Process mining is not a novelty in the rapidly developing world of digital transformation.
Since its inception in 2011, process mining has matured into a technology that undergoes ongoing enhancement, with various leading providers like Celonis and UiPath continuously refining and expanding their suite of process mining tools.
As the process mining market continues its ample growth, expected to reach $12.1 billion by 2028, understanding both the obvious and non-obvious benefits of this methodology becomes a matter of great importance for enterprises seeking a competitive advantage.
Having touched upon process mining in one of our previous articles, we will now dissect the value and purpose of this technology and outline several key steps to securing the most rewarding results.
What is process mining?
Process mining is a general term for methods of extracting and applying data to explore and improve enterprise workflows.
However, to understand process mining as a technology lying at the core of process mining solutions, it's important to recall one of the most common reasons for failed digital transformation—lack of vision and direction. According to Deloitte, organizations often resist change because they don't see the full journey and can follow their leaders' thoughts.
Moreover, business leaders can also be lacking in details, which leads to vague transformation goals, undetermined time brackets, and lack of transparency needed to motivate employees to participate in technology adoption and operate within updated workflows.
Such low visibility directly results from the abundance of enterprise data and processes that need to be sorted and examined in close detail. However, handling such tasks manually is time-consuming, increases the risk of human error, and, consequently, blindspots instead of useful insights.
Given these challenges, process mining becomes the game-changer, collecting log event data from multiple enterprise data sources and transforming them into a comprehensive graph that visualizes a specific enterprise process and its entire cycle (from beginning to end).
In addition, process mining technology allows viewing the process performance in various scenarios, such as executing the same service in different regions or comparing interactions with multiple suppliers, providing decision-makers with highly valuable details on process performance, productivity, and potential areas of improvement.
Process mining: transforming industries from the inside
Process mining is a necessary component of growth and process optimization, which is why it is continuously leveraged by major industry giants such as Siemens, ING Bank, and Coca-Cola. The technology's ability to dissect specific enterprise processes and present a detailed image of the "as is" state makes it a powerful tool that delivers unique opportunities to any specific niche.
Finance and Banking
Facilitating customer onboarding, compliance processes, and fraud detection, boosting transaction efficiency.
Improving administrative workflows, patient care, and clinical processes, analyzing and optimizing resource management.
E-commerce and Retail
Providing better inventory management practices, accelerating order fulfillment, and revealing ways to deliver improved customer experience.
Optimizing production processes, operational efficiency, and supply chain management.
Audit and tax
Introducing more transparency into auditing, improving financial testing control and transaction monitoring, identifying risks and enhancing compliance.
Analyzing customer behavior network operations and discovering new approaches to service provisioning.
Travel and Hospitality
Delivering better experience for guests and tourists by breaking down complex behavior patterns and exploring interactions in all their iterations.
Enhancing supply chain logistics, goods tracking, and introducing improved transportation flows.
Monitoring monitor equipment performance, analyzing maintenance workflows, and streamlining energy production routines.
Process mining: cross-enterprise value
In addition to delivering multiple unique advantages to industries and covering their specific pain points, process mining also unlocks new opportunities across the enterprise, injecting every department with much-needed efficiency.
- Boosting marketing and sales productivity
Process mining allows for breaking down sales processes, identifying weak spots and deviations from the desired state, and revealing new information on interaction with leads and clients.
Sales process monitoring
Visualize processes, sales reps' steps, and client interactions for KPI tracking and comparison.
Agile sales process design
Defining critical success criteria to build a more streamlined and flexible sales process with improved targeting and dynamic engagement options.
Increased conversion rates
Identifying stages where leads escape the sales funnel and don’t convert, addressing issues and activities that limit conversion.
Optimizing customer experience
Defining activities that lead to negative client choices (canceling orders, not proceeding with closing a deal) and discovering factors that affect customer experience.
Improving marketing strategies
Using data gleaned from systems that document lead interactions to evaluate their response to marketing campaigns and strategies.
Within a visualized sales process graph, heads of departments become more aware of factors that limit growth, or where sales guidelines and marketing messages require adjustments. Due to such transparency, key decision-makers can prevent emerging constraints and launch strategies that deliver results.
- Facilitating compliance and auditing
According to an EY survey, control-related activities such as auditing and regulatory compliance are among 19% of areas that can be optimized with the help of process mining—and the potential is truly promising.
While necessary for evaluating enterprise health and mitigating financial risks, auditing is a complicated task, which is usually handled manually and thus can become quite time-consuming. In some cases, overreliance on manual processing and involving too many participants from different departments can make necessary audit data irrelevant and obsolete.
Process mining allows businesses to avoid such an outcome and provides several improvements, streamlining audit processes and enabling regulatory compliance.
Accelerated issue discovery
Uncovering the root causes for enterprise operation issues through detailed workflow visualization.
Transparent financial reporting
Analyzing event logs from systems connected to financial transactions to generate more transparent and verifiable reports.
Improved financial control testing
Identifying control problems and non-compliance issues and enabling consistent auditing instead of periodic assessments.
Saving auditing expenses by reducing the number of manual tasks and optimizing employee time management and workload.
As a rule, subject matter experts refrain from calling a certain technology or solution a silver bullet. But when it comes to regulatory compliance and internal auditing, process mining truly becomes a life-changing solution, leveraging real-time insights and precise dissection of event log data for faster and more accurate audits.
Enabling a future-forward digital solution for a global audit leader
Process mining: how to glean max value from every stage?
More than 12 years after evolving from a concept to technology that lies at the core of tool vendors like Celonis and UiPath, process mining became a bridge between data science and process science, connecting analytics and data with workflows and business visions. The concept continues to change and expand, so more approaches and practices will be introduced. Therefore, there will be many changes and adjustments regarding the best approaches to implementing process mining technology and securing high ROI.
For that reason, executives and decision-makers should focus on the unchanging and resilient must-haves of value-generating process mining.
Assemble a team
Previously, process mining was a collective process that involved gathering data, surveying employees about their daily tasks, and then converting their feedback into actionable process visualization. Process mining is still a collective effort, but instead of engaging employees, it is focused on several engaged personas.
The executive determines the process mining project goals: what processes should be mined and for what business objectives this task is supposed to cover. Additionally, the executive is in charge of funding a process mining project, securing all the budget and resources necessary.
- Process owner
Process owner is responsible for creating and executing the process end-to-end as well as maintaining the productivity of a particular process. Depending on process complexity, a process owner can be an individual (someone from management) or a group of people. Within the process mining framework, a process owner defines what the desired process should be like, identifies criteria for successful analysis, and performs monitoring.
- Project manager
If a process owner covers everything related to the process itself, the project manager handles everything included in the process mining task. Based on the goals of process mining, a project manager selects and onboards technology, business experts, and collects feedback to learn all valuable details about the end goals and the process itself. They also build communication between team members, manage documentation, and report their progress to executives.
Communicating the future is an integral part of any intelligent automation effort, process mining included. A project manager can also be a relationship manager, gathering information from engineers and translating it to executives, providing them with realistic ROI and value expectations, as well as helping them establish functional deadlines.
- Business analyst
An analyst works with the data received from process mining, comparing graphs and identifying opportunities, bottlenecks, and potential risks. The analyst's task is to make sense of the gleaned insights, outlining strategies and approaches to solve the issues found and propose them to stakeholders within a comprehensive business case. In addition, analysts perform process monitoring after changes are implemented, documenting changes in KPI and overall impact on productivity.
- Data engineer
A data engineer extracts log event data from data sources, transforms it, and merges it into a single repository that can then be used to create process graphs.
- BI engineer
An application developer delivers a smooth end-user experience and proper data visualization by connecting the data extracted by the data engineer with users and providing them with the features needed to work with process graphs and build an automation pipeline.
- Infrastructure engineer
An infrastructure engineer ensures that the process mining solution fits and performs properly within the enterprise structure. Their work involves integrating a process mining solution with enterprise systems, taking care of compliance with enterprise security guidelines, and license configuration.
Perform data systems health check
Any data-driven process is only successful when there is relevant, validated, and healthy data to work with. Therefore, ensuring proper data hygiene before starting process mining is crucial to securing rewarding outcomes. Accordingly, executives, project managers, and process owners need to make sure that the necessary event data is available and there is enough data history for process mining execution.
Without sufficient data history, productive process mining is impossible. So, if you don't have enough process data on your hands, you need to wait and accumulate event history. This is crucial to determining deviations that prevent your "as-is" process from becoming "to-be."
Realize the need
The most rewarding process transformation journey starts with a properly identified need. Since it's not uncommon for business leaders to have blindspots in their vision, investigating the issue that prompted the idea for automating the process and any possible levels should be a priority—the thoroughness of research will influence the final results and, ultimately, the process mining goals.
Process mining allows you to realize the problem before it actually becomes an issue for your enterprise. For instance, you can integrate your tool with sales department data systems and see considerable deviations in their performance. You suddenly realize that some of the flows could be better, delivering results you expected—but without doing some digging first, you would have never learned that.
For that reason, we suggest implementing process mining from the discovery stage. Doing so will grant you a more accurate and detailed view of what your enterprise actually needs right here, right now.
Set proper KPIs
Once executives and stakeholders have a deeper understanding of their enterprise needs, they are ready to proceed to the next important step—outlining primary key performance indicators. Such a step is necessary for determining the data sources that will be used for extracting insights, data dimensions (region, supplier, and other factors), and estimating the overall costs of the effort as well as expected outcomes.
Having firm KPIs is vital because you'll face multiple process iterations and scenarios within your project. You must keep an eye on what's important and not lose it out of your sight. This way, you'll be able to identify the outliers and avoid getting distracted by them, focusing on the insights that matter.
Create process maps
Running process conformance checks (finding deviations, process violations, and bottlenecks) requires a visual representation of how the process occurs, which makes creating process maps an essential component of successful transformation.
It’s worth mentioning that a proper process map doesn’t show a linear point-A-to-point-B journey. It should be detailed and multilayered, outlining a detailed record of actions and interactions.
Process automation tools like Celonis and UiPath automatically generate process maps and record workflows, saving business analysis and process owners time they would have otherwise spent on manual mapping, workshops, and interviews. With an automatically generated process map, analysts have all the information displayed in front of them, which allows them to proceed straight to conformance checking and suggesting solutions.
Celonis vs. UiPath: A Comprehensive Process Mining Tool Comparison
Act on insights whenever is possible
The assumption that process mining is an insight-gathering activity, not an improvement-building one, isn't exactly correct. As process graphs and conformance checks reveal potential value, at times, it makes sense to pursue that value at once instead of waiting for the next phase or saving all the work for the automation pipeline.
For instance, after identifying intelligent automation candidates, process owners and analysts can outline ways to streamline processes before automating them.
Intelligent automation doesn't magically transform a low-performing process into a top-performing one. Without addressing issues and constraints first, transforming a bad process can only result in a bad experience. So, removing waste from the process beforehand allows for amplifying its strongest points within intelligent automation.
Advanced process mining solutions are connected to a robust RPA suite, providing expert options for regulating enterprise tasks and processes to improve their execution shortly after detecting improvement opportunities. As a result, they can quickly respond to emerging business needs and introduce process adjustments.
Process mining: tips for securing consistent growth
Intelligent automation is a non-stop, constantly evolving process that would always require more insights and a greater awareness of how enterprise operations work, and more technologies for dissecting them step by step. Following such a tendency, there will always be more to learn about process mining and more solutions to expanding process mining efficiency. For decision-makers, the best way to maintain and amplify the productivity of their intelligent automation and process mining efforts would be to follow several basic principles:
- Awareness of new trends and technologies
Executives, process owners, and project managers must regularly update their knowledge of any innovations in process mining and process intelligence, ensuring they have the most relevant practices. Connecting with fellow business leaders and consulting trusted digital partners can deliver valuable knowledge and provide fresh perspectives.
- Promoting the culture of innovation
Understanding business processes and how they can be improved reduces organizational resistance and helps employees be on the same page with leaders and their vision. However, advocating cross-enterprise change and innovation is a journey, not a destination. Leaders should be committed to checking up with their employees' perception of transformation and innovation adoption—their effort will be rewarded with friction-free initiatives delivering clear and trackable results.
- Maintaining data hygiene
Data health and relevance play a significant role in every effort and campaign. Executives and data scientists need to work on preventing bottlenecks and data silos, investing in more advanced ways of validating and processing data across all systems available to the enterprise. In addition, technology executives need to take care of constantly updating and improving security measures and guidelines, enabling smooth integration.
If you’re looking for more insights into process mining and want to explore potential scenarios for your enterprise, let’s chat! Our intelligent automation team possesses Big Four-level experience with visualizing and optimizing complex end-to-end processes, utilizing top process mining tools and enabling agile platform-agnostic integrations. With our expertise and consulting, you’ll gain a 360-degree view of your workflows and identify the most efficient and value-rich intelligent automation opportunities.