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Validation cannot be wished away till Magic takes over Big Data Analytics | Infosys

Author: Naju D. Mohan, Delivery Manager, Data Services, Independent Validation Solutions

It has dawned on us that big data is indeed a big deal. Lately, its growth has accelerated due to the digitalization of almost everything around us, whether it is organization, industry or economy. It seems evident that the future is going to be governed by big data. But don't be too enthusiastic to jump onto the big data bandwagon and start collecting data for the sake of it. We are sure it will end up in a huge digital graveyard.

All of us are emitting digital data every second, from birth to death, and this data can be stored, analyzed and processed for value extraction. It has a big impact across all industries and influences each one of us in more ways than we can ever imagine. Big data helps to drive productivity across acres of farmland used in agriculture, uses patient data diligently in healthcare to improve quality of life, improves personalized experience through customer analytics, leads to smarter law enforcement and extends support to nobler causes like prevention of human trafficking.

Every organization must strive to derive value from data to run their businesses effectively. This requires a clear understanding of an organization's objectives, a clear goal and a well thought-out plan about utilizing this data to achieve its objectives. Unlike wine, data becomes stale with age. This makes it imperative for organizations to have tools, processes and appropriately skilled people to turn this data into actionable insights before it turns obsolete. Even though it is common knowledge that machine learning algorithms and artificial intelligence programs are automating more and more steps in big data analytics, humans are required at every stage to take decisions about how the data has to be stored, what has to be transformed, and how it has to be visualized.

To err is human, so please don't forget to validate 

In my opinion, validation adds the famous 4th V, which is 'veracity' to big data. Big data would have remained the troublesome '3Vs: volume, velocity and variety,' if there was no holistic validation strategy, an automated validation approach and end-to-end test data governance. 


Figure 1: Importance of Validation

Big data validation isn't a crystal stair

Big data validation isn't a smooth and easy phase in the big data life cycle. Rather, it comes with its own unique challenges. The big data technology landscape is complex and continuously evolving. It has to be capable of supporting various types of data like online transaction processing, package application data, analytical, sensor data and others. The testers need to be on top of technology changes and come up with means to access data, and convert it into a format which can be verified and reported. Replicating a QA environment for big data implementation, which supports diverse data integration across systems, is a challenge that needs to be overcome by adopting newer technologies like virtualization and cloud. There is no proper testing tool available in the market which covers the entire landscape of big data implementations. This increases the burden on the tester to ensure proper test coverage, reduce test cycles through automation and prevent defect leakage.

You needn't be afraid of big data chaos, if testers have mastered the right skills

Bad analysis and poor decisions, due to data quality issues, have serious consequences for the business. The testers need to become adept at extreme scripting to write Pig Latin, Shell Script and Java utilities to tame big data and get valuable insights. They need to develop analytical skills to validate the patterns used in data science use cases, for data manipulations in the big data system. Selection of appropriate test scenarios to stop testing at the right time requires a deep domain understanding and clear appreciation of the business objectives of the client. In short, the testers have to believe in themselves and acquire these new skills to provide quality big data which drives the modern world.


Most organizations accept the fact that data quality is a major barrier for going ahead with their big data programs. They don't have enough confidence that the data mined from their huge stores of raw data would provide the right meaning for their business growth. Inaccurate data is definitely going to hurt business and hence, it is the right time to equip our testing organization, to tide over the risks associated with big data implementations. Given the fact that technology alone is not going to help us realize our investments in big data, we need people with specialized skills, who can validate the information derived from the ocean of raw data and act as our companion in our journey towards richer, real-time and respectful insights. 

"In God we trust. All others must bring data." - W. Edwards Deming

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