Big Data Testing : What Should You Know About it ?

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Data is a familiar term, but what about big data? As you know, data is the lifeline of any organization and without it, competition cannot be survived. With data expansion and explosion happening at faster rates than ever, most organizations face challenges in converting big data as an asset for their firm.

Gartner reports that an average organization loses $8.2 million every year due to poor data quality, and sometimes losses are as high as $100 million. The Experian Data Quality report suggests that 99% of the organizations practice unique data quality strategies, but they fail to find bad data from their large volume of semi-structured, unstructured and structured data. The McKinsey Company report emphasizes the role of next generation data integration platforms in ensuring only relevant data is identified as well as the importance of a quality analysis mechanism to convert it into an asset for the company.

With quality analysis becoming the biggest priority of business organizations, big data testing is becoming a serious issue with each passing day. Since big data testing is a new domain, it demands specialized testing beyond manual testing. Nonetheless, with the phenomenal growth of big data, testers should keep themselves up to date with current technology and acquire knowledge on how to uncover bad data. Success is not, however, going to be easier to achieve with big data testing, unless the testing team understands three important aspects of big data – Big Ideas, Big Teams and Big Deals.

Big Ideas

Big data testing is all about implementing big ideas.

Big ideas are all about optimizing the application or website to build a better business. Everyone is aware of website optimization and landing-page optimization programs, which may be partially successful, but they are not enough to bring business. This is why businesses are conducting split-tests to analyze the work behavior of each function in the web page. Big ideas are more about learning small optimizations to make their webpages more demanding or rather more saleable.

Big Teams

For years, the tester was the sole person authorized to test a website. Today, big testing empowers many people in the team to test different components of a website. They are encouraged to suggest new test ideas. With big data imposing huge challenges, it is becoming increasingly necessary for organizations to enlarge the team size of testers to cover all important aspects of testing. According to Hal Valrian, Chief Economist at a prominent search engine company, the search engine pioneers 10,000 experiments every year and a large number of people are simultaneously working on these tests. In this kind of an organization, wide testing helps strengthen the term “learning through experimentation.”

Big Deal

Big deal is about appreciating the culture of experimentation. It is very important that top level management starts appreciating testers and testing for their efforts. It should be understood that not every test is successful, but if the intention and method of execution is perfect, the tester deserves an appreciation. They should be encouraged such that the teams feel good about testing big ideas.

The above mentioned tips are a few pre-requisites to conduct successful big data testing. There are many other challenges lurking there, however. Stay tuned to our future blogs to understand them further.

To know more about how you can convert big data into a business asset, feel free to contact the Cigniti big data testing experts.