FAQ

Common questions about data engineering, my approach, and how I work with clients. This page serves as a high-level overview. For a closer look, follow the links to our detailed articles.

What are the pros and cons of working with a solo consultant?

Direct access, deep accountability, and cross-project insights are the primary benefits of working with a solo consultant like myself. Unlike large agencies where your project might be handed off to junior staff, I personally handle every phase—from strategic sanity checks to deep data engineering. Because I focus exclusively on services and project-driven companies, I bring insights from dozens of similar operational transformations. This ensures that the original business intent is never lost and that you benefit from recognized industry patterns. While my throughput is limited, the trade-off is a high-trust partnership that eliminates the risk of implementation failure. Read more here.

What is “AI-Enhanced Development”?

AI-Enhanced Development is my methodology for building modern BI solutions faster without sacrificing professional standards. By using AI for the technical “heavy lifting” (like boilerplate code and documentation) within a framework of strict guardrails, I can dedicate more time to the strategic alignment your project requires. This combination delivers a more reliable, tested solution in a fraction of the time it takes traditional manual teams. Read more here.

Why do most BI projects fail, and how do you avoid it?

Most BI projects fail due to the “implementation disconnect”—the space between a strategist’s vision and an engineer’s execution. By handling the project from A to Z, I eliminate this gap entirely. I provide an external sanity check on your internal research, challenge assumptions early, and ensure the engineering actually delivers the operational control the strategy promised. My goal is to reduce the uncertainty that often stalls essential data investments by providing a single point of accountability for the entire transformation. Read more here.

How do I align data with my business goals?

Aligning data with business goals is the most essential step in any BI project. The secret is to start with the specific business decisions you need to make rather than the technical tools you want to use. I work with you to identify the key operational and strategic questions that currently lack clear, fact-based answers. By mapping these questions to your existing data sources, we build a roadmap where every dashboard and data pipeline serves a direct, measurable business purpose. Read more here.

How do I build a data-driven culture?

Building a data-driven culture is about more than just providing tools; it’s about creating an environment of empowerment and accountability. It requires finding the “edge of chaos”—the balance between total data freedom and rigid control. When your team has access to reliable, understandable data and is encouraged to use it in their daily decision-making, the organization shifts from acting on “gut feel” to acting on evidence. This cultural shift often starts at the top and trickles down through every department. Read more here.

When do I need a data warehouse?

You need a data warehouse when “Spreadsheet Hell” becomes a daily reality for your team. If you find yourself spending more time arguing over whose numbers are correct than actually analyzing the data, your current system is likely overwhelmed. A data warehouse serves as a central repository that transforms raw data from multiple applications into a consistent, historical record. This allows for cross-departmental analysis, better performance, and a single, trusted source of truth for the entire company. Read more here.

What is the Data Vault methodology?

Data Vault is a modeling methodology designed specifically for enterprise data warehouses that need to be highly adaptable and fully auditable. Unlike traditional methods that can be rigid and hard to change, Data Vault separates business keys, relationships, and descriptive data. This architecture makes it incredibly easy to add new data sources without breaking existing reports, providing a scale-proof foundation that grows with your business while maintaining a complete history of every data change.

What is WhisperQL?

WhisperQL is an AI-powered product I developed that demonstrates the practical application of AI-enhanced development. It allows non-technical users to query their databases using plain English (Natural Language) and get instant SQL queries and insights. It’s a prime example of how I can build “democratized data access” into your organization, making it possible for anyone to get the answers they need without waiting for a technical specialist.

Should I use a Single Source of Truth?

While a Single Source of Truth (SSOT) is the ideal for core KPIs like revenue or headcount, modern architectures also recognize the value of “Multiple Versions of Truth” (MVOTs) in specific contexts. For example, a sales forecast might use different assumptions than a financial audit. I help you design a system that provides the consistency you need for your main reporting while allowing the flexibility required for specialized departmental analysis.

How do you handle data quality in large organizations?

Data quality is not a technical problem; it’s a shared responsibility. I’ve found that the best results come when data is owned by the people on the frontlines who know the clients and products best. In a recent project for a regional accountancy firm, we rescued a sprawling initiative by refocusing the entire team on a single “Data Quality Day.” By bridging the gap between engineering and the actual business context, we turned a technical liability into a revenue-generating asset. Read the full case study here.

How do your philosophy background and personal interests (like surfing) relate to data?

My background in philosophy is a “secret weapon” that helps me spot hidden assumptions and structure complex problems. Similarly, the mindset of a surfer—patience, commitment, and the ability to “read the waves”—is essential for navigating the constant changes in data innovation. Read more here.

What size companies do you work with?

I primarily work with growing services and project-driven companies with 50+ employees. These are typically organizations where operations have reached a level of complexity that can no longer be managed effectively “by feel” or through manual spreadsheets. My ideal clients are those ready to transition from reactive data handling to proactive operational control through a professional, end-to-end BI implementation.

Who is xudo not for?

I am not a good fit for companies looking for “quick fixes,” bug-hunting in legacy systems, or ongoing maintenance of BI debt built by others. I also generally do not work with firms that haven’t yet reached the “operational pain” stage (where they can still manage without automated BI) or those looking to outsource their data problems without active leadership involvement. My focus is on transformation and creating a Single Source of Truth from the ground up.

How do you charge?

I offer three clear engagement models tailored to different needs: A-to-Z Projects for complete implementations from strategy to dashboards; a Strategic Advisory Retainer for ongoing technical leadership and fractional “Head of Data” oversight; and Staff Augmentation at a day rate of €1,000/day for specific engineering support. All models are designed to provide senior-level expertise without the overhead costs of a large agency. Read more here.

Looking for more on Data Philosophy?

I have written a short eBook on how to apply philosophical frameworks to modern data challenges.

Download “Philosophers in Data” below: