Enterprise data will grow 650% within the next five years. Also, through 2015, 85% of Fortune 500 companies will be unable to exploit Big Data for competitive advantage. – Gartner
Data is the lifeline of an business and is getting bigger with every day. In 2011, experts predicted that will Big Data will become âthe next frontier of competition, innovation and productivityâ. Today, businesses face data challenges with regards to volume, variety and sources. Structured business data is supplemented along with unstructured data, and semi-structured information from social media and other third events. Finding essential data from this kind of large volume of data is becoming a genuine challenge for businesses, and high quality analysis is the only option.
There are various business advantages of Big Data mining, but separation associated with required data from junk is not really easy. The QA team needs to overcome various challenges during tests of such Big Data. Some of them are:
Huge Volume plus Heterogeneity
Testing a huge volume of information is the biggest challenge in itself. About ten years ago, a data pool of 10 million records was considered enormous. Today, businesses have to store Petabyte or Exabyte data, extracted through various online and offline sources, in order to conduct their daily business. Testers are required to audit such voluminous information to ensure that they are a fit regarding business purposes. How can you shop and prepare test cases regarding such large data that is not constant, Full-volume testing is impossible because of such a huge data size.
Understanding the Data
For the Big Data testing strategy to be effective, testers need to continuously monitor and confirm the 4Vs (basic characteristics) associated with Data – Volume, Variety, Velocity and Value. Understanding the data as well as its impact on the business is the real problem faced by any Big Data tester. It is not easy to gauge the testing efforts and strategy without correct knowledge of the nature of available information. Testers need to understand business guidelines and the relationship between different subsets of data. They also have to realize statistical correlation between different information sets and their benefits regarding business users.
Dealing with Sentiments and Emotions
In a big-data system, unstructured data drawn through sources such as tweets, text paperwork and social media posts supplement the data feed. The biggest problem faced by testers while coping with unstructured data is the sentiment attached with it. For example, consumers twitter update and discuss about a new product released in the market. Testers need to capture their own sentiments and transform them in to insights for decision making and further company analysis.
Lack of Technical Expertise and Coordination
Technology is growing, plus everyone is struggling to understand the formula of processing Big Data. Big Data testers need to understand the aspects of the Big Data ecosystem completely. Today, testers understand that they have to believe beyond the regular parameters of automatic testing and manual testing. Big Data, with its unexpected format, may cause problems that automated test cases are not able to understand. Creating automated test situations for such a Big Data swimming pool requires expertise and coordination in between team members. The testing team need to coordinate with the development team plus marketing team to understand data removal from different resources, data blocking and pre and post control algorithms. As there are a number of completely automated testing tools available in the market regarding Big Data validation, the specialist has to possess the required skill-set certainly and leverage Big Data systems like Hadoop. It calls for an amazing mindset shift for both tests teams within organizations as well as testers. Also, organizations need to be ready to spend money on Big Data-specific training programs and also to develop the Big Data check automation solutions.
Stretched Deadlines & Costs
If the testing process is not really standardized and strengthened for re-utilization and optimization of test situation sets, the test cycle / check suite would go beyond the designed and in turn causes increased costs, upkeep issues and delivery slippages. Test cycles might stretch into days or even longer in manual tests. Hence, test cycles need to be sped up with the adoption of validation equipment, proper infrastructure and data control methodologies.
These are just some of the difficulties that testers face while coping with the QA of a vast information pool. To know more about how Big Data testing can be managed effectively, call the Big Data tests team at Cigniti.
All in most, Big Data testing has very much prominence for todayâs businesses. If right test strategies are accepted and best practices are followed, problems can be identified in early stages plus overall testing costs can be decreased while achieving high Big Data quality at spee