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Unpivoting vs. Splitting by Delimiter: When to Use Each and Why It Matters

When working with data, particularly in business intelligence or reporting tools like Power BI, one of the most common challenges is how to manage fields that contain multiple values. A common example is when a field contains a list of values separated by a delimiter, like a client list for a ticket or an order.

You’ve probably encountered situations where a single field contains values like “Apple, X, Uber” and wondered: “How can I analyze this properly without ending up with a cluttered report?” Two of the most common methods for dealing with such scenarios are splitting by delimiter and unpivoting the data. But how do these approaches compare, and when should you use each one?

Let’s dive in!

The Problem: Multiple Values in a Single Field

Imagine you’re working with a dataset where tickets can impact multiple clients. The field for “Clients Impacted” might contain values like:

• Apple
• Apple, X
• Microsoft, Skip
• Apple, X, Uber

The issue is that you need to filter, group, and report on this data. But when the clients are in the same field, filtering them becomes a nightmare. You might end up seeing entries like “Apple, X, Uber” in the filter list, which is not ideal. You want a clean list of clients like “Apple, Microsoft, X, Uber,” and then to be able to see all tickets that impact Apple, even if other clients are also impacted.

To solve this, there are two main approaches: splitting by delimiter and unpivoting. Let's explore both.

Splitting by Delimiter: The Quick Fix (But Not Always the Best Solution)

Splitting by delimiter sounds like a quick fix. After all, you simply break the field into multiple rows based on the delimiter, and voilà, you're done! If a ticket impacts three clients, you end up with three separate rows, one for each client.

How It Works:

• A ticket with “Apple, X, Uber” is split into three rows:
  • Row 1: Ticket ID, Issue Description, Apple
  • Row 2: Ticket ID, Issue Description, X
  • Row 3: Ticket ID, Issue Description, Uber

This may seem like a solution, but it comes with a catch: you’re duplicating ticket data. Each row for the same ticket contains the same ticket information (e.g., Ticket ID, Issue Description) multiple times. So, while you technically get multiple rows for each client, you're also bloating your data with repetition.

When to Use Splitting by Delimiter:

• Simple scenarios: If your dataset is small and you don’t mind some redundancy, splitting by delimiter can be a quick and easy way to break down the data.
• Short-term solution: If you're just exploring the data and need a quick way to split things up for one-off analysis or reporting, this method might suffice.

Downsides of Splitting by Delimiter:

• Data duplication: As mentioned, you’re repeating ticket data, which can cause issues when aggregating or filtering. If you’re summarizing tickets, you might end up counting the same ticket multiple times.
• Performance issues: As your dataset grows, the repeated ticket information can lead to bloated datasets, impacting performance and making analysis slower.
• Messy filtering: Since you’re dealing with the raw combinations of clients (e.g., “Apple, X, Uber”), filtering becomes confusing and less intuitive.

Unpivoting: The Cleaner, More Scalable Solution

Unpivoting the data is a more structured, normalized approach to handling this type of multi-value field. Instead of creating multiple rows for the same ticket with repeated information, unpivoting breaks out the multi-value field into its own row for each client, while keeping the ticket-level data intact.

How It Works:

• A ticket with “Apple, X, Uber” is unpivoted into three rows:
  • Row 1: Ticket ID, Issue Description, Apple
  • Row 2: Ticket ID, Issue Description, X
  • Row 3: Ticket ID, Issue Description, Uber

The key difference is that ticket-level data is not duplicated—it’s simply associated with the relevant client(s).

When to Use Unpivoting:

• Scalable analysis: If you need to run detailed analysis and filters, unpivoting is the way to go. It normalizes the data without creating redundancy, making it easier to filter, group, and aggregate.
• Large datasets: For bigger datasets where performance matters, unpivoting is a much more efficient approach. It keeps the data size manageable while still allowing flexibility.
• Reporting with specific clients: If you need a clean, organized list of clients for filtering purposes, unpivoting gives you that without messy combinations of client names like “Apple, X, Uber.”

Advantages of Unpivoting:

• Cleaner filtering: You’ll end up with a neat, distinct list of clients to filter by, which makes reporting and analysis much more intuitive.
• No data duplication: Since each row is associated with just one client per ticket, there’s no redundant ticket information to inflate your dataset.
• Improved performance: By normalizing the data, unpivoting can significantly reduce the size of your dataset and make operations faster.
• Better relationships: You can create relationships between tables (e.g., linking a unique Clients table to your Tickets table) for a more robust data model.

Comparing the Two: When to Use Which?

Conclusion: What’s the Verdict?

Both splitting by delimiter and unpivoting are valid techniques, but they serve different purposes depending on the situation:

• If you’re dealing with small datasets, want a quick fix, and don’t mind some redundancy, splitting by delimiter might get the job done. Just be cautious when your data grows or when you need to perform more complex analyses.

• If you’re working with larger datasets, need cleaner filters, want better performance, and require a more scalable solution, unpivoting is the better approach. It normalizes the data, reduces redundancy, and ultimately makes your reports more reliable and easier to manage in the long run.

At the end of the day, unpivoting is usually the more powerful, flexible solution, especially as your reporting needs grow and become more complex. However, if you’re in a pinch and only need a quick, simple breakdown, splitting by delimiter can do the trick. Just remember, if you choose the quick route, don’t be surprised when things start to get messy as your data expands!

Summary: The Power of No-Code/Low-Code Data Analysis on Embedded Report Development to create Data-Driven Decision-Making Culture

No-code and low-code data analysis tools empower organizations to foster data-driven decision-making by democratizing access to data. These tools simplify data analysis, enabling users without technical expertise to extract insights and drive organizational success. Microsoft Power BI offer user-friendly interfaces, real-time analytics, and secure data accessibility, making them effective for small, medium-sized  and large organizations seeking cost-effective solutions.

Key Insights:

• No-Code/Low-Code Tools: Designed for ease of use, these tools allow users to create data workflows and perform advanced analyses with minimal or no coding knowledge.
• Microsoft Power BI: Features include drag-and-drop dashboards, real-time analytics, and integration with Office 365, providing a seamless, secure, and collaborative environment.
• Advantages: Cost-effective, user-friendly, secure, and accessible to a broader audience.
• Challenges: Limited flexibility, potential errors from predefined workflows, and the need for basic coding in some low-code tools.

Application in Embedded Reports:

A comprehensive BI solution can leverage Power BI Embedded to deliver numbers of secure, interactive dashboards without requiring end-users to have Power BI Pro licenses. Key features include Row-Level Security (RLS) for data access control, Single Sign-On (SSO) for seamless authentication, and cost optimization through premium workspaces for testing and production. The solution integrates with organizational authentication systems, ensuring a user-friendly and secure reporting experience.

Project Overview:

1. Objective: Develop dashboards with RLS and SSO, minimizing licensing costs while ensuring secure access.
2. Development Phases:
   • Requirement gathering, dashboard design, data preparation, and RLS configuration.
   • SSO setup and dashboard development using Power BI Desktop.
   • Testing, deployment, and ongoing support.
3. Benefits: Cost savings, enhanced data security, seamless user experience, and real-time insights for decision-making.

This approach highlights the transformative power of no-code/low-code tools in democratizing data access, improving decision-making, and streamlining BI implementation in organizations.

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