a step-by-step method. The starting point of the solution must be the IT domain. This is because the IT domain is considered to be both, an offender as well as a victim of the variety problem.

Identify and define IT elements: The first step must deal with the issue of identifying and defining all the IT elements or assets in a systematic manner. The creation of such taxonomy will be of much help for the enterprises.

It will provide a basis for reference to anything related to IT. It will help enterprises to deal with the problem of heterogeneity as they will have a baseline of taxonomy or terminology to which they can refer to.

Identify how an object is represented: The next step is to identify the various ways in which one particular object is represented in diverse record systems. This will enable the IT professionals to filter out data that is represented in various different forms.

The result of this step will be to get relevant and compressed data that is more manageable.

Adopt and implement an effective system: The final step is to implement and adopt a system through which a continuous examination of the environment can be carried out. This will be to check for any changes such as the introduction of new types of elements or new taxonomy to refer to the same thing.

Such steps can help organizations to start dealing with the variety problem of Big Data and help them to maximize their returns from data analysis.

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