Why Most of the Big Data Initiatives in Organizations Fail?

By Sushil Kumar Tripathi , AVP - Technology, Kellton Tech Solutions Limited

A huge amount of data is floating around us; information related to business, customers, their transactions, click stream, server logs etc. But do you know what to do with it? Is there something meaningful for you? What can be extracted from it?

We need to understand that Big Data is emerging as the next generation of technology innovation and enterprises must prepare themselves for the new digital era. Data science and data engineering are the two major contributors to Big Data Analytics.

To understand the importance of Big Data Analytics, let’s look at the difference between data science and data engineering. Often, these terms are used interchangeably, however, there is a fundamental difference in their role to bring out the potential of Big Data for an enterprise.

Data science refers to the application of scientific methods to a project that involves data analysis. It comprises of several tools ranging from statistics, computer science, UX/UI design, mathematical calculations, as well as domains pertaining to the data itself.

On the contrary, data engineering refers to application of engineering methodology to projects requiring data analysis. Utilizing tools such as data stores, complexity analysis, cluster computing etc.

Why Big Data Analytics is Important for Your Enterprise?
Examining huge data streams helps reveal hidden patterns, correlations, and other customer insights. Technologies like Hadoop, cloud-based data analytics etc. help significantly to reduce costs, identify efficient methods of business operations, enable quick decision-making processes, and create new products/services to satisfy dynamic customer needs.

Factors Resulting in the Failure of a Big Data Analytics Implementations:

1. Lack of understanding business objectives: 
Despite what data analysis tells us, most of us tend to believe their intuition. Often, businesses tend to overlook two important factors: previously tested cases and sufficient time for experimentation.

The scope of big data is fragmented. Despite incorporating several tools and cloud platforms it may not serve as an ideal solution for all industry verticals. Several chinks in the armor need to be addressed before implementing the pool of data intelligently.
Ask Yourself:
a. Why do you want to use big data analytics?
b. How much budget can you allocate for the project?

2. Lack of application of collected data: 
Project timelines and budget constraints are two major constituents of application of Big Data Analytics. Data insights, if delayed in application, lose their relevance. Similarly, you must not exceed the allocated budget.

3. Lack of skilled resources: 
Many projects fail or postpone indefinitely due to insufficient skills of the team members. Big data plans may disappoint, if you do not have the right people with right capabilities.

Ideally in this case, technology trumps organizational preparedness. The potential of Big Data Analytics has grown manifold; encouraged by advent of new tools like Hadoop that processes huge data streams from multiple sources at reduced costs. 

Preparations require enterprises to extract relevant data from multiple data centers, normalize it, and discard bad results to generate  useful insights.

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