10 Reasons Why You Shouldn’t Ignore Big Data & Analytics Testing

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Big data is a term coined to describe the exponential growth and widespread availability of data over the last few decades, in both structured and unstructured forms. It is also a broad term for extremely large and complex data sets where data processing applications are rendered inadequate. It often refers to the use of predictive analytics or other advanced methods to extract information from data from across multiple data sets.

The growth of Big Data has been phenomenal, but it has also led to the rise of bad data. Almost every enterprise uses Business Intelligence to make strategic decisions to ensure it has a competitive edge in the market. But if they generate data that is qualitatively sub-par it is only going to impede their growth.

So here are ten reasons why you shouldn’t ignore Big Data and Analytics testing:

  1. Improve efficiency with live data integration testing: Big Data applications today are required to capture live data for analysis in real time. The data needs to be sanitized since it needs to be reliable and clean. Furthermore, the data also comes from many different channels. Therefore it becomes quite complex. Data quality testing should be mandatory right from the source to the final destination for efficient analysis.
  2. Big Data can unearth hidden values by introducing transparency: A lot of information continues to exist in non-digital form [for example: information on paper], and therefore isn’t easily accessible, and cannot be retrieved immediately via networks. As this data is injected into the system over time, it is important to ensure that the quality of information doesn’t deteriorate during the digitization process.
  3. Reduce downtime by testing deployment instantly: Since a majority of Big Data applications are developed for predictive analytics, which require instant data collection and deployment, their results play a significant role in business decisions. Therefore, testing needs to be done comprehensively to avoid glitches during deployment.
  4. Big Data helps create more efficient work environments: Enterprises today have almost all information stored in digital from, ranging from employee performance to product inventories. Using Big Data, they can extract information to help determine variability and significantly raise the bar for performance.
  5. Ensure scalability: When dealing with Big Data, one inherently assumes the involvement of enormous volumes. Needless to say, the role of testing scalability becomes significantly more important in the general testing process. To be able to efficiently support this task, the architecture of the application must be tested using smart data samples while also being able to scale up without hindering the performance.
  6. Big Data is the future: The possibilities introduced to organizations because of Big Data are almost limitless. Information gathered from new places could help develop technologies and systems that shape our future. For example, a manufacturer could use data acquired from certain sensors to be able to service the needs of its clients even further.
  7. Be secure: Security is always an important aspect of any large scale operation. Big Data is derived from a varying set of sources, some of which are confidential. Therefore, the issue of security becomes high-priority. With hacking threats and attempts on the rise, ensuring data security and personal privacy of users is achieved by using different testing methods at various layers of the application.
  8. Have the best performance: Big Data applications work on live data for real time analytics. So performance is very important. Performance testing is supposed to be run alongside the other types of testing, including live integration and scalability testing.
  9. Raise the bar with quality: Checking the quality of data is an important functional aspect of Big Data testing. Once the various characteristics of the data, such as accuracy, duplicity, conformity, validity, consistency, etc. are known, an organization can then proceed to create better products and protocols based on a strong foundation of quality data.
  10. Stay ahead of the curve: With Big Data testing one can ensure that the organization stays competitive with improved decision-making, minimization of risks, and discovering new perspectives.

*Today most organizations have to deal with tight deadlines and ever-increasing demands from an informed market. Cigniti can be of great help to your delivery schedules, with QA experts who can take care of your automated testing needs.

To know more about how Cigniti can help you flourish with big data and analytics testing, please get in touch with us at contact@cigniti.com.