How to address the challenges associated with Big Data Testing

The saying ‘data is the new oil’ exemplifies the critical role data plays in any organization. It helps to understand the market, plan new strategies, and predict customer behavior, among others. In short, data can redefine competitiveness, innovation, and productivity. Even statistics have shown its importance by suggesting the expected global annual revenue to touch 274.3 USD (Statista.) However, structured and authentic data is not always easy to get and may be supplemented with unstructured or semi-structured data sourced from social media, vendors, customers, or other places. Sifting and parsing the required data from such a large volume of data have occupied data analysts and prove to be a challenge for businesses. The answer comes in the form of big data testing. Let us first understand the challenges in testing big data applications.

Challenges to any big data testing approach

Notwithstanding the advantages of data mining, there are several challenges in arriving at the right kind of data from the supposed ‘junk’ data.

High volume: Today, any CRM or ERP software suite receives humongous volume of data from various online and offline sources. Testing such data is essential to check whether they hold any business value or not. The sheer size of such data makes it difficult to store, let alone prepare manual or automated test cases.

Heterogeneity of data: Arriving at any business decision depends on processing the right configuration of data. However, big data can come in various forms and from different sources. The job of testers is to separate the data sets and find out their relevance for the business. This is easier said than done as the data in image, voice, or text form would require different approaches to sift, test, and analyze.

Monitor and validate data: Testers need to validate big data on 5 characteristics or 5Vs, namely, Volume, Velocity, Value, Variety, and Veracity. However, this requires a proper understanding of data, business rules, the relation between various datasets, and their benefits for business.

Time and cost factor: Should the big data testing process is not standardized, the outcome may stretch beyond the turnaround time and increase costs. Further, there may be delivery slippages and maintenance issues, which can be addressed by accelerating test cycles and adopting proper test tools, methodologies, and big data test automation.

Lack of expertise: Big data may not always lend itself to testing by creating automated test cases. Its heterogeneity, format, size, and unstructured nature may cause big data test automation to fail. To overcome the challenge, there should be proper coordination among team members and expertise in the test team to execute the test. The test team should understand the process of data extraction from various sources, data filtering, and algorithms related to big data processing. At the same time, the lack of expertise among testers in handling big data and analytics testing can create bottlenecks for enterprises in developing automation solutions.

Identifying customer sentiments: Any big data framework consisting of unstructured and semi-structured data may have customer sentiments or emotions attached. QA testers need to understand these sentiments and derive suitable insights for better analysis and decision making.

Strategy to address big data testing challenges

The quality assurance specialists should be adept at dealing with data processes, layouts, and loads. Besides, since big data receives data from various sources and travels fast across the world, its security should be ensured at all cost. The right strategy to address the challenges associated with testing big data applications is given below:

• Avoid the sampling approach given its risky nature and plan load coverage at the beginning. Thereafter, automation tools must be deployed to access data across layers.
• QA specialists can learn to derive patterns from aggregate data and drill-down charts.
• Any change requirement must be implemented in time based on collaboration with all stakeholders.
• There must be a centralized control over big data given the risk of unauthorized access and data theft.
• Privileged accounts can create insider threats and so, their access should be subjected to specific commands and actions instead of admin having total access.
• Testers must check end-to-end encryption and hashing passwords to ensure security issues from NoSQL injection.
• Test the data repository to detect any unauthorized file modifications by threat actors.

Conclusion

Big data, in the digital ecosystem, has the capability to transform the way we function. To ensure enterprises derive the right insights from big data and make the right decisions, testers choosing a big data testing approach should apply the best practices. It is only by incorporating the right strategy that defects in the big data structure can be identified and remediated.