Why Most of the Big Data Initiatives in Organizations Fail?

A huge amount of data is floating around us; information related to business, our 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 interchangeably used 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. it has a narrow scope of providing support to data analytics.

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. 

Lack of skilled analysts along with the tough challenge of discarding bad insights, is still a nightmare for CXOs and CIOs. 

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. 

4. Lack of strategy:  

Isolated big data projects do not work at all. Most importantly, one must approach big data analytics with a well-defined strategy.  The problem arises if there is disagreement in the usage of data insights. Once CIOs collect all data, they must inspect it from different perspectives and then make the best possible use of relevant insights. Understand its origin, validation process, and challenge the bad data or discard it entirely.

5. Asking wrong questions:

Data scientists have a deep understanding of domain knowledge; ideally a data scientist is a subject matter expert of the particular industry vertical. Once you combine such expert domain knowledge with statistics expertise, foolproof mathematical calculations, and excellent programming skills, you are bound to get excellent data insights. 

6. Improper Monitoring of Data Analytics: 

Data-Insight-Action requires continuous improvement of business processes that can be achieved by regular monitoring of operational activities and analysis of data insights timely.

How to Overcome Failure of Big Data Analytics

Developing a comprehensive Big Data Analytics plan is the first step towards the goal of harnessing the power of big data. An ideal strategic plan must highlight your critical objectives, tradeoffs and must list all your priorities; for instance, which business will receive maximum capital, emphasis on high profit margins or quick growth, and capabilities required for strong growth and performance.

Flexible, open-data infrastructure will help employees modify their approach until you achieve success. Eliminate fear and iterate towards using big data analytics effectively. Data center capacity, computational intensity, and storage are important factors to consider when choosing cloud-based servers.  

This article is featured in CIO Review, August 2016 edition