Some executive-level roles are difficult to define from an outsider’s perspective. Two of the most challenging c-suite positions to distinguish are that of CTO and CIO since they are only one letter different and both deal with the IT side of an organization. As a growing company or aspiring IT
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
Since Uber’s first ride request in 2010, the tech giant’s innovative engineers have revolutionized the rideshare industry. Fast forward to 2021, and withstanding a global pandemic, the company is now valued at over $15 billion. These are impressive numbers for what started out as a simple idea that quickly became
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.
Kalil de Lima is a full-stack developer on the Rootstrap team and writes about his experience tackling difficult problems for our many clients. You can also follow Kalil on LinkedIn. Introduction Recently, one of our clients asked us to evaluate the performance of their app in a production environment. To give that
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.
We are all blind — all of us. Even worse? We’re blind to our blindness.
We’re all victims to a wide range of cognitive biases that impair our decision making. But in my experience, the single most impactful cognitive bias that affects our business decisions is the “Availability Heuristic”.
Unfamiliar with the concept? We can reduce it, more or less, to a very simple phrase: “What I see is all there is”.
More formally, it’s this: we tend to think that things that come to mind quickly are the best representations of reality.
We can’t avoid it. It’s hardcoded into our primitive brain, which has to jump to conclusions fast and make decisions to survive.