A Holistic Approach to Data, Analytics & AI
When I was a Chief Data Officer, I got over 100 InMail messages every week trying to sell me products and services. It was exhausting, and all the messages sounded the same - dull, uninspiring, and certainly not trustworthy. The data and AI market is packed with opportunists - and unless you have deep technical expertise and a lot of time for due diligence, you'll end up paying a lot for little (or no) progress.
Fewer than 50% of professional services engagements deliver anything truly meaningful, because it's hard to prequalify technical teams. We use our deep background in quality and supply chain management to fix that.
Data integrity requires process integrity. Solving a data problem means you need to find - and solve - the process problem underneath. That's what we help you do.
Qzuku comes from the German words for quality (Qualität) and future (Zukunft). We're your on-call Chief Data Officers who can help you think through and solve tough data and AI challenges. With our partners, we help our clients:
✅ Improve the processes that amplify visibility & data integrity
✅ Construct and execute pragmatic Data & AI strategies that visibly benefit the business
✅ Build production AI/ML models in R and Python that are easy for your developers to incorporate
✅ Improve performance management processes, especially for highly skilled MS & PhD employees
✅ Build more inclusive technical teams that are empowered for high performance
✅ Support individual performance and career development through strengths-based mentorship
The mission of Qzuku is to help companies like yours achieve breakthroughs in data quality and analytics/AI excellence by taking a process approach. Whether you're looking for contractors or full-time employees, or just trying to upskill or reskill your home team, our vetted data leaders (each averaging 17 years experience) can help you avoid wasting time and wasting millions. Let us be your sherpas... we'll make sure you don't die on whatever data or AI mountain you need to scale next.
