Once portrayed as ‘the next frontier of competition, innovation and productivity’, Big Data testing is now the new name of challenge for any business dealing with the huge information bank in today’s dynamic world. For any business, data has been the fuel that drives their journey towards success.
Like a coin has two sides, large volume of data brings with it numerous challenges which require smart strategies to extract the essential data. Finding such quality information for business demands a special and dedicated team that analyses, evaluates and extracts the required or relevant information from the pool of data collected from social media and other third parties.
Since the challenges that come along this pool of big data are multifold, a team that understands them in depth is a must for any business. A bigger quality analysis (QA) team for big data testing is necessary due to the following reasons & challenges:
Gigantic Data Volume and a mix of diversified information
The first challenge the QA team comes across in big data is the volume. While today’s business requirements demand huge amount of data for analytics to obtain valuable information from them, it’s a mammoth task to perform that is not easy to attain or achieve with a smaller team. Apart from managing this volume, what happens when the data is diverse in all aspects? Well, that’s where the even bigger challenge comes on for the QA team when they deal with big data. And, testing the same, understanding its inconsistency and then picking what is relevant for the business, is next to impossible that a QA team makes possible. A bigger and dedicated team for testing is required to audit such voluminous data on a regular basis in order to meet the daily necessities of the business.
What means what?
Absolutely! The data that is not analyzed and monitored well becomes the next big challenge for any business. Effective strategies, continuous monitoring and evaluation of data are crucial to obtain information that is of great value for the company. The popular characteristics, i.e. 4Vs – Volume, Variety, Velocity and Value are of utmost importance while testing data. Understanding the data requires proper knowledge about the nature of data available which is why there is a need of a dedicated team for data testing. Loss of critical information during data testing can impact the business. Every subset of data involves the testers to understand its relationship with the other subsets and also what applies to the business rules. Statistical correlation between various subsets of data and their benefits can be a tedious task for the testers.
What is of value?
For any business, it is only the percentage of huge magnitude of data which is of value and contributes to its growth and success story. The veracity of data is an extremely important aspect to retain and accomplish during testing. This is where a bigger data testing team comes to the rescue that sort out the unnecessary elements from the data.
Does feedback count?
While collecting data, the type of information received varies and it could also have feedback from people, users/consumers who are directly or indirectly connected to the field of work or industry a business may function into. Extracting feedback is also equally important as other pieces of valuable data as it provides the insights for high level analysis of the business. Data testing functions as a support that any business requires to convert potential customers into brand ambassadors. This can only be achieved through analyzing the feedback and taking corrective measures towards the insights withdrawn from the feedback.
Appetite for Technical Expertise
Data testing process involves the use of technology and technical expertise of testers to adapt the same and simplify the data sets. At present, testers are well verse with the ecosystem of big data analysis and are stepping beyond the conventional automated testing and manual testing methods. Coordination among team members of development team and marketing team is a must to create test cases for the unstructured data in order to extract valuable components of the data.
Hence, apart from resolving the above given issues and challenges, having a bigger team for data testing provides the business with answers to more business questions through extensive analysis and faster deliveries of actionable results for business growth.
We believe, right set of strategies for identification of important data, adapting best practicing and evaluating crucial information through a dedicated team of testers can reduce the overall costs without compromising on the quality of data and delivery of analytics on time.