SQL Data Storytelling: A Comprehensive Guide

Data storytelling is the process of combining complex data from various sources to present and communicate various business insights in a concise, visual, and analytical form. The goal is to help businesses make informed decisions by presenting data in a meaningful and actionable manner. 

Why Is It Important in Today’s Data-Driven World?

It is an essential soft skill because in the vast swathes of data that we deal with in today’s world, it is very easy to get lost and not make the best use of the data there is. There is a need for concise delivery of data analysis and findings to non-technical business stakeholders to make any kind of actionable insights. 

How Is SQL Used for Story-Telling Purposes?

SQL or structured query language can be used for storing or processing large amounts of information. It is a primary tool that should be in the toolbox of a Data Analyst. SQL can be utilized in several ways to make various kinds of manipulation to the data to uncover patterns, trends, and insights.   

What Are the Main Steps in Data Storytelling? 

Understanding of the Business Problem 

An analyst must have a complete understanding of the requirements, the business problem, and the impact. For example, in the retail industry, a key issue retailers face is shrinking, which is the retail industry’s term for lost inventory. If the analyst must present insights on shrink to leadership, they must first understand what shrink is and how it affects the company’s financials.

Data Profiling, Analysis, and Pattern Discovery Using SQL 

The next step is to understand the various data sources. In the case of retail shrink, as an example, the analyst would first check for retail store inventory data within the data warehouse. They would then understand the data in detail (identify the primary keys of the table and check for null values) and combine facts with dimensions to build a base-level semantic layer. Given the vast amount of data available, SQL can be used to detect patterns and trends. The analyst would then look for products with higher shrink order them by profit margins and filter to only the top 20% (as a base level estimate). Among the products with high margins and high shrink, factors specific to the products, such as product category (bakery, produce, etc), can help craft a compelling narrative and play a role in determining how shrink can be reduced.

• How can SQL be used to draft a compelling narrative? 
  • Various aggregate functions such as SUM, AVERAGE, COUNT, MIN, and MAX can be used to better define metrics.
  • JOIN conditions such as Inner Join (which only returns records present in both tables), which are very performant, can also help return only a few records at a time.
  • CTEs or Common Table Expressions in SQL, a powerful tool that can also help clean the data and create a more performant semantic layer.
  • Filters such as WHERE conditions can help narrow down the data to a few specific cases and lead to a quicker retrieval of data.

Data Visualization and Presentation

 After the SQL Data analysis, the analyst must present the data in a concise and easy-to-understand method. Choosing the correct graphs to represent the data is crucial as it enhances the visibility of collected insights. It makes presenting the insights to the Stakeholders efficient, concise, and actionable. To better choose the right graph, the analyst should choose the right visualization tool through either a graphing tool or libraries.

• The popular options include:
  • Excel or Google Sheets – The data can be exported to Excel or Google Sheets, and graphs can be created using the inbuilt Pivot options on Excel/Google Sheets.
  • R/Python libraries – The data can be imported to Python notebooks such as Jupyter, and the graphs can be created programmatically through visualization libraries such as matplotlib, seaborn, ggplot, and plotly.
  • Tableau/ PowerBI – Data can be imported to visualization tools such as Power BI and Tableau in order to create interactive dashboards.
• Ethical Considerations in Data Storytelling.
  • The data should be findings should be accurate and truthful. There should also be considerations around PII (personally identifiable information) being handled with care.

What Are the Trends in Data Storytelling?

With the rise of generative AI, most of the data storytelling can be augmented through AI and machine learning. With the evolution of IoT, a lot of data is now real-time, and hence, there will be a need for more real-time data storytelling.

Conclusion

Data storytelling is an important soft skill that compliments SQL data analysis for any aspiring analyst. With more data being created, there is a need for more data storytelling through SQL because to have better-informed data-driven decisions, a compelling narrative is key to realizing the true value of data monetization.