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Top enterprise process mining challenges, ways to solve them
Process mining challenges might seem overwhelming but addressing them head-on can help organizations optimize their digital transformation initiatives.
As a part of broader digital transformation initiatives, process mining can help streamline enterprise operations and improve efficiency. Yet organizations often face significant process mining challenges that can derail those outcomes.
Process mining analyzes event logs from ERP, CRM and other enterprise software to discover hidden bottlenecks, redundancies and other inefficiencies, then highlights opportunities for improvement. Many companies are implementing process mining enterprise-wide, and others have rolled out limited-scope initiatives. However, organizations can face several issues once they start using process mining tools, including change management, ROI measurement and compliance concerns.
Here we examine some of the top process mining challenges and practical strategies for overcoming them.
1. Data quality and availability
Process mining requires high-quality data that is readily accessible and easily integrated for analysis. Unfortunately, using legacy systems often leads to information silos, which make event logs incomplete, inaccurate or difficult to integrate with modern platforms. Incomplete records or inaccurate data can distort process maps, leading to unreliable insights.
Data harmonization is also frequently a challenge. In complex environments involving multiple ERP and CRM systems, data is often in different formats. This can further complicate analysis.
CIOs and other leaders should ensure that data governance frameworks are implemented and that log data is aggregated across various systems using ETL (extract, transform, load) tools that can harmonize data formats on the fly. Routine data cleansing, automated validation and AI-driven anomaly detection can help identify and correct issues early.
Fostering a culture of data quality and stewardship can help ensure that the raw data for process mining provides accurate and reliable results.
2. ERP system complexity
ERP systems are often essential for enterprise operations, but their modular nature can introduce challenges to process mining.
Integrating data across multiple modules can be cumbersome, especially when an organization has extensively customized systems. Modular ERP systems often make it difficult to get a complete view of end-to-end enterprise processes and identify inefficiencies.
Modern integration tools can mitigate these kinds of challenges and enable organizations to map ERP data into standardized formats. These tools can help simplify data access and enable scalable process mining across the enterprise.
3. Change management and skills gaps
Like many other IT initiatives, process mining implementations lead to change, which can be met with resistance from users who are skeptical of the technology's potential or who fear increased oversight or job displacement. A lack of employee expertise in interpreting process maps or applying mining tools can further hinder a process mining initiative.
Proactive change management can help ensure that process mining challenges are addressed head-on, users are educated about process mining's benefits and employees receive the necessary training.
Like many other enterprise initiatives, publicizing quick wins can be helpful. Piloting process mining on a high-impact financial use case, such as invoice processing, is a good starting point.
4. Measuring ROI and aligning to business goals
Process mining can deliver meaningful ROI, yet project leaders often find it difficult to measure and report those results.
Defining clear objectives that align with strategic priorities, such as cost reduction or customer satisfaction, can be helpful. Project leaders should establish KPIs and report progress regularly to project sponsors. Some examples of potential process mining KPIs include reducing cycle times by 20% or cutting compliance costs by 15%.
Dashboards can be helpful for tracking progress and can display before-and-after comparisons to demonstrate positive ROI, which can lead to continued executive support and investment. Additionally, regular KPI reporting can identify areas that require improvement.
5. Compliance and security concerns
Process mining often uses sensitive data, which could lead to non-compliance with regulations such as GDPR, HIPAA, the Gramm-Leach-Bliley Act or the Sarbanes-Oxley Act. Event logs containing personally identifiable information (PII) that are replicated to another enterprise repository could increase the risk of data exposure to unauthorized parties.
A security first mindset can help, including applying strong data governance, implementing role-based access controls and using anonymization techniques to remove any PII while still preserving the data's analytical value. Prioritizing ethical data use and transparency can safeguard operations while still enabling companies to use process mining's capabilities.
James Kofalt is the president of DX4 Research, a technology advisory practice specializing in ERP and digital transformation. He spent 16 years at SAP working with SME business applications and was a product manager for integration technology at Microsoft's Business Solutions division.
