process.science

process.science is a specialized software provider based in Germany that delivers advanced process mining tools directly integrated into leading Business Intelligence (BI) platforms such as Microsoft Power BI, Qlik Sense, and Tableau. Unlike traditional standalone process mining solutions that often create data silos and require complex separate infrastructures, process.science functions as an integrated extension of the user's existing BI environment. This approach allows organizations to leverage their current investments in data visualization and analytics while gaining deep, data-driven insights into their business processes. The tool works by transforming event log data from ERP, CRM, and SCM systems into interactive process maps that reveal the actual flow of operations, highlighting bottlenecks, deviations, and inefficiencies. The core functionality of process.science revolves around three pillars: Process Discovery, Conformance Checking, and Enhancement. Through automated discovery, the software reconstructs the 'as-is' process from raw data without needing predefined models. Conformance checking then compares these actual process flows against 'to-be' target models to identify non-compliant behavior or operational risks. Finally, the enhancement module provides the analytical depth needed to optimize lead times, reduce costs, and improve overall process quality. By utilizing the data already present in systems like SAP, Microsoft Dynamics, or Oracle, process.science enables real-time process monitoring and transparency across the entire organization without the need for additional software silos or high-risk system interventions.

The primary competitive advantage of process.science is its 'native integration' philosophy, which stands in stark contrast to market giants like Celonis or Signavio. While competitors often require customers to purchase and maintain a massive, separate software stack, process.science allows companies to keep their data within their existing, secured BI environment (Power BI or Qlik). This results in a significantly lower Total Cost of Ownership (TCO) because there are no additional infrastructure costs, and training requirements are minimized as users stay within a familiar interface. Another critical differentiator is the 'Time to Value.' Traditional process mining projects can take months to configure; however, process.science customers frequently report going from initial installation to tangible business insights within just a few days. This is made possible by their predefined 'Process Apps' and industry-specific content packs for common processes like Procure-to-Pay (P2P) and Order-to-Cash (O2C). Security and data sovereignty also represent a major edge. Because process.science doesn't require data to leave the customer's tenant (especially in on-premise or Azure-hosted Power BI setups), it inherently satisfies the strictest GDPR and corporate security requirements. Finally, their transparent and granular pricing model—starting as low as €4.91/user/month—makes process mining accessible to departments and smaller subsidiaries that were previously priced out of the market by enterprise-only competitors. This combination of speed, cost-efficiency, and security makes it the preferred choice for BI-centric organizations seeking a pragmatic approach to process intelligence.

• SMB
• Mid-Market
• Enterprise