Stefan Lautenschlager from NETZSCH: Cloud First, Not Cloud Only—Moving to the Cloud was a Clear Strategic Decision for Us

Ivana Karhanová: Let’s start by framing company story. What’s your core business?

Stefan Lautenschlager: Our company consists of three business units. The largest is Pumps and Systems, operating globally. Our pumps transfer fluids—everything except water—like fish oil, gas, milk, or even living fish. They also support grinding systems. The second unit, Grinding and Dispersing, focuses on breaking down materials into smaller components and mixing them—things like gold, granite, coffee beans, paint, and coatings. The third and smallest unit is Analyzing and Testing. This division produces lab equipment for material testing and analysis. For instance, NASA uses our equipment to evaluate metal deformation in rockets.

Ivana Karhanová: The company manufactures physical products. Why move to the cloud?

Stefan Lautenschlager: Technology increasingly supports all business processes—purchasing, sales, and production. It generates data, which we analyze to improve operations. Also, our products are evolving. In the past, customers bought a pump and called us for repairs or replacements. Now, many want to buy the service—having liquid transported from point A to B—which requires advanced data and analytics.

Ivana Karhanová: So you’re shifting from selling products to selling services?

Stefan Lautenschlager: That’s the potential. We still sell products, but the service model could become a significant shift in our business.

Ivana Karhanová: Moving to the cloud isn’t easy—or cheap. What was the tipping point for this decision?

Stefan Lautenschlager: We’ve long used SAP for business intelligence, specifically BW, while monitoring other platforms like AWS, Google Cloud, and Microsoft Azure. The trend is clear—cloud providers are heavily investing in new features, which largely target the cloud. Cost transparency was another factor. While on-premises systems seem straightforward, hidden costs—like security, backups, and network maintenance—add up. Cloud services make these costs more predictable with a pay-as-you-go model.

Ivana Karhanová: You chose the Microsoft Intelligent Data Platform. Was it hard to convince management?

Stefan Lautenschlager: Not really. We have two strategic partners: SAP for ERP systems across all units, and Microsoft for cloud-related solutions. When I joined, we had 12 front-end tools, from SAP Lumira to Power BI, plus countless PowerPoint and Excel files. My first goal was consolidation. We chose Power BI as our primary tool, and it’s now established across the company. While there are technical challenges, such as compatibility between SAP BW and Microsoft, Microsoft’s features and AI tools—like Fabric and integrations with ChatGPT—were decisive. They offered the most advanced options for business intelligence and analytics in the cloud.

Ivana Karhanová: Did you calculate ROI for the move, or was it purely strategic?

Stefan Lautenschlager: It was both. Our IT strategy is cloud-first, not cloud-only, so we still use some on-premises systems. We compared cloud providers and calculated ROI, but the strategic need for cloud adoption was clear.

Ivana Karhanová: How challenging was it to calculate ROI across hyperscalers?

Stefan Lautenschlager: It’s always tricky. We worked with Adastra to calculate total cost of ownership. They helped us estimate requirements—like virtual machines, Azure Synapse, Data Factory, and a data lake—then mapped out costs over several years. While helpful, these estimates are never perfect, especially with faster-than-expected adoption and growing data volumes.

Ivana Karhanová: What tangible results are you seeing already?

Stefan Lautenschlager: Yes, even though we started only a year and a half ago. For instance, we improved data quality by 24% using machine learning to identify errors, like mislabeled spare parts. We visualize these issues in Power BI for immediate correction.

Ivana Karhanová: How do you measure data quality?

Stefan Lautenschlager: We use KPIs like timeliness, completeness, and accuracy. For example, ensuring material data is correct involves checking weights, costs, and materials. Each dataset—materials, customers, or suppliers—has tailored quality metrics.

Ivana Karhanová: You mentioned consolidating reporting tools. Is Power BI now your sole tool?

Stefan Lautenschlager: Yes, we’re phasing out others by 2025. We train employees and promote Power BI exclusively. It’s part of broader data culture initiatives.

Ivana Karhanová: What are those initiatives?

Stefan Lautenschlager: We focus on three pillars: business (data quality, ROI, use cases), technology (tools and platforms), and organization (training and communication). We host monthly Business Intelligence & Analytics Community sessions where employees, Microsoft, and SAP share insights. This year, we’re launching mini-podcasts and short educational videos on topics like machine learning and reporting, shared internally via Microsoft Stream and Teams.

Ivana Karhanová: Do employees engage with these resources?

Stefan Lautenschlager: Yes, around 50–100 employees join live sessions, with 30–40 watching recordings later. Given many colleagues work in production without daily data needs, this engagement is significant.

Ivana Karhanová: What challenges did you face?

Stefan Lautenschlager: The biggest challenge was buy-in. Employees were attached to legacy tools like Zoho and Excel. Replacing them with Power BI offered no immediate benefit to users and even disrupted workflows initially. Management support from the CFO, IT director, and others was crucial. Success stories—like widely used reports—also helped build trust.

Ivana Karhanová: Microsoft FastTrack and Adastra played key roles, right?

Stefan Lautenschlager: Absolutely. Adastra helped set up resources, pipelines, and DevOps while guiding our transition. Microsoft FastTrack provided deep technical expertise, like protocols for integrating SAP BW with Azure. Their support ensured a smooth start and internal alignment.

Ivana Karhanová: Okay. So to gain their trust.

Stefan Lautenschlager: Right, exactly.

Ivana Karhanová: What’s your vision now? At the beginning, you mentioned the idea of selling data as a product or service. What does that mean for your core business?

Stefan Lautenschlager: First, we’re focusing on serving our niche group. We’re not selling any external products yet, but we’ve explored the idea. For example, we’ve considered selling an internal solution—a detector that identifies third-party activities undermining our spare parts business. While it works well for us, its broader application is uncertain. However, I’m confident we can develop similar solutions as software-as-a-service products. Companies could bring their data, use our algorithms, and generate their own benefits.

Ivana Karhanová: Does that align with a vision of data mesh or data democracy?

Stefan Lautenschlager: Internally, it’s a bit different. Externally, we’re considering that route, but internally, we’re still centralizing. We started with sales, as everything revolves around an order, but we’re now expanding into production, financials, and purchasing. Our focus is on scaling the solution, integrating more data into our platform, and delivering use cases quickly. A data mesh is a long-term vision. Some teams are already using self-service analytics like Power BI and basic SQL. These early adopters could progress to more advanced tools, but for now, our team in Brazil handles most of the work. Self-service analytics in the front end is developing gradually.

Ivana Karhanová: How long do you think it will take to achieve a more decentralized or democratized approach to data?

Stefan Lautenschlager: It’s tough to say. Economic conditions in Germany and challenges in hiring tech talent complicate things. Selb is too remote, and Freiburg competes with bigger hubs. Most of our growth is international. With skilled or trainable people, we could see quick progress. Our goal this year is to showcase initial success cases to inspire broader adoption, which could snowball over the next few years.

Ivana Karhanová: Regarding external data monetization, what are the use cases?

Stefan Lautenschlager: A mix of IoT data and analytics is key. For instance, instead of selling pumps, we could offer a service to transport liquid from point A to B, with real-time monitoring and remote control. Customers would pay based on throughput, like a cloud pay-as-you-go model. For example, Coca-Cola could pay for transporting 100 hectoliters per hour rather than buying and maintaining pumps. This concept has been discussed for years, but advancements in real-time data, sensors, and cloud technology bring it closer to reality.

Ivana Karhanová: What would be the game changer to make this happen?

Stefan Lautenschlager: The main challenge is sensor costs. For larger pumps in remote locations, like a dam in Brazil, IoT solutions make sense. These systems provide real-time monitoring, shutting down overheating pumps remotely. However, for smaller pumps, sensor costs can outweigh the pump price, making it less viable. Reducing sensor costs would be a significant game changer.

Ivana Karhanová: As you’re halfway through this digital transformation, what are your main KPIs?

Stefan Lautenschlager: Adoption rate is crucial. We track how often our reporting tools are used, as this indicates whether the business is leveraging automated systems instead of manual processes like extracting data from SAP into Excel. Project delivery times and data quality improvements are also key metrics. Ultimately, the goal is to ensure widespread adoption and measurable impact.

Ivana Karhanová: What lessons have you learned so far?

Stefan Lautenschlager: One lesson was managing expectations. Coming from a larger organization with cloud infrastructure in place, I underestimated the challenges of scaling security and overcoming firewall issues. While our new team resolved these problems, timelines had to be adjusted. Another lesson was simplifying communication. Data professionals often focus on technical details, but business leaders care more about impact. One of our data scientists transitioned from academic precision to emphasizing business outcomes, which has been transformative.

Ivana Karhanová: What advice would you give to other enterprises on a similar journey?

Stefan Lautenschlager: Research thoroughly before deciding on cloud adoption or providers. Look into reports from institutes like Gartner or Forrester to understand market trends. Cloudification is essential for most industries, especially in manufacturing. While some cases may still require on-premise solutions, investing in cloud infrastructure offers greater scalability and innovation potential.

Ivana Karhanová: Thank you, Stefan, for sharing your insights from this fascinating journey to cloudification.

Stefan Lautenschlager: Thank you so much.