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.

Testing Center of Excellence – Does your business have one? And Seven reasons why you need it

Testing Center of Excellence – Does your business have one? And Seven reasons why you need it

Testing teams are constantly under pressure to reduce development time without compromising quality. Traditional methods of quality assurance fail as they just cannot keep up with the challenges in constantly delivering software that is time-bound, robust and efficient. This has prompted more and more companies to lean towards the idea of establishing a centralized testing service.

What is a Testing Center of Excellence?
A testing center of excellence (TCoE) is a framework where testing is maintained as a centralized service and shared across the organization.

Does your business need a Testing Center of Excellence (TCoE)?
The answer is ‘yes’ if any one of the following SEVEN CRITERIA applies to your organization:

  1. Your QA is aligned to project goals rather than the organization goals – In the absence of a centralized testing framework, the testing teams report to individuals and hence do not share a common goal or direction. TCoE consolidates all testing functions under a single umbrella so that they operate towards a common goal which is, in turn, aligned towards the mission, vision and goals of the organization. This serves to add more clarity and visibility to QA in the eyes of the top management.
  2. Finding testing resources with desired technical and domain skills is a challenge and hence, projects suffer from high training costs – In a traditional QA set up, there is limited cross project visibility due to which resources are not utilized optimally. Under TCoE, testing resources are organized on the lines of technology and LOB as per their core skills which aids in proper deployment of resources across projects.
  3. Your QA process is not transparent. In other words, you are not sure how much ROI you get out of your QA process. Without a TCoE, there is no efficient tracking of how much an organization has invested into testing and how much return has it generated. TCoE introduces metric based tracking which measures the success of the QA process in terms of test coverage, test effort, defect slippage, test effectiveness etc. and the ROI on testing.
  4. You desire to reduce your testing time without compromising on quality – With TCoE, you can achieve a mature QA with standard tools and frameworks resulting in a more efficient test cycle. Organizations with TCoE in place show an average reduction of up to 30% in the testing cycle which considerably reduces the time-to-market.
  5. You do not have a standard QA process and there is no sharing of best practices, tools and automation opportunities across teams at an organization level. Unnecessary time and effort is spent on reinventing the wheel each time. TCoE standardizes testing processes throughout the organization and sets guidelines for test planning, test scripts and test execution. It facilitates sharing of best practices, lessons learnt and automation opportunities. This leads to a reduced learning curve and eliminates chaos arising from variation in testing across projects.
  6. Your projects are often struggling with issues related to defect leakage and missed deliveries – With TCoE, organizations have been able to achieve up to 50-70% level of automation in testing with an average of 30% reduction in test cycles and limit defect leakage to less than 2%.
  7. Your organization is not aware of new trends in testing. There is no focus on emerging technologies and you do not possess the framework necessary to test them. A TCoE structure helps QA to be more business oriented. It is in sync with new technologies and trends in testing which gives the organization a competitive edge and empowers it to respond quickly to new business opportunities.

Organizations that have adopted TCoE have reported an average cost reduction of 35% over a 3-year period. Some of the other benefits of TCoE? It brings in more agility to QA and helps to establish a continuous improvement process driven by metrics. Setting up a TCoE does require certain amount of change along with support and commitment from the top management. But you always have the option of partnering with a company that already has TCoE capability.

Read about how Gallop’s TCoE can help you transform your testing functions.

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