Automated UI testing is a daily struggle for efficiency and reliability. A single misconfigured line of code can cost teams in hours of lost feedback time and test error triaging—potentially costing companies hundreds of thousands of dollars. In this case study we will see how interactions with only two web elements led to a 34% degradation in the test execution time.
Incredibly excited about today’s news that Qlik has acquired Blendr.io. Blendr.io’s easy-to-use, scalable and secure embedded integration and automation platform (iPaaS) will expand our ability to deliver on our vision of Active Intelligence, where real-time, up-to-date data triggers immediate action to accelerate business value across the entire data and analytics supply chain.
As businesses increasingly leverage data-driven decision making, the ability to use and understand data at the company-wide level becomes mission critical. While tech behemoths like Netflix, Airbnb, and Spotify have strong data cultures built over the last decade, most companies often face challenges getting up and running with data.
Garbage collection (GC) is a very important process for all programming languages, whether it’s done manually (in low-level languages like C), or automatically. The curious thing is that most of us barely stop to think about how JavaScript — which is a programming language, and hence, needs to GC — does the trick. Like the majority of high-level languages, JavaScript allocates its objects and values to memory and releases them when they’re no longer needed. But, how?
The Insurance industry is in uncharted waters and COVID-19 has taken us where no algorithm has gone before. Today’s models, norms, and averages are being re-written on the fly, with insurers forced to cope with the inevitable conflict between old standards and the new normal.
The only certainty in today’s world is change. And nowhere is that more apparent than in the way organizations consume data. A typical company might have thousands of analysts and business users accessing dashboards daily, hundreds of data scientists building and training models, and a large team of data engineers designing and running data pipelines. Each of these workloads has distinct compute and storage needs, and those needs can change significantly from hour to hour and day to day.
It’s time, again, to look at a chart that you might not be using, but that you definitively should consider using when doing data visualization. The waterfall chart is great at visualizing the cumulative effect from positive and negative changes, as, for example, you would see in a Profit and Loss (P&L) report.
On average, data-driven companies grow more than 30% every year. Because of the competitive advantage that data confers to incumbents who are capable of extracting value from it, it has been called the new oil. Companies are tapping into this well of resources because of the advantages that it has to offer: But using data to run your operations poses its own set of challenges.
Every company is a digital company. Whether you are a traditional e-commerce company who has been selling goods online for years, a healthcare company who recently needed to accelerate online visits for telehealth, or a company whose business model has drastically shifted due to Covid, the need for digital transformation is here.
