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.<\/p>\n
[Tweet “Gartner reports that an average organization loses $8.2 million every year due to poor data quality”]<\/strong><\/p>\n Gartner reports that an average organization loses $8.2 million every year due to poor data quality,<\/strong> 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 <\/strong>as well as the importance of a quality analysis mechanism to convert it into an asset for the company.<\/p>\n 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 \u2013 Big Ideas, Big Teams and Big Deals<\/strong>.<\/p>\n [Tweet “With quality analysis becoming the biggest priority of business organizations, big data testing is becoming a serious issue with each passing day. “]<\/p>\nBig Ideas<\/u><\/strong><\/h3>\n