A guide for those in business, marketing or strategy roles in tech.

If you want to work in tech, you’ll be surrounded by tons of data, be it marketing campaign open rates, in-App user activities, or network performance. According to The Economist, data has become the new oil, with companies ramping up their data collection and analytics teams in order to optimize their platforms and create “data-driven strategies”.

I became a part of this trend during my internship on the Global Growth Strategy & Brand team at Tumblr. I needed to understand how our users interacted with the platform internationally in order to move the needle on user growth, retention, and monetization. More specifically, I needed a way to browse, organize, and make sense of the data coming in daily from over 350 million blogs worldwide, and for that I needed SQL.

In short, if you want to use large amounts of data, you need to know how to use SQL.

I want to preface with saying that I’m not an expert in SQL. In fact, most people in a tech companies don’t need to be experts. What’s important is that we know enough SQL to be able to extract, organize, and leverage the data necessary for our roles, be that as a business analyst, marketing manager, or product manager. In other words, we need to know “enough to be dangerous”.

What is SQL?

SQL, which stands for structured query language, is a language for accessing and manipulating databases. Don’t let this definition intimidate you — using SQL for basic functions doesn’t need to be too complicated. Think of it as a way to choose what data you want to access by providing a list of criteria. If you’re trying to understand how many of your users logged in on Halloween and posted a picture of their costume, your list might include:

  • Logged in on October 31st, 2016
  • Posted Picture
  • Picture description included word “costume” or “#costume”

You would then produce a query that looks something like:

Once you get past the syntax, you’ll start to see how logical and simple extracting data can be. Where SQL gets really exciting, however, is when you start to piece together data from across the company in order to form new insights. Say for example you want to understand how your email marketing campaigns contribute to daily active usage in Mexico for Q2 2017. If the right data is being collected, you can stitch together data tables from across the organization to answer this question and optimize email campaigns in Mexico.

Why is this important?

There are two reasons why SQL is important to learn. The first is that it allows you to work directly with primary data, rather than requiring someone else to provide you with organized datasets. This allows you to move quicker on strategy, lead projects independently, and become a technical asset to your team.

Secondly, SQL increases your economic moat — a term Warren Buffett uses to describe your competitive advantage against others in your field. SQL is one of the most in-demand skills in the market right now and it is something that will surely set you apart from your peers.

How can I learn SQL?

There are a ton of free resources that can help you learn the fundamentals of SQL:

  1. Khan Academy — This is the one I used to get started. The slow pace and sandbox system they use is especially helpful for those of us with little or no prior coding experience.
  2. Code Academy — Code Academy has a great reputation in the tech community for delivering high quality lesson plans and projects. While you can start with a few modules for free, you’re eventually looking at $20 a month — which isn’t that bad for the amount of content they offer.
  3. W3 Schools — If you have some technical background, I’d suggest W3 schools’ DIY list of commands and SQL features. They explain concepts clearly with examples and even have a SQL interface you can play around with.

Once you’ve got the fundamentals down, check out this guide for Google BigQuery, a tool that lets you run SQL-like queries on any datasets you may want to query. Its insanely simple and free for small amounts of processing power.