Today we’re constantly bombarded with information about new apps, hot technologies, and the latest, greatest artificial intelligence system. While these technologies may serve very different purposes in our lives, many of them have one essential thing in common: They rely on data. More specifically, they use databases to capture, store, retrieve, and aggregate data.
This begs the question: How do we actually interact with databases to accomplish all of this? The answer: We use Structured Query Language, or SQL (pronounced “sequel” or “ess-que-el”).
Put simply, SQL is the language of data — it’s a programming language that allows us to efficiently create, alter, request, and aggregate data from databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets.
Modern applications can use SQL to deliver valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.
SQL at Work
A wide variety of roles can benefit from using SQL. Here are just a few:
- Sales manager: A sales manager could use SQL to increase sales by comparing the performance of various lead-generation programs and doubling down on those that are working.
- Marketing manager: A marketing manager responsible for understanding the efficacy of an ad campaign could use SQL to compare the increase in sales before and after running the ad.
- Business manager: A business manager could leverage SQL to streamline processes by comparing the resources used by various departments in order to determine which are operating efficiently.
SQL in Everyday Life: Real-World Examples
We’re constantly interacting with data in our lives, which means that, behind the scenes, SQL is probably helping to deliver that information to us. Here are a few examples:
Extracting Data
At its most basic, SQL is about accessing data locked away in databases. Think about the last time you received a report about how your company or team is performing. This probably had some key metrics like sales figures, conversion rates, or profit margins based on data stored in a system like a customer relationship management (CRM) or eCommerce platform.
A developer or analyst, or maybe even you, used SQL in order to access the data needed to produce that report.
Web Applications
Think about the last time you looked up the name of a movie on IMDb, the Internet Movie Database. Perhaps you quickly noticed an actress in the cast list and thought something like, “I didn’t realize she was in that,” then clicked a link to read her bio.
As you were navigating through that site, SQL may have been responsible for returning the information you “requested” each time you clicked a link.
Synthesizing Data to Make Business Decisions
With SQL, you can combine and synthesize data from different sources, then use it to influence business choices.
For example, if you work at a real estate investment firm and are trying to find the next up-and-coming neighborhood, you could use SQL to combine city permit, business, and census data to identify areas that are undergoing a lot of construction, have high populations, and contain a relatively low number of businesses. This might present a great opportunity to purchase property in a soon-to-be thriving neighborhood!
Why You and Your Business Need to Understand Data Science
On a high level, data professionals collect, process, clean up, and verify the integrity of data. They apply engineering, modeling, and statistical skills to build end-to-end machine learning systems that uncover the ability to predict consumer behavior, identify customer segments, and much more. They constantly monitor the performance of those systems and make improvements wherever possible.
Looking at the field as a whole, there’s a wide array of tools available to help data experts perform tasks ranging from gathering their own data to transforming it into something that’s usable for their needs.
In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three prevalent technologies that are transforming how we understand and use data. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data scientists and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.
These skills have surprising uses beyond data, bringing delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown. Download the paper to learn more.
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