Building analytics success isn’t just about tools and infrastructure. It’s also about uniting technical and business teams. At the 10th edition of the Data Innovation Summit, Jonas Dieckmann, a Data Engineering Leader at Philips, will share how organizations can bridge this gap!
With nearly a decade of experience, Jonas has helped drive smarter, data-informed decisions at Philips, where he leads a global team focused on building scalable data platforms. His session will explore how fostering collaboration, addressing data quality, and aligning goals can transform analytics efforts.
We sat down with Jonas to learn more about his journey in data and analytics.
Hyperight: Can you tell us more about yourself and your organization? What are your professional background and current working focus?
Jonas Dieckmann: I’ve been passionate about data and analytics for nearly a decade now. Currently, I work at Philips, where we develop medical equipment for hospitals, including ultrasound, CT, and MR machines. My focus is on using data to drive commercial, customer, and digital insights – essentially helping the organization make smarter, data-informed decisions.
Today, I lead a global data engineering team, and one of the unique aspects of my role is that our team works remotely from various parts of the world. Right now, my main priority is building a reliable and scalable data platform to support our organization’s analytics needs. Beyond my work at Philips, I also teach data-related topics at a university near Hamburg, which gives me the opportunity to share my knowledge and stay connected with the academic side of the field.
Hyperight: At the Data Innovation Summit 2025, you will present on aligning technical and non-technical analytics teams for better data outcomes. What can the delegates expect from your presentation?
Jonas Dieckmann: In analytics, the biggest challenges aren’t data availability or technical infrastructure – they’re people-related. Whether it’s adapting to new tools, identifying the right problems, or managing change, humans are often the bottleneck. Technical teams usually focus on building systems and tools, but they often lack the domain knowledge that analysts or stakeholders have. This gap can lead to misunderstandings or missed opportunities.
My presentation will explore how we transformed our data engineering team to work more collaboratively with analysts and product owners. Instead of just solving tickets, we’ve started solving problems together and creating meaningful solutions.
Hyperight: Jonas, what are some common communication barriers between technical and non-technical analytics teams, and how can they be overcome?
Jonas Dieckmann: One of the biggest barriers is the difference in their “language” and skills – technical teams and non-technical teams often speak in completely different terms. Analysts or stakeholders might assume certain domain knowledge is obvious, but for technical teams, that context isn’t always clear. Without a shared understanding, there’s a risk of making the wrong assumptions.
Another issue is time pressure and top-down pushes that lead to unrealistic plans or over-promising. Clear and honest expectation management is essential to avoid this. Open communication, regular check-ins, and an effort to build mutual understanding can help bridge these gaps.
Hyperight: What strategies do you recommend for aligning business objectives with advanced data architectures in an organization?
Jonas Dieckmann: Alignment is a two-way street. Too often, data teams ask, “What do you need?” while business teams ask, “What can you deliver?” Instead, both sides need to listen to the other side of the house. Data teams should raise awareness of what’s possible – but also be upfront about limitations. Meanwhile, business teams need to define their goals based on the impact they want to achieve. From there, data teams can create a roadmap that shows how data and technology can support those goals. The most important thing to remember is that stakeholders’ problems are always business problems, not just data problems.
Hyperight: What role does data quality play in bridging the gap between technical and non-technical teams?
Jonas Dieckmann: Data quality is absolutely crucial for building trust in analytics. However, there’s a common misconception that ensuring data quality is solely a technical responsibility. While tech teams can perform checks for completeness and anomalies, assessing data quality from a business perspective is a task for non-technical teams. They need to define what “good data” means in the context of their work. Strong collaboration between both sides is necessary to ensure data quality meets technical and business needs alike.
Hyperight: What are the most common pitfalls in building robust data pipelines, and how can they be avoided?
Jonas Dieckmann: One common pitfall is designing pipelines without scalability in mind. Solutions that work for smaller datasets or limited use cases often fail when the volume, velocity, or variety of data grows. To avoid this, it’s important to think ahead and design systems that can handle future demands.
Another challenge is poor communication between teams. When developers don’t fully understand the data’s context or how it will be used, it can lead to inefficiencies or errors. Establishing strong communication channels and regular feedback loops can help.
Lastly, neglecting monitoring and observability is a major issue. Pipelines can break for many reasons, from schema changes to data anomalies. Setting up robust monitoring and alerting mechanisms can prevent small issues from turning into major disruptions.
Hyperight: What are the long-term benefits for organizations that successfully align their technical and non-technical analytics teams?
Jonas Dieckmann: When technical and non-technical teams work well together, the whole organization benefits. For one, it leads to better solutions – technical teams bring the “how,” while non-technical teams provide the “why.” Together, they can create data products that solve real business problems. It also builds trust in data. When stakeholders see that their input is valued and that technical teams deliver on promises, they’re more likely to embrace data-driven decision-making. Over time, this alignment fosters a culture where data isn’t just a tool – it’s a strategic asset that drives innovation and growth.
Hyperight: What data and analytics trends do you expect to see in the next 12 months?
Jonas Dieckmann: One trend I see gaining momentum is the shift from dashboards to actionable insights. Teams are moving away from static reports and focusing on real-time, automated solutions that embed insights directly into workflows. Another trend is the growing focus on ethical AI and responsible data usage.
With regulations tightening and public awareness increasing, organizations are putting more effort into ensuring their AI models are fair, transparent, and explainable. Finally, the rise of composable data platforms will continue. Instead of relying on monolithic systems, companies are adopting modular architectures that allow them to adapt quickly to new business needs and technologies.
Don’t miss Jonas’ presentation at the Data Innovation Summit 2025! He’ll dive into bridging teams and uniting analytics for better outcomes. He’ll also share insights to help you break down silos and elevate your analytics. If you’re passionate about turning data into impact, this is a session you won’t want to miss!
Articles written by Jonas…
Focus on Impact: Pitfalls and Maturity Levels in Data Analytics. Every day, many challenges arise for data and AI professionals. With the increasing impact of technology, analytics teams must continue to prove their impact on businesses. Working in data analytics at Philips, Jonas Dieckmann shares the most common observations of commercially focused data teams.
Add comment