A leading railway organization in Europe faced challenges with its heavy reliance on complex DAX (Data Analysis Expressions) within PowerBI to generate business insights. While DAX was essential for deriving critical data metrics, its syntax and logic were often too intricate for non-technical stakeholders to interpret, creating a bottleneck in decision-making. Business users needed a way to understand DAX expressions and leverage insights without depending on technical teams for every query or report.

 

Alex AI’s Natural Language Code Explainer was deployed to address this gap by directly converting DAX expressions into clear, business-friendly language within the PowerBI environment. This feature enabled business users to easily comprehend the purpose and impact of complex DAX expressions, transforming intricate code into actionable insights. By bridging the technical divide, Alex AI fostered a culture of data literacy, allowing non-technical stakeholders to interpret and act on insights independently.

 

The choice to implement Alex AI was driven by its accuracy and efficiency in translating complex code into accessible language. This solution effectively democratized access to data insights, empowering business users across departments without requiring deep technical knowledge. The ability to automate the translation of DAX expressions aligned with the organization’s broader goal of increasing data literacy and fostering autonomous decision-making.

 

Alex AI was integrated with PowerBI during implementation, and decoding DAX expressions on demand immediately started. Users could now directly see plain-language explanations of calculations, metrics, and data flows within their PowerBI dashboards. This reduced the need for technical support and provided immediate clarity for users, enabling them to make faster, more informed decisions without waiting for expert intervention.

 

The immediate benefits were tangible: business users could fully understand and act on DAX-based insights without needing constant technical assistance. This improved data literacy and user autonomy led to a more efficient workflow and quicker decision-making processes. Over time, the organization saw a marked reduction in reliance on technical experts for routine PowerBI tasks, freeing up those resources to focus on more strategic, complex projects.

 

The results were equally compelling. Onboarding times for new users were reduced by 30%, and the organization saw significant decreases in support requests related to DAX interpretation. Business users can now understand and act on insights in PowerBI independently, allowing technical teams to focus on more complex tasks. With Alex AI, the organization created a more efficient, self-sufficient environment for data analysis, where all users—technical and non-technical alike—could fully engage with data insights.

 

Customer Overview

 

• Region: Europe

• Industry/Sector: Transportation and Railways

 

Challenge

 

• Capability Area: DAX Code Translation and Data Literacy

• Data Complexity: The organization faced difficulties in translating complex DAX (Data Analysis Expressions) into actionable insights for business users. The intricate syntax and logic of DAX created a barrier for non-technical stakeholders, who struggled to interpret the code and fully leverage its insights.

• Technical Landscape: PowerBI was the primary tool used across the organization to generate insights from data, but the reliance on DAX expressions made it challenging for business users without technical expertise to understand key metrics and data flows.

• Operational Inconsistencies: Non-technical stakeholders were dependent on technical experts for every query or report, creating delays in decision-making and hindering the overall flow of business insights. This dependency on technical support created bottlenecks, slowing down business processes and reducing the overall autonomy of teams.

 

 

Value to the Customer

 

• Immediate Benefits:

  • Enabled business users to understand and utilize DAX-based insights without technical support.

  • Increased data literacy and autonomy among non-technical stakeholders, fostering confidence in data-driven decision-making.

 

• Long-term Value:

  • Improved efficiency by reducing dependency on technical subject matter experts for routine PowerBI tasks.

  • Enhanced cross-departmental alignment on data projects, enabling faster insights and response times for business needs.