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How Does Big Data Testing Impact an Organization?

We cannot deny the fact that data is the gold stock for an organization without which its sustenance is dim. Big Data, known as extremely large sets of data acquired from multiple sources is beneficial for extracting valuable information. This is the reason why every organization is focused to implement the right techniques for the collection, storage, assessment and testing of Big Data.

What is the importance of Big Data testing? It primarily helps to eliminate data complexities through the confirmation of the quality and integrity of your data. Here are three essential pointers that support the vital nature of big data testing in today’s highly competitive world.  

  • High-quality assessment of data is listed as one of the topmost preferences for business organizations. It has become imperative to utilize big data by large organizations to acquire tested and affirmed insights.  
  • For instance, if big data does not undergo a quality test, it might become difficult to provide useful information that can support an organization with decision-making tasks.
  • Even though numerous enterprises organize comprehensive Big Data analysis, there are cases when they fail to obtain the desired result. Under such circumstances, the only smart way to ensure the correct processing and assessment of data is through the implementation of Big Data testing.

Applicability of Big Data Testing for an Organization

Processing of big data can be tricky and does require a structured plan which shouldn’t limit itself to just data validation but also lay emphasis on system testing and other aspects. To overcome this issue and distribute efforts evenly across segments, there are different methods to operate on big data.

Testing Scheme: Frame better testing actions that automate the procedure concerned with the collection and examination of valid data. It is essential to make sure that the test automation scheme runs parallel to the stated business targets.

Functional Testing: While reviewing every aspect of Big Data, functional testing is required to authorize the outcomes. It becomes crucial to perform and validate at each stage with a purpose to eradicate defects and fulfill customer expectations.

Performance Testing: Assessing the performance to measure the speed, stability, and adaptability concerning different data sets. This action involves the analysis of large data under a short period. To be specific, the goal is to highlight defects and remove the blockers which are known to hamper performance.

Benefits of Enterprise-Level Big Data Testing

Apart from the above-mentioned assistance, Big Data testing does prove advantageous in several other ways. Here are 5 such profitable facets of Big Data testing impacting your enterprise:

Slashed Downtime

It is a fact that several applications rely on data for active operations. As for bad data, it tends to hamper the performance and effectiveness of the application. There are cases when during the collection and distribution of data on applications, organizations aren’t able to analyze data health that results in downtime. A smart solution to this issue is Big Data testing which can help modify data quality and associated application processes that eventually reduce the overall downtime.

Better Market Strategies

Currently, businesses are concentrated on extracting big data benefits to plan their digital marketing schemes. As web technology advances, it has become convenient for businesses to gather huge amounts of data based on history and user activity. This data can be converted into a persuasive, individualized experience for every customer who visits the website. In short, big data testing can assist businesses in setting up optimization targeting to make improved decisions.

Enhanced Data Security

For enterprises involved with client applications, it is vital to maintain data confidentiality to sustain the level of trust established by their clients. In case of a breach in data security, the brand name falls in the risk zone. Therefore, it is suggested to conduct big data testing at all levels to avert the chances of data security failure.

Big Data Testing & Security Concerns (Image Source)

ROI Improvement

When it comes to enterprises, it is necessary to get competitive while structuring strategies for Predictive Analytics and Big Data. The addition of testing as a compulsory activity right before any analysis or processing ensures that the organization is dealing with the correct data to expect better outputs. Such an execution is a fine example of uplifting the ROI and take a significant lead over competitors.

Controlled Consistency

The use of different applications by enterprises is a routine act for dealing with diverse data sets which can cause data inconsistencies. It is surely a big disgrace to organizations when the acquired output through Predictive Analytics and Big Data does not show consistency. It is through Big Data testing that variability in data can be determined beforehand to permit suitable actions for discarding the uncertainty.  

This write-up gives a brief outlook as to why Big Data testing is staged upfront for managing Big Data systems. Inappropriate testing of Big Data systems directly affects business, which is something that needs to be addressed first hand. The incorporation of Big Data Testing is basically a correction method that ultimately avoids the wastage of resources.

Big Data testing and its effective impact to an organization can be further understood through expert guidance at ImpactQA. The team of testers and developers are always ready to resolve your doubts associated with testing Big Data for diverse processes.

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Top 5 Software Testing Trends to Look Out in 2020

  1. Growing Use of Technology like Machine Learning and Artificial Intelligence
  2. Digital Transformation
  3. Transition to the Cloud and Increasing Adoption of IoT
  4. Performance Testing to Performance Engineering
  5. Big Data Testing

With the acceptance of digital technologies, software testing has taken a new spin. Organizations are increasingly considering approaches like Agile and DevOps, which encourage collaboration between testing teams and development teams. According to Gartner, by 2020, the costs of developing the IT industry will grow from 3.76 to 3.87 trillion dollars. Thus, IT plays a crucial role in our lives.

The global software testing market is also expected to grow at 14% CAGR (Compound Annual Growth Rate) by 2023. With the improvement and progress in the software methodologies and widespread acceptance of DevOps and Agile, software testing has evolved into a robust practice.

Considering the current trends in technology and testing, we have projected top 5 software testing trends to look out for 2020:

1. Exponential Use of Technology like Machine Learning and Artificial Intelligence
Machine learning (ML) is the next exponential tech trend, and it is knocking on the front door. It has already led a revolution in the development sector and use of applications. Certainly, the market for machine learning is projected to grow from $1.41 billion to $8.81 billion by 2022. There will be more testing solutions for recurring tasks.

Major tasks performed by the adoption of Machine Learning:

• Optimizing the Test Suite – unique code checks

• Forecast – Prediction of the main test configurations

• Log Analytics – Identification of checks that can (or could) be executed automatically

• Defect analytics – Discovering high-risk application points for prioritizing regression tests

The market scenario for artificial intelligence is also growing faster. It is anticipated that by 2025 it will attain 190.6 billion dollars. This suggests that the IT business is progressively moving to machine learning.

Influence of AI on testing optimization. Source of the image

Artificial Intelligence shows that the evolution of software testing is entering a new phase, the benefits of which have yet to be learned.

2. Digital Transformation
 According to the World Economic Forum, the value of digital transformation for both society and industry could reach $100 trillion by 2025. Enterprises continue to undergo intense digital transformation and become more dependent on new-edge technologies. This sets high requirements to the stability and reliability of software. Hence, the significance of software quality assurance increases as well.

The share of IT budgets spent on Quality Assurance has full-fledged improved by 35% since 2015 and is predicted to face another increase by 2020. At the same time, digital transformation presupposes experimenting with digital features, which calls for the agility and flexibility of the development and QA processes. Therefore, quality assurance faces contradicting demands for flexibility and stability, which sets a challenge for QA managers. They continue to search for non-trivial means of setting QA processes in a way that allows meeting both requirements. Know Top 10 Software Testing Companies in USA.

3. Transition to the Cloud and Increasing Adoption of IoT
With great stability, a growing number of enterprises move data storage and processing to the Cloud. According to the current research by Sogeti, 75-76% of all apps are cloud-based. At the same time, organizations are progressively adopting the Internet of Things (IoT) as this technology gives access to previously unavailable customer data and allows enterprises to make informed business decisions on the basis of data. The same study by Sogeti reports that 95-97% of the examined enterprises have implemented IoT solutions in any way.

Quality assurance for IoT and cloud-based apps calls for extreme specialized skills of QA team and QA engineers to better understand these apps’ implications on company’s business procedures.

The IoT creates a new height in systematic software testing. The following kinds of checks will be conducted in the IoT zone:

  • Scalability Testing
  • Testing the Compatibility of Device Versions
  • Monitoring Connection Delay
  • Safety Analysis (Device Authenticity, Availability and Accuracy of Authorization)
  • Data Integrity Evaluation

Despite the ever growing role of the IoT, 34% of the World Quality Report revealed that their products have Internet of Things functionality, but they still do not have a testing approach.

4. Performance Testing to Performance Engineering
Whether it is SMEs or big giants Performance Testing will continue to bring results and guarantee robustness. By 2020, performance engineering is gradually expected to replace performance testing methods. Performance Engineering evaluates customer experience, which results in commercial viability. Furthermore, it is significant to gauge the product performance to build robustness even in a crisis condition. Dates back the task of software testing was to assure product performance, however, now it is not sufficient to pay attention only to this element. Additionally, it is significant to focus on other elements like customer value, convenience and practicality of use, and configuration quality.

5. Big Data Testing
The trend of big data testing has been fueled crucially because of the robust procedures that various enterprises are following to churn out the best of marketing strategies. Acknowledging this fact, we must claim that the need for testing big data apps will witness a new height in 2020. In Big Data testing, software testers have to verify that terabytes of data are processed effectively using commodity cluster and other supportive elements. This kind of testing focuses on functional testing and performance testing. The quality of data is also a crucial factor in big data testing. The data quality is checked on the basis of distinct traits like consistency, validity, conformity, accuracy, data completeness, duplication, etc.

In addition, manual testing is gradually being replaced by automated testing. In 2020 we will see a hybrid of these two types, since there are still not sufficient tools to completely automate data processing and monitoring. But in subsequent years, it will be possible to see an almost complete replacement of manual testing with automatic.

Software testing companies will evolve into new dimensions by 2020 and the QA tester will have to grow to fill in larger shoes in future. The software tester will have to compete with a varied IT landscape, filled with new-edge technologies that demand automated, continuous integration among other traits, rooted deeply in the basics of software testing.

 

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