process.science

The implementation process for process.science is designed to be lean and results-oriented, typically spanning just a few days for a standard 'Process App' setup. The journey begins with the 'Discovery' phase, where relevant data sources (such as SAP or Microsoft Dynamics) are identified. Next is the 'Extraction' phase, where raw event logs are pulled from these systems. The third and most critical step is 'Transformation,' where the process.science Event Log Transformer (using SQL or SSIS) cleans and organizes the data into the required format (Case ID, Activity, Timestamp). Once transformed, the data is loaded into the process.science visual within Power BI or Qlik. Users then enter the 'Analysis' phase, where they can visualize the actual processes, perform conformance checking, and identify bottlenecks. The final step is 'Optimization,' where insights are translated into actionable business changes. This streamlined approach minimizes IT overhead and ensures that companies see tangible business value almost immediately after deployment.

process.science offers extensive customization capabilities that allow it to fit into highly unique enterprise environments. At the visualization level, users can customize themes, colors, and KPI mappings directly within the Power BI or Qlik interface. At the data level, the software is highly extensible; because it uses SQL Server Integration Services (SSIS) and standard SQL for its ETL processes, data engineers can build complex, custom transformation logic to handle multi-source data or unique business rules. The Enterprise version also allows for the creation of custom 'Content Packs' and industry-specific dashboards. Furthermore, process.science supports API extensibility, enabling organizations to trigger external workflows or alerts based on process mining findings. This 'developer-friendly' approach ensures that the tool can scale from simple visual discovery to a deeply integrated part of an automated enterprise architecture.

Transparency is a core value for process.science, and they aim to minimize hidden costs. The base subscription covers the license for the visual and the standard transformers. However, customers should be aware of potential additional costs related to the underlying BI platform (e.g., Power BI Pro or Premium licenses) and any cloud infrastructure costs (such as Azure hosting) if they choose a cloud-based deployment. For large enterprise implementations, there may be professional service fees for custom SSIS package development, complex SAP connector configurations, or white-glove onboarding sessions. Training through the online Academy is free, but on-site workshops or personalized coaching sessions are quoted separately. There are no 'maintenance' fees beyond the subscription, and updates are included in the active license price.

Training is a robust component of the process.science ecosystem, primarily delivered through the 'Process.Science Academy.' This free online platform provides comprehensive learning paths for different user roles, including 'Analyst' (focused on interpreting process maps and variant analysis) and 'Data Engineer' (focused on ETL, SSIS packages, and data transformation). Each course includes video tutorials, documentation, and practical exercises. Upon completion, users can earn certifications that validate their expertise in BI-integrated process mining. For enterprise customers, process.science also offers live webinars, dedicated Q&A sessions with technical experts, and on-site training workshops for larger teams. This multi-layered training approach ensures that all users—from business managers to data scientists—are equipped to derive maximum value from the tool.

Security is one of the strongest pillars of the process.science offering. Since the tool is an integrated visual within Power BI or Qlik, it inherits the enterprise-grade security posture of those platforms. Data stays within the customer's own environment (on-premise or within their secure tenant in Azure or Qlik Cloud), meaning process.science does not host your sensitive ERP data on its own servers. This architecture inherently supports GDPR compliance and satisfies internal corporate security audits. For data transformation, they utilize secure SSIS packages and encrypted SQL connections. The software itself follows modern secure coding practices and is regularly updated to address emerging vulnerabilities. For organizations in highly regulated sectors like finance or healthcare, this 'data residency' model provides a level of security that standalone SaaS-based process mining competitors often struggle to match.

process.science maintains a rapid release cadence, typically providing monthly updates to their custom visuals and transformers. These updates often include performance optimizations, new visualization features (such as enhanced drill-downs or swim lane logic), and updated connectors for systems like SAP or Salesforce. Deployment of updates is straightforward: for Power BI, users can simply update the visual from AppSource; for the Enterprise SSIS packages, the company provide updated components that can be seamlessly swapped out. They follow a version support policy that ensures compatibility with the latest releases of Power BI Desktop and Service, as well as Qlik Sense Cloud and On-Premise versions. Major feature launches, such as the AI Chat Bot or Task Mining components, are accompanied by detailed documentation and training videos in the Academy.

Under the process.science model, the customer retains 100% ownership and control of their data at all times. Because the software operates as a visual component within the customer's existing BI environment, the raw data, the transformed event logs, and the final dashboards remain within the customer's infrastructure. There are no restrictive data lock-in policies. If a customer chooses to stop using the software, all their original data remains in their ERP and BI systems. Furthermore, since the data is stored in standard SQL or BI formats (like Power BI's .pbix or Qlik's .qvd), users can easily export their data or analysis results into CSV, Excel, or other formats for further reporting. This approach ensures maximum data portability and sovereignty, aligning with the highest standards of data protection and corporate governance.

The software is designed to scale effortlessly with organizational growth. For small teams, the 'Process Analyzer' visuals can handle tens of thousands of rows with high responsiveness on a standard desktop. As organizations grow, process.science transitions into a high-performance enterprise architecture using SQL Server Integration Services (SSIS) for automated, server-side data processing. This allows the system to analyze millions of event log rows across multiple global regions without compromising on speed. The Enterprise version supports multi-user environments with robust role-based access controls inherited from the host BI platform (Power BI/Qlik). Whether scaling across users, departments, or data volumes, the underlying BI infrastructure handles the load, while process.science visuals ensure that the complex process data remains clear and actionable for every stakeholder.

The terms and conditions for process.science are structured around a subscription-based model, typically billed annually, though monthly options exist for smaller tiers. Contracts are transparent and clearly define the user limits or data row limits associated with each plan. Renewal is generally automatic unless cancelled with notice (typically 30-90 days before the period ends, depending on the specific enterprise agreement). The software includes a Service Level Agreement (SLA) for enterprise customers, ensuring high availability of support and regular security updates. For the free visuals, the terms are governed by the respective marketplace (e.g., Microsoft AppSource) terms of use. Cancellation policies are fair, ensuring that customers are not trapped in long-term commitments that no longer serve their business needs.

As a German company, process.science is built with a foundation of strict compliance, particularly regarding GDPR. They assist their customers in achieving compliance by providing a tool that does not require data to leave the 'Safe Harbor' of the customer's own IT infrastructure. While the software itself is a component, the company ensures its development processes align with ISO 27001 principles for information security management. For clients in specific sectors, the tool's architecture supports HIPAA (Healthcare) and SOC 2 (SaaS) requirements by leveraging the compliance certifications of the host platforms (Microsoft Azure and Qlik Cloud). They also provide documentation to support customer-led Data Protection Impact Assessments (DPIA), ensuring that the implementation of process mining is fully documented and compliant with both local and international data protection laws.