GLS
When handed the responsibility for the goods of millions of customers every year, fast speed to business intelligence insights is the foundation of effective decision-making. For GLS Denmark, BI and data are the backbone of everything they do, creating high standards that their insights must meet.
This article explores how GLS has utilized Tabular Editor 3 to solve issues critical to delivering the right data to the organization, at the right time – ultimately achieving faster speed to insights, higher quality semantic models, and stronger cooperation across the organization, all by implementing Tabular Editor in their BI tool stack.
About
GLS Denmark is a parcel logistics company, specializing in the delivery of small to medium-sized parcels. As part of the global GLS Group, GLS Denmark has a strong international network, allowing them to import and export parcels all over the world. GLS offers day-to-day delivery to their customers, with thousands of daily packages delivered to home addresses and their 1700 parcel shops across Denmark.
Business intelligence plays a crucial role in the GLS Denmark organization, both in terms of retrospective analysis and to support tasks in the daily operation, including shift planning, parcel sorting, and much more.
Challenges
Delivery time: Daily operations are dependent on a fast delivery time of data solutions.
Maintaining data quality: Delivering the data value chain without impacting the quality of the data.
Frequent crashes: The previous tool stack caused a lot of crashes and ineffective downtime.
Solutions
Improved model quality: C# scripting, the Vertipaq Analyzer, and serializing the semantic models, have significantly improved the quality of GLS’s semantic models.
Increased trust in BI insights: The improved speed, model quality, and collaboration have led to more consistent and reliable BI reporting – increasing stakeholders’ trust in GLS’s data and insights.
GLS's challenges in fast delivery of data insights without compromising quality
With data as the backbone for many of their decisions, GLS relies on fast delivery time of their data solutions. But with their previous tool stack being a scattered landscape with many different platforms and data silos, they experienced everyday issues such as clunky interfaces, frequent crashes, and subsequent delay time. A related pain point was delivering the data value chain to the internal stakeholders at a fast pace without compromising the quality of the data. Another significant challenge was internal cooperation across teams, due to several large semantic models characterized by redundant logic and a lack of best practices. This resulted in a long time to market and significant risks associated with developing and deploying new models.
GLS’s work in Tabular Editor started with the open-source version, Tabular Editor 2, which they had utilized on their on-premises Data Warehouse to build their semantic models. They were – according to Andreas Benthin-Bruun, Head of Data & AI at GLS – initially swayed by Tabular Editor’s clear dedication to addressing the needs of semantic model developers; a dedication that’s evident across versions.
Choosing Tabular Editor 3
As previous users of Tabular Editor 2, it was the natural choice to make the upgrade to Tabular Editor 3 and unlock advanced semantic modeling features. Another significant driver in making the move was the Tabular Editor 3 features that are directly linked to Fabric, creating numerous benefits in GLS’s Fabric-based Data Platform.
Tabular Editor has been the application of choice since the beginning – and if you are using Microsoft Fabric, it’s almost a no-brainer.
Andreas Benthin-Bruun, Head of Data & AI, GLS Denmark
How GLS improved their data value chain with Tabular Editor
Fast speed to insights
For GLS, real value is created when they can provide high-quality insights to the right people at the right time. With Tabular Editor 3, the speed to insights is at an all-time high, enabling GLS to leverage their BI insights to make informed decisions every day.
Tabular Editor has allowed GLS to speed up the development process of their semantic models – leading to increased delivery speed of their data products and faster speed to insights – by:
- Making it easier to identify performance bottlenecks
- Offering faster model development
- Allowing for faster and easier pull request reviews due to the improved project structure
- Minimizing risk of failures and downtime by introducing clear modeling best practices
Improved model quality
With the advanced modeling features in Tabular Editor, GLS has benefited from an overall improvement in the quality of their models. Some of the features that have been most noteworthy in achieving this include:
- Serializing semantic models has resulted in a clean version control experience with increased visibility of what changes have been made and enabled parallel model development.
- Scripting their semantic models using Tabular Editor’s built-in C# script capability, making it easier to test and implement best practices across semantic models. C# scripting has also made it possible to automate some of the housekeeping on the models, saving valuable time.
- The Vertipaq Analyzer provides a good overview of the model in question, making it easier to find subjects for optimization within the model.
The features in Tabular Editor have not only improved the quality of GLS’s semantic models. They have also made the modeling experience more pleasant.
Cooperation across the organization
Utilizing Tabular Editor has enhanced collaboration opportunities between Data Engineers and Data Analysts at GLS. Engaging the decentralized team of data analysts to help create and maintain the semantic models in Tabular Editor has given the team greater ownership of the semantic models, while also increasing the speed of delivery as part of the workload shifts from the central data team to the decentralized data analysts.
Involving the decentralized data analysts in the creation and maintenance of semantic models has increased the quality of the models, as the employees with the domain knowledge now have a direct role in the development of the semantic model.
Increased trust in BI insights
The faster speed to insights, optimized model quality, and improvement of cooperation between data teams have led to an increased trust in BI insights internally in GLS. The Data & AI team has experienced more reliable reporting, as they’ve found it easier to ensure that their various semantic models are up to date and aligned on key measures.
The increased consistency between their semantic models has created more reliable data products, ultimately increasing stakeholders’ trust in the data and their BI reports.
Tabular Editor as the cornerstone of GLS's BI strategy
For the Data & AI team at GLS, Tabular Editor has become a cornerstone of their business intelligence strategy – and the only real option when it comes to semantic model development tools, as the alternatives don’t meet their high demands for what a BI tool should offer in terms of speed and features. In combination with their Fabric-based Data Platform, Tabular Editor is an essential tool in their BI work that will continue to be impactful in the long run.
The easy interaction with models, modern model types, and tables in the Fabric ecosystem makes Tabular Editor 3 well worth it – both in terms of Quality of Life and ease of use. It is also very clear when using Fabric that Tabular Editor 3 is very close to the continuous development of Fabric, and hence a product that will be useful in the long run.
Andreas Benthin-Bruun, Head of Data & AI, GLS Denmark