Is SQL Necessary for a Business Analyst?

In the modern business environment, ever-larger amounts of data are being gathered and stored in databases. With SQL being the language used to query relational databases, is there a need or benefit for business analysts to learn SQL?

SQL is not necessary for a business analyst. However, SQL, as a practical component of your skillset, can be helpful. It allows you to understand and write your unique SQL queries, thereby increasing your effectiveness.

If you are currently a business analyst or considering a career in the field of business intelligence, read on. This article will help you understand why knowledge of SQL can be beneficial to your job performance.

Important Sidenote: We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

An Overview of SQL

As a business analyst, to gauge the value you can derive from familiarizing yourself with SQL, it is vital first to understand what SQL is and its essential role in querying relational databases.

SQL is a non-procedural language. SQL allows you to access individual or multiple records in a database without writing numerous commands regarding how to access them. You can accomplish a query by merely writing a command stating the data and parameters you want to be queried.

The American National Standards Institute (ANSI) and the International Organization for Standardization (ISO) have adopted SQL as a standard and issue documentation regarding its compliance. As a result, SQL has become the primary querying and manipulation language for relational database management systems.

How SQL and Business Analysis Intersect?

While a business analyst’s definition and job role may vary from company to company, the core element of being a business analyst involves analyzing the processes and procedures of an organization. The end result that is delivered is improved efficiencies and better organizational performance.

No matter the industry that a business analyst works in, the data required to conduct their work will usually be stored digitally in relational databases. Having explained the role of SQL in relational database management systems, the overall benefit of having SQL knowledge as a business analyst should be implicit.

However, SQL and business analysis do not intersect solely in the general procedures of accessing data from a database. There are more specific use cases that could be of more significant benefit to a business analyst. 

Data Mining

In its most basic form, data mining involves converting raw data into useful and actionable information. Data is collected and stored so that business analysts may identify patterns of significance to a business.

Knowledge of SQL, specifically SQL Server Analysis Services, can help a business analyst better comb through data and identify patterns. Business analysts can use this type of information at the enterprise level to provide essential information for elaborating strategic initiatives and other top-level decisions.

At the operational level, using SQL in this manner can help a business analyst identify areas in a production process where changes are needed or where modifications can increase efficiency.

More importantly, when it comes to SQL and data mining for a business analyst, it can serve you as a base for performing and accomplishing even more complex analytical functions by being proficient with the process.

Developing Metrics and KPIs

Key Performance Indicators (KPIs) are established to help guide the strategic path of a company. Metrics provide a way to measure the performance of those KPIs.

For some business analysts, tracking a company’s KPIs is a fundamental part of their job. Generating KPI reports for this type of tracking is made easier with well-crafted SQL queries. Forming a proper query will allow for a more in-depth exploration of the data while also allowing for a more detailed and specific analysis.

Knowing SQL, at least enough to write your custom queries, will make your KPI reporting more accurate and help you deliver KPI and metrics reports faster. In turn, this more expeditious manner for handling KPIs will also allow you to incorporate metrics involving narrower time frames for even higher precision in performance reporting.

Even if a business analyst were not to write their SQL queries, knowing how to interpret the queries written by others can ensure that the correct data is being used when applying metrics to gauge performance.

Conducting Risk Analysis

Every business enterprise is going to have areas of uncertainty. These areas of uncertainty imply risk. 

One of the responsibilities of business analysts is to investigate these areas and determine their potential for negative impact on the company. The risk has to be quantified in terms of probability and impact. How likely is a threat to occur, and if so, how much damage could it cause? The entire process is risk analysis.

Identifying trends is paramount in locating and identifying risks. A business analyst competent in SQL and data mining can uncover trends that will reveal risks and their frequency more accurately.

SQL knowledge can also help the business analyst in estimating the potential impact of the identified risks. Such an outcome is made easier through the way that data manipulation is expedited with SQL. That makes it possible for a business analyst to draw more data and run it through different assessment models to calculate the impact of an identified risk more accurately.

A business analyst should see SQL knowledge as a path for locating and manipulating data more efficiently to conduct more accurate risk analysis models. When analysts rely on queries prepared by others or are limited only to a query model that depends on a limited number of tables and databases, the resulting analysis may not be as precise as desired.

Conducting Root Cause Analysis

It is not uncommon for companies to experience what can seem to be chronic problems. These can exist at the operational, administrative, logistical, and enterprise levels.

Even though effective business analysis would seemingly mitigate such issues, they can and do occur. The reason for this extends from legacy problems inherited from prior ownership or management to problems developing over time as part of larger shifts in operating environs.

Root problems can also encompass those that caused a specific company initiative to fail.

When these issues exist, a business analyst will have to find the root cause of the problem. The process for achieving this involves examining information that will trace back to the source of the problem. Identifying the problem, however, is only the first step of root cause analysis.

The subsequent step involves collecting as much relevant data on the problem as possible. SQL experience will help a business analyst to query data from different databases and tables within those databases. The same process can identify anomalies and false or missing data to determine the cause more accurately.

You can also use SQL to generate inputs from the data to use in causal diagrams, such as Ishikawa, Fishbone, and other cause-and-effect diagrams. SQL can also help a business analyst determine inputs that can be used to confirm the root causes discovered through front-end efforts instead of business analysis.

SQL on Enhancing Your Career Path

Something else that needs to be noted regarding SQL and business analysts is how having SQL knowledge can help career advancement and entry into the profession. Even when companies do not place SQL as a prerequisite for a business analyst position, having SQL experience can allow you to obtain better compensation packages, faster advancement, etc.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


SQL may or may not be listed as an official prerequisite by a company when recruiting business analysts. However, having SQL knowledge as a business analyst can allow you to be more efficient in your job performance. 

Data mining, working with KPIs and metrics, conducting risk and root cause analysis—you can significantly enhance all of these processes if you have SQL knowledge.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Business analyst. (n.d.). Job Search | Indeed.
  2. Business analyst: Career path and qualifications. (n.d.). Investopedia.
  3. Data mining: How companies use data to find useful patterns and trends. (n.d.). Investopedia.
  4. Here’s the difference between metrics and KPIs—and why it matters. (2021, January 7). The Answers Are In Your Data |

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Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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