A data engineer brings metric-driven clarity to sustainability

When Stanislav Kazanov stepped into his role as Head of Sustainability at Innowise Group in January 2024, he brought an unique yet increasingly valuable perspective to the position: close to a decade of experience as a data engineer. This background has shaped his approach to one of the pharmaceutical industry’s most pressing challenges—how to innovate in terms of how the industry measures and tracks environmental impact within a heavily regulated sector.

“Coming from data engineering, I know the impact of clear, measurable information,” explains Kazanov. “In sustainability, it’s actually easy to get lost in big-picture ideas. So I do my best to bring that data focus to Innowise’s sustainability projects and break the environmental challenges into manageable pieces.” Kazanov, whom we profiled on Pharmaceutical Processing World and is speaking at PHARMAP in Berlin, applies the following data engineering principles to pharmaceutical sustainability:

Building robust data pipelines

Just as data engineers create systems to collect, validate, and process information, Kazanov’s team develops sensor networks throughout pharmaceutical facilities. “Pharma’s GMP requirements are a real asset,” he points out. “The thorough validation process ensures the environmental data we collect is highly reliable.”

This focus on data quality prevents the “garbage in, garbage out” problem that plagues many sustainability initiatives. “We’re building software to track things like energy use and waste, so we can base our strategies on hard numbers, not just good intentions,” Kazanov says.

Designing for scalability

Data engineers build systems that can grow and adapt to increasing demands, a principle Kazanov applies to sustainability infrastructure. “Today, real-time data analytics provides a critical advantage for pharma companies,” he notes. “They get a constant stream of information on everything from emissions and resource use to critical quality parameters like temperature and contamination.”

This comprehensive monitoring capability allows manufacturers to address issues immediately while maintaining compliance. “This allows pharma manufacturers to address issues immediately, adapt to changing ESG expectations, and maintain GMP compliance all at the same time,” Kazanov explains.

Bridging siloed systems

Breaking down data silos is a common challenge for data engineers, and Kazanov sees similar opportunities in pharmaceutical sustainability. By bringing together different departments around unified sustainability metrics, companies can identify inefficiencies more quickly.

“The key is finding solutions that work—read: make impact—within the existing regulatory framework,” Kazanov says. His approach helps pharmaceutical companies tackle what he identifies as their three biggest sustainability challenges: “optimization of energy use in manufacturing and cold storage, finding new ways to manage waste, particularly chemical byproducts and packaging, and optimization of the supply chain to reduce transportation emissions.”

From monitoring to prediction

“It’s surprising that only 6% of companies use AI for sustainability. There’s so much potential, especially in pharma,” Kazanov notes. “For a quick start, AI can analyze equipment performance to identify energy savings or automate ESG reporting. But the real potential lies in longer-term initiatives: optimization of drug production, supply chain needs forecasts, and even simulation of environmental impact before manufacturing begins.”

For Kazanov, making pharmaceutical manufacturing more sustainable is about having the right data in the first place. “It’s about making sustainability a practical, achievable goal,” he concludes.

Stanislav Kazanov will be speaking at PHARMAP 2025 by BGS Pharmaceutical Events in Berlin on April 14–15, 2025.