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