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5 Tips for You From a Senior SQL Data Analyst

Are you looking for tips for becoming a better data analyst? Do you want to know about the mistakes and issues you may face? I have collected five tips for your everyday work based on my experience as a data analyst.

You may have already read about what data analysts do, how much they earn, and how to become one. However, there is more to it than just becoming a data analyst. You have influence over many factors to become even better at your job and advance your career.

We usually learn about these factors only after years of experience with painful and costly mistakes. I'm going to try to save you some of that time with five tips from my experience working as a data analyst for many years in a wide range of business settings.

I'll cover the following:

  • Taking care of your SQL queries.
  • Mastering GROUP BY.
  • Validating your data.
  • Understanding the needs of the business.
  • Continuing to learn and practice SQL.

For each tip, I'll give you the context in which they occur, explain their importance, and provide some resources for following up.

An overarching lesson in each tip is that good SQL knowledge prevents many mistakes and makes you a more efficient data analyst. As the best way to improve a skill is by practicing it, you gain lots of value from our SQL Practice track tailored for this specific need.

Let's start with the topic of SQL queries.

Tip 1: Take Care of Your SQL Queries

When writing an SQL query, your goal is to get insights from the dataset. Your main concerns are speed and the output. This is different from our long-term focus when we want to understand, build upon, and manage existing SQL queries that you and other members of your organization write.

Accessibility is a great strength of SQL, allowing us to write up queries quickly without vexing syntax and formatting constraints. However, this ease of use requires us to be mindful of the format and style of our SQL queries. I had often struggled in understanding the code I wrote in the past because I did not take the care to make the code readable when I wrote it. Without a consistent and commonly accepted language, team members have difficulty understanding our queries and the relationship between our metrics and reports.

For this reason, follow best practices when writing SQL queries, like using descriptive names and being consistent with formatting and style. Doing so allows you to understand your queries well after you write them and be an effective team member.

When working on big, complex projects, you may want to introduce modularity to your queries with documentation and tests. You can do this by leveraging tools like DBT that allow you to apply engineering principles to your analytics.

Tip 2: Master GROUP BY

The GROUP BY clause is one of the basic SQL commands. We use it to go beyond the individual record level and examine data grouped into categories (e.g., months of the year, product categories, and customer groups).

We learn GROUP BY early in our SQL journey, but its use cases go much farther. We can use GROUP BY with multiple calculations, with multiple grouping columns, or by conditioning our query on aggregated values. For more serious reports, we can generate subtotals, super-aggregated rows, or multiple grouping sets.

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We often make mistakes when using GROUP BY. Examples include using the wrong conditional clause (WHERE vs. HAVING), grouping data on non-unique keys, neglecting NULLs, and aggregating the wrong values (e.g., count of rows vs. count of distinct values). Not being aware of these issues often leads to false reports and painful misunderstandings.

To utilize the full benefits of GROUP BY and protect against mistakes, master its use cases and possible problems. Check out our courses for options!

Learning the ins and outs of GROUP BY allows you to gain more in-depth insights, produce feature-rich reports, and avoid bad business decisions from reporting mistakes.

Tip 3: Validate Your Data

Imagine you have been working hard on a problem for days. You have succeeded in generating the report, but the results are off. When you reverse engineer your steps, you realize some of your data is missing or "dirty" (e.g., it has inconsistent values or bad column names).

With SQL, we manage data in relational databases. This gives SQL a great advantage over Excel. However, this also means we need to be more mindful of the dataset and its relationships.

As you advance in your career, you take on bigger projects. In such cases, maintaining data quality throughout the whole data pipeline becomes a separate task. This taps into Data Engineering and Analytics Engineering.

SQL gives you the means to check for bad and missing data, which you should do actively and often. Checking your data frequently prevents unnecessary work and reports based on wrong assumptions. It also makes you better understand the dataset and its business value.

Tip 4: Understand the Value of Data Analysis for the Business

Another common mistake is to jump on data analysis right away trying to get insights too quickly. I've had cases in which, after working with the data for days, I would realize the values in the data did not mean what I thought they meant. In even worse scenarios, the original question would turn out to be wrong because of bad assumptions about the data and the business case.

For this reason, do not waste time just analyzing data. Your ultimate aim is to solve business problems and help strategic decisions. Understand the problem before you start writing queries.

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A great way to do this is by stepping back and spending time understanding the problem. It also helps if you get exposure to real business problems in many settings like marketing, business analytics, or finance.

Having a strong business understanding makes you a better analyst and better able to solve higher-level problems.

Tip 5: Learn SQL and Keep Practicing It

SQL is not a static skill you learn once and never look back. I wrote my first SQL query more than ten years ago, but still, there are countless topics and techniques I know very little about.

True, SQL is great – you can write effective queries by knowing only the fundamentals. However, the language provides plenty of space to improve query speed, depth of insights, maintenance, and readability. To write better queries, learn these approaches by developing your SQL skills constantly.

Fortunately, there are many ways to improve your SQL skills. Read SQL queries by other analysts. Participate in online discussions. Read books. You can also answer questions in Stack Overflow, the DBA Stack Exchange, and other forums organized around your favorite SQL dialect. For practice, use our SQL Practice track or SQL Cookbook.

Improve Your Data Analysis Skills

I've given you five tips to upgrade yourself as a data analyst and advance your career. These tips for analysts involve writing better queries, mastering the GROUP BY clause, validating your data, being mindful of the bigger picture, and learning continuously. I have also shared resources for following up on each tip.

If you are looking for a comprehensive approach to learning SQL, guiding you from the fundamentals to advanced techniques, check out our SQL from A-to-Z track. Also, check out our more function-specific offerings (like our track on reporting).