Data analytics is a complex field that has rapidly become essential to modern business and technology. When placed at the center of fields like business intelligence and strategy planning, data allows people to make better decisions that lead to better results. Big data is set to reach a market value of nearly $275 billion by 2023, with the relative value of the field increasing every year. At the core of data analysis is the ability to manipulate, structure, and interact with data at scale. In order to use data effectively, data analysts need to understand the language of structured data.
To gain proficiency in manipulating and interpreting data at an advanced level, individuals can enroll in specialized data analytics courses, providing them with the necessary skills to excel in this evolving and dynamic field.
Especially when exploring other programming languages that are better suited for data analytics, like Python or Java, it may seem strange to use SQL (Structured Query Language). However, SQL is still a leading scripting language, allowing developers to communicate with databases, create relationships, and collect data for their analysis.
Considering that the syntax of SQL is one of the easiest to learn, this is also a wonderful starting point for those who aren’t familiar with programming languages and want to get a running start. SQL is fantastic at manipulating data, a skill that the vast majority of business data uses to some extent.
In this article, we’ll dive into the use of SQL for data analytics, demonstrate the benefits of using SQL in databases, and explore how you can get started with SLQ for data analytics. Let’s dive right in.
What is SQL?
Structured query language, better known as SQl, is a leading programming language that allows developers and analysts to store and store information in relational databases. Relational databases map data into a tabular form, including structured information in isolated tables. Using SQl, you are able to store data in tables, update information, remove data, or even determine relationships between two or more tables.
SQL has been one of the most popular programming languages for decades, with its ease of use and straightforward syntax making it a phenomenal choice when working with data. When using SQL for data analytics, analysts can employ the language to interact and retrieve specific pieces of information.
Drawing together different sets of data can expose new relationships and shed light on compelling insights for businesses.
What Are the Benefits of SQL in Data Analytics?
Dating back to the 1970s, SQL has been around for decades due to one simple reason – it works incredibly well. While easy to learn, SQL is a powerful tool when interacting with data in a relational database. While not all data arrives to businesses in a structured format, due to the extensive transformation process that the data pipeline can execute, most businesses will deal with structured data on an extremely regular basis.
SQL has a number of useful benefits that make it a pertinent choice for data analysis:
- SQL is a fairly straightforward language that is easy to write and understand, making it very transferable in a business.
- SQL is impressively fast and can help process lots of data in a short amount of time, drawing information from disparate databases and data sets with ease.
- SQL is a well-established language that has extensive documentation. Analysts will be able to find examples and tutorials for almost any function due to the long-standing nature of this programming language.
As a modern, extensive, and powerful tool, SQL is one of the core languages used in relational databases and data analytics.
How to Use SQL for Data Analytics
SQL allows data analysts to forge connections between different sets of data, creating connections that then give disparate pools of information more meaning. Much of business intelligence comes from the connections that data analysts form. Especially considering that most business analytics will arrive to an analyst through the data pipeline as structured data, SQL is one of the most precise languages available for laying the foundation for accurate analysis.
The central function of SQL for data analytics is to define relationships between different sets of data. For example, if you had a list of customers and a list of total purchases each had made, you could create a query to define each of these customers into a category. Many businesses like to segment their audience for marketing and sales strategies, which this SQL query can help do.
Using a query, you can segment your customer list, assigning those who have bought 3+ more items to a Loyalist category vs. First-Time customers. Once you have this segmented list, you can then feed this new data relationship into BI tools to facilitate data-driven actions from your research. SQL defines, outlines, and realizes the relationships that businesses can then take advantage of.
SQL also works fantastically with a number of business intelligence tools, like Power BI and Tableau. The link to these tools will allow you to use your SQL skills to analyze data on a larger scale. Equally, you can optimize SLQ queries relatively easily, making this a wonderful language for streamlining performance. Check out how to debug a SQL query for more information on database optimization.
Part of what makes SQL so effective for analyzing datasets is its extreme usability. As one of the easiest programming languages to learn, analysts can dive head-first into SQL querying with relatively little training, hitting the ground running and starting to create analysis as soon as possible.
Especially with the surge in the democratization of data in the world of business, the ease of this language gives absolutely anyone the ability to start drawing new insights from data sets.
Conclusion
When breaking into the field of data analysis, analysts need to go beyond only seeing data as a final form. To better understand how to interact with data and draw meaning from it, analysts must have a comprehensive understanding of all the languages that manipulate, structure, and interact with large data sets.
Using SQL for data analytics is a fundamental skill that every data analyst should cultivate. By being able to create relationships between disparate tables of data and forge connections, analysts can draw more understanding from the data that they engage with. SQL is a vital skill for all data analysts and one that should be a first focus when building skills in this area of informational sciences.
