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Our consultants come armed with both technical and soft skills to support you to make the most of your data.
The Data School has been paramount to our success at JLL. It has allowed us to bring in highly skilled Tableau and Alteryx consultants. It saves considerable time trying to recruit contractors as I know their skills and training are to the highest standard. Over the past 5 years my team has developed analytical insights which have generated multi million dollar savings of which a considerable amount can be attributed to the Data School consultants we have had working on projects.
Paul Chapman, Global Director of Performance Management, BI and Innovation at JLL
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What our consultants are working on
We believe that sharing knowledge and expertise is key to driving innovation and growth in the data community. That's why we're excited to share our latest insights, tutorials, and industry trends with you through this blog.
Written by our team of experienced data consultants, these posts aim to solidify their own learning while giving back to the community.
Tue 22 Sep 2026 | Vivien Lee
Workout Wednesday 2026 W42 Solution
This is a step-by-step guide of how to create this week's challenge and the bonus challenge.
Challenge
So for the main challenge you need to create the table, the charts, and the info button bookmark.
Table:
1. Create the measure for the Fastest Lap per Race
Tue 13 Oct 2026 | Michael Lamprell
How to Build a Bump Chart
One fun and cool thing you can build with table calcs is a bump chart. Here's how!
For this walkthrough I am going to be using the Superstore dataset (clicking will download the dataset): https://public.tableau.com/app/sample-data/sample_-_superstore
Tue 13 Oct 2026 | Olivia Millar
Tableau Performance Optimisation: Calculations
When building Tableau dashboards, calculations can provide essential additional analytical flexibility. However, how and where you compute these calculations affects query execution time and rendering speed
Tue 13 Oct 2026 | Dev Bhatnagar
Using DDL and DML to organize data from the Bureau of Transportation Statistics (BTS)
Last week we had the opportunity to select data, understand it, then organize it so it would be useful for our user stories that we created. I picked data from Bureau of Transportation Statistics
Tue 13 Oct 2026 | Jalil Cooper
Data Structures 2: Advanced Data Modeling
In one of my previous blogs I explained how the comprehension of data structures is critical to the data cleaning process. In this blog, I will expand upon some of those concepts through a data modeling project I recently completed
Tue 13 Oct 2026 | Marcus Rocco
From Raw Data to Insights: Building a Data Pipeline with Snowflake and SQL
From Data Storage to SQL
In a previous blog, The Data Journey: Where Data Lives, we explored the different places data can be stored, from flat files and databases to data warehouses and modern data lakehouses
Tue 13 Oct 2026 | Skyla Patterson
Data Pipeline Project in Snowflake: Housing Connect Data
In this blog I'll be going through my process of building a data pipeline in Snowflake using DQL, DDL, and DML.
The first step of this project was exploring the data; understanding the granularity, what each field meant, and then building a schema
Tue 13 Oct 2026 | Bianca Beingolea-Joseph
WHERE vs HAVING vs QUALIFY in Snowflake: A Guide for Tableau Users
If you've been using Tableau for a while, you likely have the Tableau Order of Operations basically memorized. When transitioning to Snowflake SQL, however, data filtering can feel slightly unnatural at first. You might wonder, for example, why you can't put SUM(sales) > 1000 in a WHERE clause
Fri 02 Oct 2026 | Amy Afriyie
A Beginner's Introduction to dbt
What is dbt?
Before diving into what dbt is, let's start with what it stands for: Data Build Tool.
dbt is a data transformation tool that streamlines workflows to get insights faster by helping data teams transform raw data into clean and structured data that is ready for analysis
Thu 08 Oct 2026 | Jaden Matthias
Error Handling in Python
When interacting with APIs and other systems in Python, errors can happen. 404, 501, 429?? What do these numbers even mean?? Let's discuss some common error codes that might happen and how we might want to deal with them within our script
Mon 12 Oct 2026 | Tyler McKillop
Using Codegen in dbt to Save Time Writing SQL and YAML
When working with dbt, I found that a lot of time can be spent writing repetitive code, particularly when creating source definitions, staging models and YAML files
Fri 20 Feb 2026 | Claudina Mukangabo
What Profit Margins Won’t Tell You About Your Business
When analysing business performance, it’s easy to default to the same two metrics: sales and profit margins. They’re familiar, easy to track and provide a quick indication of how things are going.
But on their own, they can create a misleading picture
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