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  • Olivia Hugentobler

Data Analysis With SQL

No skill is more essential or fundamental to a successful data project than a working knowledge of Structured Query Language (SQL).


Impactful data projects begin with competency in SQL and advance from there. However, SQL isn’t just for prepping data and readying it for downstream processes; it can also be used for analyzing data in a complex way.


Billions of data points are being generated every minute in our data-driven world, but without proper management, this raw data has no story to tell. SQL, with the help of a good database reporting structure, allows us to extract this data, analyze it, and unlock the data’s full potential. Further analysis allows for questions to be answered, trends to be seen, and problems to be avoided.


SQL may seem like something only those in the IT department need to understand, but there is value in knowing its purpose and place within your organization. Understanding the basics help create a data culture within your organization that leads to better outcomes for you and your patients.


What is SQL?


Structured Query Language (SQL) is a computer language used to interact with a relational database. It is a tool for organizing, managing and retrieving archived data.


SQL is the most common language for extracting and organizing data stored in a relational database. It facilitates retrieving specific information from databases that is further used for analysis.



Even when the analysis is being done on another platform like Python or R, SQL would be needed to extract the data that you need from a company’s database prior to that.


What can you do with SQL?


This programming language has various uses for data analysts & data science professionals. It is particularly helpful because it can:

  • Execute queries against a database

  • Retrieve data from a database

  • Insert records into a database

  • Update records in a database

  • Delete records from a database

  • Create new databases, or new tables in a database

  • Create stored procedures & views in a database

  • Set permissions on tables, procedures, and views

In other words, SQL allows you to explore the data within your databases, join data from different locations, make more data-driven decisions and create data reports that drive decision making. In addition, it also allows you to create and manage databases more effectively.


SQL in Behavioral Health


SQL is woven into most aspects of data analytics. If you work with data, it is likely that you are also working with SQL, but how exactly does it fit in with behavioral health?



Let's look at an example; say you are implementing a process to monitor what states are calling ‘Same Day Access’ or ‘Open Access’ to improve efficiency and provide care to more patients within your organization. You likely would want to know your overall performance and how your trends and data vary over time.


One powerful method is to look at data over time in quantiles and identify overall performance, but also find where the bulk of the data falls and who the outliers are. Over time, the bulk of the data should fall increasingly into the 25th to 75th percentile. Outlier data that falls outside this range can be easily identified, and those exceptions and opportunities can be more easily addressed.


In our experience, monitoring performance data in this way has been remarkably effective in creating an environment where real benefits can come to patients and providers alike. SQL assists with this whole process.

How to learn more


As you can see, SQL is a critical tool for data professionals. It is undoubtedly the most essential language for completing projects in data analysis or data sciences. That being said, it is valuable for everyone in your organization to have a basic understanding of this vital tool. With better understanding comes better data, and that benefits everyone.



Want to learn more about SQL? Or perhaps get a refresher? Pinnacle's September webinar will look at ways to use SQL for data analysis. We'll look at central tendency, quartiles, and regression areas. Practical tools that will jumpstart any data project and allow you to leverage your SQL skills to gain insight into your valuable data resources.


Register for the webinar here.


Missed the webinar? You can access all of our previously recorded webinars here.


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