Posts in category: Case

Challenge:

As Data Lead & Business Analyst at Power BiSon Consultancy, I was faced with a pressing challenge: our client, a large insurance company, was struggling with a flood of customer complaints. Their legacy system was overloaded, creating bottlenecks and making timely resolution impossible. Given the importance of customer satisfaction to their business, they needed a robust solution for the complaints process.

Our approach:

My team and I developed a suite of Power BI dashboards to significantly improve the complaints process:

• Complaints Analysis – Provides a complete overview to quickly gain insight into complaints
• Complaint Action Plans – Prioritizes overdue cases and provides clear direction for urgent follow-up
• Complaints Details – Provides detailed information for accelerated and target group-oriented processing

Results:

The dashboards proved to be a game-changer. Where data previously got lost in the overabundance, they offered insight into patterns and urgency, plus real-time tracking of processing. They became the "single source of truth," and customer satisfaction significantly increased.

Future plans:

Our journey doesn't stop here. We continue to improve the dashboards, including with predictive analytics, so complaints are not only handled but also prevented.

Team Expertise:

Challenge:

At KVL, an organization with the mission of "Bringing Analytics to the People," an ambitious training program was on the agenda: training a new generation of Data & Analytics professionals. We had to streamline delivery, sales, account management, product and process development, and simultaneously strategically align with business initiatives.

Our approach:

In my role as Business Lead Data and Product Owner (OPEX), I was hands-on involved in projects, supported teams, and monitored performance and financial indicators. I developed and executed strategies to achieve business goals, coached twenty Data Heroes and four KVL mentors, and mentored both technical and operational leaders. I combined Agile, Scrum, and IT management methods with coaching skills to generate buy-in and growth.

Results:

The results were clear: improved resource planning, more rigorous project-based quality assurance, and controlled progress. Power BI reports visually broke down productivity statistics, and my technical and soft skills facilitated strong communication with stakeholders.

Future plans:

We're building on these successes by expanding the training program to include advanced data management techniques and analysis tools. Our goal: a culture of continuous learning and adaptation in the changing data landscape.

Team Expertise:

Challenge:

The biggest challenge was improving the tracking and analysis of fundraising campaigns to maximize their effectiveness. The organization needed detailed insights into proceeds, pledges, and spontaneous donations to strategically measure and adjust the results of various campaigns.

Our approach:

We developed two key Power BI dashboards:

• Fundraising Dashboard – Tracks and analyzes the performance of fundraising campaigns with features such as trend analysis, forecasting, overview of active and inactive pledges, analysis of spontaneous donations, and donor segmentation.
• Donors Dashboard – Provides deep insights into donor demographics and behavior for targeted fundraising strategies. It includes trends of active and inactive donors, segmentation, and strategies to reactivate dormant donors. This is supported by a robust Power BI data model that allows for integration and reporting:
• Starscheduleoptimization: Improves query performance and effective filtering
• Integrating multiple data sources: Brings together data from Ifunds Engage, Dynamics 365 and more for rich analytics

Results:

These tools allow us to monitor fundraising and donor behavior much more effectively. The dashboards and data model provide a complete overview of all activities, supporting strategic, data-driven decision-making and improved efficiency.

Future plans:

We continue to refine dashboards using predictive analytics to identify trends and opportunities. Further integration will include real-time data for faster response.

Team Expertise:

Challenge:

At the De Stichtse Rijnlanden Water Board, responsible for water management in a substantial part of Utrecht and part of South Holland, a complex challenge arose. The organization wanted to optimize its data analysis and reporting processes to more effectively manage water management, purity, and water safety in the region. Existing systems lacked the necessary flexibility, accuracy, and efficiency.

Our approach

In my role as Power BI/QBIX Functional Manager (at Qcompany BV), I led a thorough analysis of the existing data architecture and reporting methods. I proposed deploying QBIX and Power BI in accordance with the organization's architectural standards and branding. This included improving and creating new reports, ensuring availability, performance, and continuity, and carefully balancing the interests of users and the organization with each report update.

Results:

Our efforts led to significant improvements in operational efficiency: faster reporting, higher data accuracy, and improved usability, based on data-informed decision-making. The reporting process also became more flexible and user-friendly.

Future plans

Building on this success, the next step is to explore the integration of AI and machine learning to predict water management and enhance trend analysis. We aim to develop predictive models for water quality and dike stability to support proactive management.

Team Expertise:

Our team—consisting of one Business Intelligence Consultant and one Functional Manager, with expertise in Power BI, QBIX, and advanced data architecture—was essential in addressing technical challenges and strategically aligning solutions with the water board's mission. This project underscores the importance of functional management in achieving operational excellence and the role of integrated solutions in complex public data management challenges.

Challenge:

The complex customizations in Van Oord's Power BI solutions resulted in overly complex DAX calculations, causing the core visualizations to load more than 24 seconds. The desired load time was under 700 milliseconds.

Our approach:

We reexamined the data model, carefully mapped the business processes, and translated them into a simplified data model. Every measure was rewritten and optimized with Server Timings for efficient performance. This included a thorough overhaul and refinement of all DAX calculations to increase efficiency and reduce loading times.

Results:

The most complex visualization now loads in just 1300 milliseconds, with most visualizations loading in under 300 milliseconds. This is a significant improvement over expectations.

Future plans:

The team intends to continue with production implementation and onboard more projects and team members based on the new, scalable model. This expansion will leverage the optimized data model to further improve project management within Van Oord's operations.

Team Expertise:

Our team used an advanced mix of tools and techniques, including DAX Studio, VertiPaq Analyzer, Server Timing Optimization, Query Plan Optimization, and Power BI DAX. We also applied Kimball modeling and star schema design to create an efficient and scalable data architecture. This combination was crucial for addressing the performance challenges and delivering a robust solution for Van Oord.

Challenge:

Enza Zaden's Seed Operations department needed reliable, scalable business intelligence solutions to support strategic and tactical decision-making. Existing reporting lacked consistent, fast, and proactive monitoring capabilities, which limited the ability to quickly act on data insights.

Our approach:

As a Business Intelligence Reporting Specialist, I translated complex business needs into scalable data models and insightful dashboards. Using Microsoft Fabric, Dataflows, and Deployment Pipelines, I achieved seamless data integration and automated delivery. I also implemented Data Activator alerts for real-time monitoring and proactive decision-making.
For long-term scalability and consistency, I helped define uniform data definitions and a governance framework, which fostered collaboration between teams. Using Tabular Editor, I optimized the performance of tabular models, and using Databricks and DBT, I automated large-scale data transformations that strengthened insights for operational improvements.

Results:

The dashboards significantly improved the speed, consistency, and usability of reporting within Seed Operations. Real-time alerts enabled timely intervention, and optimized data models enabled faster analysis and deeper insights. Alignment with governance practices ensured reliable and reusable reporting structures across the department.

Team Expertise:

This project was made possible through close collaboration between IT, data professionals, and policy advisors. My expertise in data governance, framework design, and AI readiness—enhanced by a strategic and collaborative mindset—was crucial in aligning and scaling both fundamental data principles and emerging AI technologies.

Challenge:

Waternet, Amsterdam's regional water authority, needed a structured, organization-wide data governance framework to manage the increasing complexity of its data landscape. The challenge was fragmented data ownership, unclear responsibilities, and inconsistent validation procedures—all of which hindered data-driven decision-making and sustainable AI adoption.

Our approach:

As Data Management Specialists, we began by analyzing the existing data landscape and identifying systemic bottlenecks. Based on this analysis, we designed a comprehensive data governance framework with clear roles, responsibilities, and procedures for long-term data quality, ownership, and traceability. Simultaneously, we addressed day-to-day data management issues by acting as a sparring partner for various stakeholders within the organization. We translated complex data challenges into pragmatic, actionable solutions, promoting clarity and alignment in decision-making. Knowledge transfer was a key component—teams were supported through documentation, coaching, and strategic advice to increase their data maturity. Additionally, I contributed to Waternet's AI strategy by developing implementation plans for Microsoft Copilot AI and supporting the design of a future-proof AI governance structure.

Results:

This approach has laid the foundation for sustainable and structured data use throughout the organization. Collaboration has improved, roles and responsibilities are clear, and governance principles are embedded in daily operations. The AI governance plans position Waternet to scale AI-responsibly within a secure and transparent framework.

Future plans:

The next steps are to embed the data governance framework across all departments and launch a pilot for Copilot AI. We also want to implement metrics to measure data maturity improvements and ensure governance practices evolve with changing business and technological needs.

Team Expertise:

The approach was implemented in close collaboration between business stakeholders and data professionals. My expertise in Power BI, Microsoft Fabric, Databricks, DBT, and governance frameworks played a central role in the technical implementation, while cross-functional collaboration ensured that the solutions were aligned with operational realities and strategic goals.