How Digital Assurance is Different from Traditional QA

How Digital Assurance is Different from Traditional QA

Today’s digital economy is transforming the way in which businesses are run. This is also causing a major shift in the way quality assurance (QA) service is provided. Businesses depend mainly on reliability, quality, and digital quality assurance for fulfilling the market demands before their competitors do so, without compromising on the Customer experience in order to achieve a successful digital transformation. Because of this, the demand for assuring a flawless performance of systems with regards to User Experience Testing and Security Testing has reached a peak in this digital world.

Digital assurance is confined not only to testing applications across the Social, Mobile, Analytics & Cloud (SMAC), BigData, Internet of Things (IoT), etc. but there is also a need to assure that the desired business outcome is achieved as a result of the digital transformation initiatives adopted. While QA must support all the digital initiatives, the digital assurance solutions need to go beyond the functional validations needed for SMAC and should encompass the various aspects into them like network capability, interoperability, optimal performance and enhanced security. This will shift the focus from traditional QA (testing) towards assuring a better customer experience and integrated testing of various embedded software, digital devices, and big data

The following changes need to be adopted in the world of Digital Assurance as against the traditional QA approach:

1. QA should act as the guardian for customer experience and brand: Organizations need to focus more on creating a consistent and seamless experience apart from just satisfying the customers’ demands for professional services provided to them as they use multiple touch points for performing the different transactions while availing any services. So, from the perspective of digital assurance, measuring user experience across different digital channels like web, mobile tablets, etc. has become paramount. Organizations thus need to prepare automated solutions that have better accessibility, offer seamless experiences, are consistent, and have less user wait times.

2. Adopting DevOps Model to meet delivery agility: To keep up with the advancements and fast paced growth of the present IT world, organizations are increasingly adopting the DevOps model to accelerate digital transformation as it leads to a well-organized and collaborative IT life-cycle methodology spanning across entities like Business, Development, QA, and Operations. Because of adoption of Continuous delivery, changes to applications are being made more frequently and are smaller in size to reduce service disruption. In the DevOps model, QA teams collaborate with the development teams for continuous integration and also participate in the Operations cycle for deployment validation and monitoring activities in order to engage with the business to support the activities related to business assurance. Hence, in the digital world, the QA activities must shift in all the possible directions in order to assure agility.

3. Need for QA to shift from application-level to life-cycle automation: The invention and adoption of modern technologies such as SMAC and IoT have shifted the focus from traditional QA (such as function and regression testing) to the entire life cycle of the application development comprising of cross-platform compatibility, customer-experience, and network testing. There is a need to adopt Lab-based automation as multiple products are in use and todays dynamic software involves a lot of permutations and combinations for testing them. It can be better materialized by adopting Simulators or physical devices that make use of popular commercial or open-source tools to perform automation. There is also a rising need to adopt Script less automation using business friendly navigation flows and various keyword abstraction tests to encourage the involvement of business stakeholders in testing.

Organizations also need to facilitate daily execution of the automated scripts in agile and CI environments. Unconventional automation methodologies like Artificial Intelligence (AI) and Autonomics may be adopted in order to obtain continuous and frictionless QA across the testing life cycle.

4. Assurance of data quality in Big data: The decoding process for big data needs next-generation data-integration platforms in order to ensure that all the data gathered is relevant, and only then should it be used for further analysis. There is a need to build QA frameworks across data integrity, its privacy and security in order to keep pace while fulfilling these requirements. The frameworks should also be compatible with open-source platforms such as Hadoop.

5. Using advanced analytics for decision support: Advanced analytics is needed to mine data from different data sources including social-media platforms like Twitter and Facebook. Traditionally the QA organizations depended mainly on production defects and reports from satisfaction surveys for customer feedback, but the present day Digital systems allow QA teams to create actionable intelligence through customer experiences which can be used for predictive and prescriptive decision making in testing and delivering digital assurance and provide a better quality insight.

To hear more about further differences between traditional QA and Digital assurance, attend a Webinar on ‘Accelerating Digital Transformation Journey with Digital Assurance QA’ on 20th July 2016, 11AM EST by Sai Chintala, Senior Vice President, Global Pre-Sales. Reserve your slot here.

gallop-software testerAbout the Author: Abhijeet Srivastava is an Associate Manager at Gallop Solutions. He is a part of Enterprise Solutions Group which primarily helps convert Leads to Deals by devising the best solutions. He holds a B.Tech in Electronics & Communication Engineering from Sikkim Manipal Institute of Technology and PGDM from TAPMI, Manipal. His Core Skills are Business Analysis, Sales pitch, Architecting Solutions, building Proposal, etc.
The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

Moving from Descriptive Metrics to Predictive & Prescriptive Metrics

Descriptive metrics to predictive and prescriptive metrics


With the deluge of data being churned every day in businesses, organisations are turning to analytics solutions to understand what these huge volumes of data mean, and how can they help improve decision making. We need to chart a new course with data, which is to predict.

Every organization that is driven by data wishes to fulfil its promise of doing it right. Reviewing the available analytic options in itself can prove to be a humongous task in itself. Analytics are necessary when data is needed to answer specific business-related questions, whereas through metrics it’s being responsible to a certain action and in order to measure, metrics are formulated from the analytics available.

The analytic options can be categorized at a high level into three distinct types, Descriptive, Predictive, and Prescriptive.

Descriptive Metrics is raw data in summarized form. It uses the techniques of data aggregation and data mining to provide meaningful insight into the past and answer: “What has happened?”.  For example, the no. of Defects, Testers, Iterations and other metrics that show what’s happening in testing department in an easy-to-understand way. Descriptive metric explains a specific aspect of a system. For instance, if we were trying to automatically analyze a web page, we may want to rate the legibility of its text based on the font size being used. To identify the font size being used–a descriptive metric– and report this value, an automated tool may be used. The metric also helps identify the modal font sizes for other websites without claiming any implications of the value. This is in contrast to a predictive metric.

Predictive Metrics use forecasting techniques and statistical models to understand future behaviour by studying patterns and answer: “What could happen in the future?”.  For example, Code Coverage, Defects in future, etc. Predictive metric describes an aspect of a system for providing a prediction or estimate of its usability. For example, if the font size used on a web page is used as a predictive metric (for example, observing that a larger font size is more legible), a designer may assume that as a larger font increases the page ranking, it will also increase the usability of the design. This is in total contrast to Descriptive metrics that do not make any explicit claims as to the implications of the measurement.

Prescriptive Metrics use simulation algorithms and optimization to advise on the possible outcomes and answer: “What should we do?”. For example, efficiency, effectiveness, risk-based testing, and increase code coverage.

Predictive vs. Prescriptive Metrics

Predictive and prescriptive metrics can be drawn from descriptive metrics which give insights and decisions. Effective business strategy can be chalked out with both types of metrics. Predictive metrics by themselves are not enough to beat the competition. It is the Prescriptive analytics that provides intelligent recommendations for the next best steps for almost any business process to achieve the desired outcomes and to increase ROI.

While both types of metrics inform business strategies based on collected data, the major difference between predictive and prescriptive metrics is that while the former forecasts the potential future outcomes, the latter helps you draw up specific recommendations. In fact, Prescriptive metrics uses Predictive metrics for arriving at the different options available along with their anticipated impact on specific key performance indicators.

There’s an interesting example in an article published in Business News Daily. It states “For example, a nationwide sporting goods store might use predictive and prescriptive analytics together. The store’s forecasts indicate that sales of running shoes will increase as warmer weather approaches in the spring, and based on that insight, it might seem logical to ramp up inventory of running shoes at every store. However, in reality, the sales spike likely won’t happen at every store across the country all at once. Instead, it will creep gradually from south to north based on weather patterns.”


It is obvious that for getting the most accurate predictive and prescriptive analytics, the data available needs to be kept constantly updated as per real-time successes and failures. As someone mentioned, “The analytics are only as good as the data that feed them.”

To know more about these burning topics, attend a Webinar on ‘Accelerating Digital Transformation Journey with Digital Assurance QA’ on 20th July 2016, 11AM EST by Sai Chintala, Senior Vice President, Global Pre-Sales. Reserve your slot here.


The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

5 Big Data Testing Challenges You Should Know About

5 Big Data Testing Challenges You Should Know About

Enterprise data will grow 650% in the next five years. Also, through 2015, 85% of Fortune 500 organizations will be unable to exploit Big Data for competitive advantage. – Gartner

Data is the lifeline of an organization and is getting bigger with each day. In 2011, experts predicted that Big Data will become “the next frontier of competition, innovation and productivity”.   Today, businesses face data challenges in terms of volume, variety and sources. Structured business data is supplemented with unstructured data, and semi-structured data from social media and other third parties. Finding essential data from such a large volume of data is becoming a real challenge for businesses, and quality analysis is the only option.

There are various business advantages of Big Data mining, but separation of required data from junk is not easy. The QA team has to overcome various challenges during testing of such Big Data. Some of them are:

Huge Volume and Heterogeneity

Testing a huge volume of data is the biggest challenge in itself. A decade ago, a data pool of 10 million records was considered gigantic. Today, businesses have to store Petabyte or Exabyte data, extracted from various online and offline sources, to conduct their daily business. Testers are required to audit such voluminous data to ensure that they are a fit for business purposes. How can you store and prepare test cases for such large data that is not consistent? Full-volume testing is impossible due to such a huge data size.

Understanding the Data

For the Big Data testing strategy to be effective, testers need to continuously monitor and validate the 4Vs (basic characteristics) of Data – Volume, Variety, Velocity and Value. Understanding the data and its impact on the business is the real challenge faced by any Big Data tester. It is not easy to measure the testing efforts and strategy without proper knowledge of the nature of available data. Testers need to understand business rules and the relationship between different subsets of data. They also have to understand statistical correlation between different data sets and their benefits for business users.

Dealing with Sentiments and Emotions

In a big-data system, unstructured data drawn from sources such as tweets, text documents and social media posts supplement a data feed. The biggest challenge faced by testers while dealing with unstructured data is the sentiment attached to it. For example, consumers tweet and discuss about a new product launched in the market. Testers need to capture their sentiments and transform them into insights for decision making and further business analysis.

Lack of Technical Expertise and Coordination

Technology is growing, and everyone is struggling to understand the algorithm of processing Big Data. Big Data testers need to understand the components of the Big Data ecosystem thoroughly. Today, testers understand that they have to think beyond the regular parameters of automated testing and manual testing. Big Data, with its unexpected format, can cause problems that automated test cases fail to understand. Creating automated test cases for such a Big Data pool requires expertise and coordination between team members. The testing team should coordinate with the development team and marketing team to understand data extraction from different resources, data filtering and pre and post processing algorithms. As there are a number of fully automated testing tools available in the market for Big Data validation, the tester has to possess the required skill-set inevitably and leverage Big Data technologies like Hadoop. It calls for a remarkable mindset shift for both testing teams within organizations as well as testers. Also, organizations need to be ready to invest in Big Data-specific training programs and to develop the Big Data test automation solutions.

Stretched Deadlines & Costs

If the testing process is not standardized and strengthened for re-utilization and optimization of test case sets, the test cycle / test suite would go beyond the intended and in turn causes increased costs, maintenance issues and delivery slippages. Test cycles might stretch into weeks or even longer in manual testing. Hence, test cycles need to be accelerated with the adoption of validation tools, proper infrastructure and data processing methodologies.

These are just some of the challenges that testers face while dealing with the QA of a vast data pool. To know more about how Big Data testing can be managed efficiently, call the Big Data testing team at Gallop.

All in all, Big Data testing has much prominence for today’s businesses. If right test strategies are embraced and best practices are followed, defects can be identified in early stages and overall testing costs can be reduced while achieving high Big Data quality at speed.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.

2 Major Challenges of Big Data Testing

2 Major Challenges of Big Data Testing

We all know that there are umpteen number of challenges when it comes to Testing – lack of resources, lack of time, and lack of testing tools. The industry has faced, probed, discovered, experimented and found its way out of most of the challenges of data testing. Having trumped so many challenges you would think developers can now sit smug and relax.

Not really. Those many challenges were just small fry when compared to the BIG one. We are of course talking about the BIG problem that the industry is currently wrestling – Big Data Testing. What are these challenges then?

Challenges of Big Data Testing

Why Big Data testing is more challenging than other types of data testing is because unlike normal data which is structured and contained in relational databases and spreadsheets, big data is semi-structured or unstructured. This kind of data is contained in database rows and columns which makes it that much harder. To top it all, just testing in your own time frame isn’t enough. What the industry needs today is real-time big data testing in agile environments. Large scale big data technologies often entail many terabytes of data. Storage issues aside, testing these Terabytes that usually take servers many months to import, in the short development iterations that are typical of an agile process, is no small challenge.

So let’s look at how this can impact two of the many facets of Testing:

1. Automation

Automation seems to be the easiest way out in most testing scenarios. No scope for human error! That seems very appealing when you’ve faced some painful ‘silly’ mistakes that can mess up your codes big time. But there are a few challenges here:

Expertise: To set up automated testing criteria requires someone with quite a bit of technical expertise. Now, Big Data hasn’t been here long enough to have seasoned professionals who have dealt with the nuances of testing this kind of data.

Unexpected glitches: Automated testing tools are programmed to scope out problems that are commonly expected. Big data, with its unstructured and semi-structured format can spew out some unprecedented problems that most automated testing tools are not equipped to handle.

More Software to Manage: To create the automation codes to manage unstructured data is quite a task in itself, creating more work for developers which misses the whole point of Automation!

2. Virtualization

This is one of the integral phases of testing. What a great idea to test the application out in a virtual environment before you launch it in the real world? But then again, here are the challenges:

Virtual machine latency: This can create timing problems, which is definitely not something you want, especially in real time big data testing. As it is, fitting in big data testing in an agile process is already a herculean task!

Management of images and the VM: Terabytes naturally gets more complicated with images. Seasoned testers know the hassles of configuring these images on a Virtual machine. To add to this, there is that matter of managing the Virtual Machine on which these tests are to be run!

There are many more challenges to Big Data testing that we will be discussing in future blogs. So what is the solution? Call the software testing experts at Gallop to know how your big data testing needs can be best managed.

The opinions expressed in this blog are author's and don't necessarily represent Gallop's positions, strategies or opinions.