Turning Data into Design Decisions: How Business Intelligence Can Overcome the Challenges of Sustainable Product Development
As part of our ongoing work on how Business Intelligence (BI) and Big Data Analytics (BDA) can support companies in strategically managing sustainability risks and opportunities, we’ve been diving deep into recent scholarship on sustainable product development (SPD). One article that particularly caught our attention this week was “Challenges in addressing sustainability within product development” by Katharina Zumach, Sven Wehrend, and Dieter Krause (Proceedings of the Design Society, 2025).
The authors conducted a literature review and research project in the aviation sector, identifying nine categories of challenges that companies routinely encounter when trying to integrate sustainability into product development:
Strategy (management commitment, business model fit)
Operations (integration into existing processes, stakeholder involvement)
Society/Organizational Culture (resistance to change, regulatory uncertainty, customer acceptance)
Collaboration (difficult communication across departments and value chains)
Data Availability (scarce, fragmented, or low-quality data; difficulty quantifying risks)
Resource Allocation (limited money, time, expertise)
Method and Tool Support (selecting and implementing the right methods)
Decision-Making (navigating trade-offs, weak links between sustainability and profitability)
Complexity (technological, methodological, and product-induced).
What struck us most is how directly these challenges overlap with the pain points we see in industry conversations about BI/BDA. Sustainability in product development isn’t just about inventing greener materials or clever circular business models. It’s about managing information under uncertainty—exactly where BI and BDA are designed to excel.
Here are seven ways BI/BDA can help companies address the specific SPD challenges surfaced in this research.
1. Making Data Available When It Matters Most
Zumach and her colleagues describe a fundamental tension: in the early design stages, companies have the greatest ability to shape a product’s sustainability outcomes—but they also have the least reliable data. BI platforms can reduce this gap by aggregating fragmented data sources (supply chain inputs, lifecycle inventories, emissions factors, cost structures) and making them available in near real-time. Advanced analytics can then fill gaps with modeled estimates or scenario ranges. The result isn’t perfect precision, but it is actionable insight at the right moment.
2. Clarifying and Prioritizing Sustainability Criteria
The study notes the lack of unified sustainability criteria as a major barrier—companies face an alphabet soup of metrics, from greenhouse gases to recyclability to social indicators. BI tools can test these criteria side by side, showing how they interact and which ones are most material in a given product context. For example, analytics might reveal that water intensity is the key differentiator in one supply chain, while end-of-life recoverability dominates in another. Instead of leaving teams paralyzed by too many choices, BI turns criteria selection into a transparent, data-backed process.
3. Bringing Trade-offs into the Open
Trade-offs are at the heart of sustainable product design—durability versus recyclability, lightweighting versus toxicity, performance versus cost. Too often, these remain fuzzy or politically charged debates. With BI/BDA, companies can run what-if scenarios and visualize the implications of each choice. A dashboard might show, for instance, how shifting from aluminum to composite materials reduces use-phase emissions but increases recycling challenges. When trade-offs are explicit and quantified, decision-makers can argue less about assumptions and focus more on values and strategy.
4. Directing Scarce Resources Where They Count
The article highlights resource constraints—money, time, and expertise—as persistent barriers. BI/BDA can act like a triage system. By analyzing large datasets, these tools can pinpoint sustainability “hotspots” where small interventions yield big impacts. Automating repetitive tasks like data collection or compliance reporting frees up sustainability experts to focus on innovation. And predictive analytics can help prioritize R&D investments, ensuring that scarce resources are allocated to the initiatives with the highest potential sustainability and business payoff.
5. Building a Shared Language for Collaboration
Collaboration challenges—both within companies and across value chains—were a recurring theme in the research. Engineers, designers, procurement officers, and sustainability specialists often talk past one another, each with their own metrics and priorities. BI dashboards can provide a common ground, translating technical detail into accessible visuals and shared reference points. When a supplier, designer, and CFO are all looking at the same data visualization, alignment becomes far easier to achieve. In effect, BI provides not just information, but also a language of trust.
6. Reducing Complexity Without Oversimplifying
The authors emphasize that sustainability inherently adds layers of complexity to product development—new technologies, new methods, new stakeholder expectations. BI and BDA can’t eliminate this complexity, but they can make it manageable. By surfacing patterns across product lifecycles, mapping interdependencies, and forecasting impacts, analytics provide a structured way of navigating complexity without collapsing it into oversimplified checklists. In this sense, BI acts as a compass: it doesn’t flatten the terrain, but it helps teams find their way through it.
7. Embedding Sustainability in Strategy, Not Just Compliance
Finally, the article notes that many companies struggle to integrate sustainability into core strategy; too often, it remains a compliance or reporting exercise. BI can elevate sustainability by linking product-level metrics to long-term business outcomes such as competitiveness, resilience, and brand reputation. Dashboards that track sustainability alongside cost, quality, and time-to-market signal that it is a target variable, not an afterthought. This reframing transforms sustainability from a burden into an engine of strategic advantage.
From Data to Intelligence, From Challenge to Opportunity
Zumach and her colleagues conclude that sustainability in product development introduces new objectives and new complexity. That complexity, however, doesn’t have to mean confusion. With the right BI/BDA systems, companies can turn sprawling sustainability data into clear intelligence that supports faster, smarter, and more resilient design decisions.
This is exactly where the frontier of practice lies: not just gathering more data, but transforming that data into business intelligence that allows companies to see both sustainability risks and opportunities more clearly. In a landscape defined by regulatory pressure, shifting consumer expectations, and climate urgency, clarity is power.