Some executive-level roles are difficult to define from an outsider’s perspective. Two positions that can be difficult to distinguish are that of the VP of Engineering and the CTO. As a growing company or aspiring IT executive, you need to know: What is the difference between a VP and a
Duolingo came on the scene in 2011 operating as a teach and translate language learning app. It has since transformed into a $700+ million business, and become the first EdTech app to top $140 Million in annual revenue. Pretty good numbers, for a language learning app that still doesn’t charge
Stress tests and load tests are both used to measure performance, so many find it hard to distinguish between the two. Although both tests share similar facets and tools, there are many differences between the two. Regardless of their differences, both tests are vital to achieving a high-performance rate. According
Have you ever experienced any of the following digital failure scenarios? You open a website on your phone, but nothing looks right, and you can’t even navigate. You hit ‘checkout’ on an eCommerce app on your tablet, and nothing happens. You try to use an app on your desktop but
Have you ever said one of these tricky phrases? I’m sure this feature is going to rock! We need this feature before going live, it is a must! Users will use this for sure! Well, if you found yourself saying any of these phrases without any numbers that support you,
To be globally recognized digital masters, that’s our vision at Rootstrap. And we are convinced that to get there we should hire the right people. We seek those that will help us continue to offer a premium service, create remarkable products and be the perfect addition to our creative and
You could compare the speed and innovation of the mobile app industry to that of a world-class factory line. In the blink of an eye, a new app, technology, and a device have been created on a never-ending belt of new ideas. Off they go to be consumed but to
An in-depth analysis of your system’s health.
For many projects, clients hire us to only run code audits. In other cases, we inherit legacy code, and going through a code audit is a requirement for working with us.
With time and repeated experience, we refined and strategized our audit process. It’s now a distinct work product that we offer to clients on its own.
As said, we often get projects that were created by other teams — sometimes in-house techs and sometimes offshore providers. In these cases, clients ask us to just take over the work or to only fix the problems. But we don’t work like that.
Explaining conceptually what it really means, and why it matters.
This article outlines a mental framework to organize our work around Data Quality. Referencing the well-known DIKW Pyramid, data quality is the enabler that allows us to take raw data and use it to generate information, starting from raw data.
In this piece, we’ll go over a few common scenarios, review some theory, and finally outline some advice for anyone facing this increasingly common issue.
The amount of data being generated every second is almost impossible to comprehend. Current estimates say that 294 billion emails and 65 billion WhatsApp messages are sent every single day, and all of it leaves a data trail. The world economic forum estimates that the digital universe is expected to reach 44 zettabytes by 2020. To give you an idea of what that means, take a look at the byte prefixes and remember that each one multiplies by 1000: kilo, mega, giga, tera, peta, exa, zetta.
Understanding the big picture first will set the stage for success in this journey.
Data is one of the biggest new trends in both tech and business in general. Data “experts” are quickly becoming some of the best-paid individuals in the industry, and every single company wants to surf the wave of data capabilities.
It is becoming a fundamental way of understanding the world around us. We can think of data sciences as epistemology or a way of knowing. We can think of it, about a way to approach problems and solving them.
But as with any new trend, we have to ask ourselves: what do all these buzzwords actually mean?
What is a data scientist? In short, a person who is better at statistics than any software engineer and better at software engineering than any statistician.
