Business Case: Enhancing Business Intelligence through Tableau

Client Overview:

The client is one of the world’s leading logistics and transportation companies globally. Within a specific sector of the business, the internal business analyst created manual Excel reports to visualize customer profiles, order lifecycles, landfill optimization, and various other delivery-related information. 

As an effort to minimize the manual creation of their reports, Tableau was introduced into the process. Allowing clients to access their overview without relying on meetings. Additionally, this allows internals to check their performance and other metrics at any time.

My Role:

As the external BI consultant specializing in Tableau, I was responsible for the following:

• Understanding the logistics-based logic used in the previous dashboards.
• Collaborating with the internal business analyst to re-create the existing Excel dashboards in Tableau.
• Coming up with a more suitable structure for the data source to work best with Tableau.
• Brainstorming internally about more dashboards that didn’t exist before, which would help the business further with understanding their data.
• Testing with the users to get their feedback on improvements.
• Creating a dashboard documentation on how to use the dashboards, what they entail, and explaining the logic used in them.
• Teaching Tableau to the internal Business Analyst as we developed the dashboards.

Project Scope:

The project’s scope was to deliver business-ready dashboards that aligned with the Excel reports previously created. The project included several dashboards to replicate. Plus, with the additional time, create new ones that were planned to be made in Tableau.  

Below are some examples of the dashboards that were created and replicated:

• POD Dashboard: Proof-of-Delivery dashboard that tracked the statuses of the proof of delivery documents per different customers over time. This dashboard would be used in chasing inconsistent/lost POD documents.
• Drop Profile Dashboard: A customer overview dashboard outlining metrics such as how many deliveries were made per landfill and the size of the deliveries. This dashboard would be used for reporting to customers on their performance. 
• Landfill Dashboard: Showing customers with the most truck savings potential by looking at the fill rate of their trucks. This dashboard would be used for route optimization purposes.
• Order Life Cycle Dashboard: Showing information on orders within different parts of the order process, from orders received to invoices booked per customer, whether they are out of acceptable lead time or not. This dashboard would be a performance-related dashboard used to see which orders take more time than they would need to. 
• Site Cycle Time Dashboard: Dashboard used to outline the time spent between the delivery process, also outlining detailed information on the excessive site cycle times.

Challenges:

During development, there were a few challenges that emerged during the process in different phases of the project.

Data quality issues at the start: At the start, there were data quality issues that were identified that needed to be addressed first before data could be visualised. Therefore, working with the internal data engineer, these issues were addressed first. 

Data entry policies: In certain cases, the data entry procedure was not exactly clear, therefore resulted in poor data quality as well. Working with the internal business analyst, we came up with a procedure that should be followed by the people who enter the data. 

Planning Priorities: In the initial phase, due to misaligned priorities, more time was spent expanding certain dashboards with more information rather than finishing other dashboards. 

User Testing: Due to the unavailability of certain members on the team towards the end of the project time, we couldn’t get as many testers as we wanted.

Contributions:

The key contributions from my time at the client resulted in the following:

• Developed several dashboards outlining key performance and logistics-related metrics by replicating existing Excel dashboards and creating new ones.
• Identified data quality issues.
• Supported the adoption of the new dashboards through usage videos and documentation. 
• Supplied Tableau development knowledge to the internal business analyst.

Lessons Learned:

During my time with this project, there were several takeaways as lessons learned. 

• Early planning of the time spent on each dashboard is very important to avoid uneven attention to different dashboards. Making a priority list goes along with this. 
• There will be data quality issues to address when developing. Extra dedicated time to address these is useful in the project scope.

Conclusion:

This project marked a significant step forward in the client’s journey toward modernizing their reporting infrastructure. By transitioning from manual Excel-based dashboards to dynamic Tableau visualizations, the business gained more timely, accessible, and insightful performance metrics, improving both internal decision-making and external reporting capabilities.

While challenges around data quality, process clarity, and resource availability emerged along the way, these were addressed through strong collaboration with internal stakeholders. The outcome was a robust suite of dashboards that not only replicated existing tools but also introduced new analytical capabilities. 

Additionally, by supporting knowledge transfer and documentation, the project helped ensure long-term adoption and scalability of the Tableau environment within the business.