Process mining isn't a novel concept. Since its emergence in 2011, process mining has been undergoing continuous improvement as leading providers like Celonis and UiPath consistently refine and expand their suite of tools.
The process mining market is expected to reach
The definition of process mining
Process mining involves extracting and utilizing data to explore and improve enterprise workflows. The reason why it is important is directly connected to the process most common reason for failed digital transformations—lack of vision and direction, as noted by Deloitte. Organizations resist change when they can’t see the entire journey clearly and thus fail to align with leaders' thoughts.
Business leaders may not provide specifics, resulting in unclear transformation goals, undefined timeframes, and a lack of transparency for employee motivation within updated workflows. Low visibility arises from the abundance of enterprise data and processes requiring detailed sorting and examination. However, manual handling is time-consuming, error-prone, and leads to blind spots rather than insights.
Due to this, process mining becomes a game-changer by collecting log event data, transforming it into a graph that visualizes a specific enterprise process and its cycle. By using process mining technology, executives and decision-makers can view process performance in different scenarios and gain insights on potential areas of improvement.
Process mining value
Process mining offers new opportunities across the enterprise and brings efficiency to various departments
Marketing and sales productivity boost
Process mining allows breaking down sales processes, identifying weak points. It also delivers insights into lead and client interactions.
- Monitors sales process: visualizing processes and interactions for KPI tracking.
- Designs agile sales process: defining success criteria for a flexible process.
- Increases conversion rates: identifying and addresses stages limiting conversion.
- Optimizes customer experience: defining activities impacting customer choices.
- Improves marketing strategies: evaluating lead responses to marketing campaigns.
Compliance and auditing facilitation
According to an EY survey, process mining can optimize control-related activities, including auditing and regulatory compliance, in about
- Accelerated issue discovery: uncovering the reasons causing operational issues through detailed workflow visualization.
- Transparent financial reporting: analyzing event logs for financial transactions.
- Improved financial control testing: identifying control problems and non-compliance issues for consistent auditing instead of periodic assessments.
- Cost reduction: reducing manual tasks and optimizing employee time management.
How to get max value from process mining
Over 12 years, process mining has evolved from a concept to a core technology embraced by vendors like Celonis and UiPath. As new approaches emerge, the landscape of implementing process mining technology will see changes. Despite this, executives and decision-makers should focus on the following essentials.
Form a team
Process mining was once a collective effort, involving data gathering and employee surveys for
- Executive: sets process mining project goals and secures funding and resources.
- Process owner: is responsible for end-to-end process execution and productivity, defining desired processes, and monitoring.
- Project manager: handles all aspects of the process mining task, selects technology, manages team communication, and reports progress.
- Business analyst: analyzes process mining data, identifies opportunities and risks, proposes solutions, and monitors process changes.
- Data engineer: extracts, transforms, and merges log event data for creating process graphs.
- BI engineer: connects data with users, ensures a smooth user experience, and facilitates data visualization for process graphs.
- Infrastructure engineer: ensures proper integration of the process mining solution within the enterprise structure, considering security and compliance.
Check data system health
For any data-driven process to succeed, having relevant, validated, and healthy data is essential. To ensure fruitful process mining outcomes, executives and project managers must verify the availability of necessary event data and sufficient data history.
Identify the need
A successful process transformation begins by recognizing a clear need. To avoid blind spots, business leaders should thoroughly investigate the issue prompting the idea for process automation, considering various levels. The depth of research directly impacts the final results and process mining goals.
Establish KPIs
After understanding enterprise needs, executives and stakeholders move on to the crucial step of defining primary key performance indicators. It involves specifying data sources for insights extraction, considering data dimensions (region, supplier, etc.), and estimating costs and expected outcomes.
Develop process maps
For successful transformation, creating detailed and multilayered process maps is essential. These maps visually represent the process, facilitating conformance checks to find deviations, process violations, and bottlenecks.
Act on insights
Contrary to the belief that process mining is only for insight-gathering, it can also contribute to immediate improvements. After identifying opportunities through process graphs and checks, it's beneficial to address issues promptly instead of postponing for later stages or automation.
For example, process owners and analysts can streamline processes before implementing intelligent automation, ensuring a smoother transformation. Addressing issues upfront enhances the success of subsequent automation efforts.
How to secure consistent growth
Intelligent automation is a continuous process that constantly demands insights and awareness of enterprise operations. To improve process mining productivity, there are several important guidelines for business leaders to follow.
- Stay updated on trends. Decision-makers should regularly update their knowledge of process mining and intelligence trends, connecting with peers and consulting digital partners for valuable insights.
- Foster innovation culture. Promoting innovation understanding among employees reduces resistance to change. Leaders should consistently assess employees' perceptions of transformation, ensuring friction-free initiatives with clear, trackable results.
- Prioritize data hygiene. Maintaining data health by preventing bottlenecks and silos. Investing in advanced data validation methods, and constantly updating and improving security measures for smooth integration across systems.