The evolution of process mining

Process mining is gaining increased prioritization and investment as organizations work to drive efficiencies and enhance their processes. It’s now projected that the global process mining software market will reach $11 billion by 2030.

How exactly did process mining become such an important technology? In the past, organizations relied on process mapping, which had to be completed by teams of people who met in person for several days to figure it out on a whiteboard or spreadsheet. With this approach, most of the models became outdated within days.

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Today, with digitization and decentralized work, processes and workflows have changed. Process mining has emerged to meet new priorities, using powerful algorithms and advanced data transformation to make discovering and optimizing processes fast, analytics-driven, continuous and fully digital.

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What is process mining?

Process mining is a strategic combination of data science and process management practices that involves analyzing data in event logs. It creates event logs as work is done, such as when an order is received, a product is delivered or a payment is made. The logs make visible what, how and when work is happening; administrators can see who took action, the length of time a process takes and whether parts of the process are deviating from the average.

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In other words, process mining uses existing data — available in corporate information systems — and automatically displays the actual process behind that data. Process mining software can help organizations easily capture information from enterprise transaction systems and provides detailed, data-driven information about how key processes are performing.

Process mining primarily works to:

• Survey processes across the enterprise.
• Analyze processes fully and accurately based on the facts.
• Identify bottlenecks and inefficient processes.
• Continuously monitor processes and measure improvements.

With process mining in place, companies can cut through the noise by analyzing enterprise data from different sources and mapping the way that data moves and interacts. This detailed analysis makes identifying backlogs and inefficiencies possible, with the goal of creating more streamlined workflows.

Combining RPA and process mining

Even as process mining technology has enormous potential to revolutionize corporate processes and expedite digital transformation on its own, process mining is even more successful when paired with robotic process automation, or RPA.

By using RPA in concert with process mining, businesses can do more than just identify backlogs and areas for improvement: They can quickly create and adjust automated workflows that resolve inefficiencies. Used together, the technologies can create an immediate return on investment.

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With the critical insights that process mining offers, businesses can easily identify activities that are prime targets for automation. This boosts employee productivity and ensures teams have more time to spend on tasks that require more creativity.

Equally though, process mining improves RPA efficiency at every step of its lifecycle. Because process mining leverages system data to generate a map of as-is business processes at a company, it can play a key role in smoothly implementing RPA. Process maps can be used as guidelines for deploying robots as they help businesses identify what needs to be automated first to maximize return on investment.

Moving from process discovery toward continuous discovery

While business resilience as a competitive advantage isn’t new, fewer business leaders understand that maintaining business resilience requires a constant focus on business outcomes and evolving business processes. It has been proven that process improvement practices and automation technologies play a key role in maintaining resilience and achieving business outcomes. This is particularly vital when navigating adverse conditions, such as periods of economic decline.

As a result, many organizations are seeing the future of process discovery and adjusting to focus on continuous discovery, which uses discovery and automation technologies to uncover, understand and take action toward an organization’s desired KPIs.

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At its core, continuous discovery calls for and enables strict prioritization and scientific optimization of processes. Continuous discovery allows organizations to meet targeted process and business goals.

Meanwhile, these same technologies support the continuous monitoring of overall organizational performance and process transformation initiatives to ensure processes are optimized. Process mining is an ideal approach to include in a continuous delivery strategic plan; it helps in the discovery phase of a process lifecycle, further optimizing business outcomes by expanding process-based knowledge across new business areas.

Bringing process mining and RPA into the continuous discovery process

With continuous discovery, automation champions and business leads can collaborate on holistic digital transformation. A holistic approach includes connecting business goals, processes and automation opportunities with the aim of delivering business outcomes to the C-suite on a sustained basis. This approach to digital transformation is key to building organizational resilience, enabling organizations to make changes, discovering new improvements and monitoring impact while transforming processes.

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Maintaining business resilience requires a constant focus on business outcomes and evolving business processes. Maximizing organizational processes for productivity and results not only shapes the way employees work but improves the customer experience. Together, RPA and process mining have changed the landscape of impactful, company-wide discovery, unlocking what can and should be automated next in order to achieve greater results.

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Palak Kadakia is the VP of Product Management at UiPath. With a passion for building innovative products and teams, she is focused on building and designing intuitive customer experiences. Palak leads the engagement platform for RPA, which includes a low-code app development platform as well as human-in-the-loop, conversational AI and data services. Prior to joining UiPath, Palak was a product management leader at Microsoft.