5 Big Data Testing Challenges You Should Know About

5 Big Data Testing Challenges You Should Know About

Enterprise data will grow 650% in the next five years. Also, through 2015, 85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage. – Gartner

Data is the lifeline of an organization and is getting bigger with each day. In 2011, experts predicted that Big Data will become “the next frontier of competition, innovation and productivity”.   Today, businesses face data challenges in terms of volume, variety and sources. Structured business data is supplemented with unstructured data, and semi-structured data from social media and other third parties. Finding essential data from such a large volume of data is becoming a real challenge for businesses, and quality analysis is the only option.

There are various business advantages of Big Data mining, but separation of required data from junk is not easy. The QA team has to overcome various challenges during testing of such Big Data. Some of them are:

Huge Volume and Heterogeneity

Testing a huge volume of data is the biggest challenge in itself. A decade ago, a data pool of 10 million records was considered gigantic. Today, businesses have to store Petabyte or Exabyte data, extracted from various online and offline sources, to conduct their daily business. Testers are required to audit such voluminous data to ensure that they are a fit for business purposes. How can you store and prepare test cases for such large data that is not consistent? Full-volume testing is impossible due to such a huge data size.

Understanding the Data

For the Big Data testing strategy to be effective, testers need to continuously monitor and validate the 4Vs (basic characteristics) of Data – Volume, Variety, Velocity and Value. Understanding the data and its impact on the business is the real challenge faced by any Big Data tester. It is not easy to measure the testing efforts and strategy without proper knowledge of the nature of available data. Testers need to understand business rules and the relationship between different subsets of data. They also have to understand statistical correlation between different data sets and their benefits for business users.

Dealing with Sentiments and Emotions

In a big-data system, unstructured data drawn from sources such as tweets, text documents and social media posts supplement a data feed. The biggest challenge faced by testers while dealing with unstructured data is the sentiment attached to it. For example, consumers tweet and discuss about a new product launched in the market. Testers need to capture their sentiments and transform them into insights for decision making and further business analysis.

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Lack of Technical Expertise and Coordination

Technology is growing, and everyone is struggling to understand the algorithm of processing Big Data. Big Data testers need to understand the components of the Big Data ecosystem thoroughly. Today, testers understand that they have to think beyond the regular parameters of automated testing and manual testing. Big Data, with its unexpected format, can cause problems that automated test cases fail to understand. Creating automated test cases for such a Big Data pool requires expertise and coordination between team members. The testing team should coordinate with the development team and marketing team to understand data extraction from different resources, data filtering and pre and post processing algorithms. As there are a number of fully automated testing tools available in the market for Big Data validation, the tester has to possess the required skill-set inevitably and leverage Big Data technologies like Hadoop. It calls for a remarkable mindset shift for both testing teams within organizations as well as testers. Also, organizations need to be ready to invest in Big Data-specific training programs and to develop the Big Data test automation solutions.

Stretched Deadlines & Costs

If the testing process is not standardized and strengthened for re-utilization and optimization of test case sets, the test cycle / test suite would go beyond the intended and in turn causes increased costs, maintenance issues and delivery slippages. Test cycles might stretch into weeks or even longer in manual testing. Hence, test cycles need to be accelerated with the adoption of validation tools, proper infrastructure and data processing methodologies.

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These are just some of the challenges that testers face while dealing with the QA of a vast data pool. To know more about how Big Data testing can be managed efficiently, call the Big Data testing team at Gallop.

All in all, Big Data testing has much prominence for today’s businesses. If right test strategies are embraced and best practices are followed, defects can be identified in early stages and overall testing costs can be reduced while achieving high Big Data quality at speed.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.