Is your Enterprise Big Data Tested?

Is your Enterprise Big Data Tested

The Startup buzz is gaining grounds and it has transformed the way enterprises strategize and operate. Startups are known to leverage various technologies that boost cost effectiveness, efficiency and time to market. For instance, thanks to the Open source platforms, today Startups have access to the best Big Data infrastructure and testing tools at ‘zero’ cost. They run a mile further in optimizing the Cloud to reap the most from their Big Data investments.

Big Data implementation for enterprises can work wonders. What you need is a robust application that is rigorously twisted and tested to fit your organization’s requirements and objectives.

IDC (a market research firm) estimates 50% increase in revenues from the sale of Big Data and business analytics software, hardware, and services between 2015 and 2019. Big Data and Analytics Sales are expected to reach $187 Billion by 2019.

How does Big Data Empower Businesses?

Big Data has proved to be a game changer for American retail stores, as they have been able to further analyze and effectively segment the customer database and market. This has enabled to create customized marketing campaigns and offer relevant deals. Further, they have been equipped with information to schedule their deals and offers as per the data drawn by the application.

It is further predicted that government organizations across the globe will leverage Big Data to radically reduce government expenditure. High profile statisticians and officials will be replaced with Data Scientists to derive the required numbers.

After the super successful and intense Climate Change talks in Paris, there is going to be a whole lot of difference in the way Climate Change is perceived. It will not be alleged as a matter of threat, but an enabler for Market Capitalization purely on the basis of Big Data technologies. For instance, Big Data will analyze climate change views and expert comments across Social Media and Internet, which will help determine the impact rather than just depend on the conventional Meteorological reports.

Big Data implementations have brought remarkable results for enterprises who knew and kept their conviction towards the business objectives. However, it can be a major disappointment for organizations that miss out on the underlying purpose of Big Data implementation.

If the data is managed methodologically, it can empower an organization to make informed choices while venturing in the market place.

What does Big Data Testing Entail?

Big Data testing involves authenticating various data processes and not just the features. Performance and Functional testing work effectively for Big Data applications. While testing their applications, QA engineers process three types of data – Batch, Real time, and Interaction.

Collaborating with an experienced testing partner is absolutely key, as it is important to devise a high level test strategy. Moreover, before the testing starts, it is important to check the data quality and confirm related factors like data accuracy, duplication, and validate whether the existing data is all-inclusive.

In this article, we would like to highlight some prominent benefits of Big Data testing, assuring desired results that can enable informed decision making and ensure higher ROI.

Eases Downtime

The emerging concept of Bring-Your-Own-Device (BYOD) and implementation of Cloud services facilitates anytime, anywhere access to enterprise applications. Due to this there is a rising dependency on the organization’s data to run these applications. This sometimes affects the performance of the application. So, it is important to test the Big Data applications that are expected to be available for employees 24*7. It will avoid bugs, enhance data quality, and ensure seamless functioning of the application. In summary, reduce any expected downtime.

Eases Operating with Large Data sets

With Big Data Applications, development begins with implementation of small data set and then moves on to the larges data sets. As expected, the glitches occurring with small data sets are way lesser than with larger ones as the development process matures. With a view to avoid breakdown of enterprise level applications, it is crucial to test the application’s lifecycle and ensure flawless performance irrespective of changes in data sets.

Maintains Data Quality

Integrity and quality of data is immensely vital for an organization’s growth and attaining overall business objectives. Big Data is increasingly getting popular today, as it empowers enterprises and top management folks to take informed decisions based on historical as well as contemporary data points. Testing these business critical applications helps you avoid duplicity and redundancy with the data sources.

Strengthens Credibility & Performance of Data

The effectiveness and performance of Big Data applications depends on the accuracy and authenticity of the existing data available within an enterprise. Big Data testing involves verification of these data layers, data sets, algorithms, and logic. This efficiently ensures performance of business critical Big Data applications.

Authenticates Real-time data

As mentioned earlier, real-time sourcing of data defines the effectiveness of Big Data application for enterprises. Performance testing of the required data is important to confirm its operational efficiency in real-time. Time is the key word and testing is the only mechanism to determine the ‘time’ factor.

Digitizing data

Organizations across the world have data stored in hard copies, which needs to be cleaned and digitized. Testing helps to scrupulously assess and ensure that no data is not corrupted or lost. The data is converted into various digital formats as per the organization’s requirements. This further ensures availability of essential data in real-time and optimize the processes.

Checks Consistency

When data is digitized, it gets converts into various formats. With Big Data applications and predictive analysis, there are chances of inconsistency over a period of time. Testing brings down these disparities, thus reducing uncertainty.

A comprehensive Big Data and Predictive Analytics strategy enables enterprises to be more analytical in their approach, ensuring higher ROI. Today, enterprises are rapidly seeking Big Data and Analytics solutions. It is predicted by market research firms that the utilities, healthcare and BFSI sectors will bring fastest revenue growth in Big Data and Business Analytics.

Collaborating with the right partner is the need of the hour. Gallop has worked with global enterprises to devise a resourceful Big Data Testing strategy. Connect with our experts and understand the various facets of Big data testing.




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.

10 Signs You Need Help With Big Data & Analytics Testing

10 Signs You Need Help With Big Data & Analytics Testing

Many industries, of late have decided to venture into the new world of Big Data and Analytics. They are slowly beginning to fully understand the limitless benefits that Big Data unearths for them, but a lot of enterprises are also struggling to deduce useful information from their Big Data programs. Many missteps made by a company are due to the fact they haven’t tested their Big Data processes and protocols thoroughly.

Here are 10 signs that clearly indicate if one needs help with Big Data and Analytics testing:

  1. High amounts of down-time: During the deployment of Big Data applications revolving around predictive analytics, organizations might face a multitude of problems. It is almost certain that issues have gone un-checked during data collection in such cases. This is easily tackled by testing instantly during data collection and deployment, thereby reducing total down time.
  2. Issues with scalability: Usually, the development cycle starts with smaller data sets and gradually progresses to handling larger sizes. If the initial runs of the application work as designed, but if results tend to deviate, issues with scalability become quite evident. One can avoid this entirely by using smart data samples to test the framework of the application at key moments.
  3. Poor efficiency: Big data applications extract information from data sets across many channels in real time to perform data analysis. Frequently, the data obtained is extremely complex and is prone to be full of inaccuracies. The quality of data needs to be tested from the source to its last destination to ensure its reliability, thereby increasing the overall efficiency throughout the process.
  4. Bad optimization: A manufacturer should ideally be able to create new business process with the help gained from Big Data and predictive analytics. Inability to handle data in an appropriate fashion over an extended period of time, visibly indicates improper optimization of existing processes to deliver the best results. With the right mode of testing, this can be avoided.
  5. Inadequate quality: While using Big Data, various characteristics associated with data need to be checked, some of them being: validity, accuracy, conformity, consistency, duplicity, etc. If one or more aspects are ignored, the quality of data takes a massive hit. An organization should invest in thoroughly checking the data to ensure proper functionality.
  6. Lapses in Security: Issues with security while dealing with Big Data can be catastrophic for any organization. Data sets containing confidential information need to be protected to maintain client’s trust. Testing should be carried out at various layers of the application using different testing methods to avoid becoming a victim of hacking.
  7. Sub-par Performance: Since Big Data applications interact with live data for real time analytics, performance is key. Performance testing, when run alongside other types of testing, such as scalability and live integration, testing allows one to stay competitive.
  8. Issues with the digitization of information: Even today, a substantial amount of information exists in the non-digital forms [paper documents] and hence is not’t available at the click of a button. As organizations convert those to digital forms, it is important to adequately test the data to ensure information isn’t lost or corrupted.
  9. Inconsistent functionality: Access to a lot of different data sets today is what makes Big Data lucrative. An enterprise can generate limitless possibilities with the right kind of knowledge. But if the results acquired over time with Big Data applications and predictive analytics turn out to be inconsistent, it becomes a case of hit or miss for the organization. Appropriate testing allows them to determine variability accurately and eliminates uncertainty.
  10. Ensuring competitive advantage: An organization can continue to stay relevant in today’s highly competitive and dynamic market while using by employing the use of the right testing tools available to them to get the best results.

Big Data testing has a lot of prominence for todays’ businesses. If the right test strategies and best practices are followed, then defects can be identified in the early stages. To know more about Big data testing, Contact Gallops team of Big Data testing experts.

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.