Rootstrap Blog

Category: Engineering

Total 6 Posts

Building engineering goals

Rootstrap is a digital agency that focuses on web and app development. We’re based in Uruguay and the United States. As a digital agency, our vision is to become globally recognized digital masters. This epithet doesn’t just sound nice. It also means a lot: have one of the most skilled full teams, have people who love where they work, build products our client and their users love, and much more.

We had exponential growth at Rootstrap last year. Our headcount doubled to more than 100 people. Our biggest department is engineering, with about 70 people. We’re extremely excited about this growth. But no matter how much we grow and change, we won’t lose the pillars of our culture, like taking care of our most valuable asset: people.

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Code audits

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.

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Data Demystified — Data Quality

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.

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Data Demystified — DIKW model

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.

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Availability Heuristic

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.

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