How does Process Mining turn data into strategic decisions?

According to a study by McKinsey, companies that apply advanced analytics to their operational processes can achieve up to 30% cost reduction and a 20% increase in productivity. In a scenario where efficiency and competitiveness go hand in hand, knowing in detail how processes actually occur — and not just how they were designed — becomes essential.

This is where process mining enters the picture, a discipline that combines data science, artificial intelligence, and log analysis to map, visualize, and optimize operational flows in real-time.

Companies that adopt this approach have managed to transform raw operational data into competitive advantages. But how does this technology work in practice? And what are the real benefits for your operation?

Continue reading to discover how process mining can be the turning point for elevating your business’s performance.

How does process mining solve bottlenecks that traditional systems do not detect?

Traditional management systems, such as ERPs or CRMs, record the activities performed but do not provide a clear view of how processes actually flow between departments, systems, and people. They show what was done, but not necessarily how or in what sequence — and it is precisely in this gap that many bottlenecks hide.

Process mining directly addresses this visibility failure. By analyzing event logs — records automatically generated by systems during the execution of tasks — the technology reconstructs the real path taken by each process, with temporal and sequential precision. As a result, it becomes possible to:

• Identify process variations that deviate from the ideal model;
• Detect rework, delays, and repetitions that impact efficiency;
• Measure actual cycle times instead of generic estimates;
• Compare units, branches, or teams to understand where the biggest deviations are;
• Expose invisible bottlenecks that do not appear on conventional dashboards.

Unlike traditional systems, which rely on manual inputs or retrospective analyses, process mining provides a dynamic, data-driven view capable of guiding continuous improvements based on what truly happens in daily operations.

How to use Process Mining to increase efficiency in financial and logistics processes?

Financial and logistics processes are among the most critical — and complex — within an operation. Payment errors, delivery delays, or lack of visibility over the flow of orders and billing can lead to losses. Process mining has been consolidating as a powerful ally to enhance efficiency and mitigate risks in these areas.

Efficiency in financial processes

In the financial context, process mining allows mapping and analyzing steps such as accounts payable, accounts receivable, reconciliations, billing, and accounting closing. Key gains include:

• Identification of duplicate or overdue payments;
• Monitoring of exceptions and manual approvals that can delay the flow;
• Reduction of payment cycle time (DPO);
• Standardization of processes across units and branches;
• Automated compliance, with alerts about deviations from internal policies.

This ensures more agility, control, and predictability in the company’s financial flow.

Efficiency in logistics processes

In the logistics chain, process mining enables an end-to-end view of orders, from entry to delivery, passing through stages such as sorting, shipping, and transportation. This allows:

• Monitoring of actual delivery times (OTD – On Time Delivery);
• Analyzing causes of recurring delays in certain distribution centers;
• Tracking deviations between the order and what was actually delivered;
• Detecting bottlenecks in critical stages, such as approval of carriers or document issuance;
• Improving the accuracy of delivery promises made to the customer.

By applying Process Mining in these areas, companies can not only optimize their operational KPIs, but also improve the end customer experience and strategic decision-making based on real data.

What operational metrics really improve with Process Mining?

The application of process mining goes beyond visualizing real flows: it allows continuous monitoring of operational performance indicators (KPIs) that truly reflect the impact of implemented improvements. By identifying patterns, deviations, and automation opportunities, it is possible to elevate the performance of the monitored areas with precision.

Check out some of the most impacted metrics:

1. Cycle Time

Process mining reveals exactly how much time is spent between the start and end of a process, considering each stage. With this data, it is possible to identify specific bottlenecks and accelerate operational flows.

2. Lead time between stages

In addition to the total time, the tool allows analyzing the intervals between specific activities — for example, between the approval of an order and its release for billing — helping to optimize transition points.

3. Process Compliance

By comparing the actual process with the ideal model (to-be), it is possible to measure how often the flows adhere to established rules. The higher the compliance, the lower the risk of rework, failures, and non-conformities.

4. Rework Rate

The technology allows quantifying repeated activities in the process, such as duplicate approvals or reissuance of documents. Reducing this indicator is directly linked to increasing productivity and standardizing processes.

5. Throughput (productivity per period)

Based on logs, it is possible to understand how many instances of a process are completed within a given period. This metric helps to measure the actual operational capacity, by team or system.

6. Average Exception Resolution Time

Process mining allows isolating and measuring the time spent on outlier cases — such as orders with registration flaws, credit blocks, or transport errors. This way, companies can directly address the causes of these exceptions.

These metrics, monitored in real-time or in periodic cycles, provide valuable inputs for strategic and tactical decisions. More than just measuring, Process Mining helps transform numbers into action — and action into measurable results.

How does Process Mining integrate with systems like SAP, Oracle, and Salesforce in practice?

The strength of process mining is directly linked to its ability to connect to the systems that store the transactional data of daily operations. In this regard, solutions like SAP, Oracle, and Salesforce are rich sources for extracting events (logs) — essential for the automated analysis of processes.

Extraction of structured data

Systems like SAP and Oracle store large volumes of structured data in modules such as finance, purchasing, sales, and logistics. Integration with process mining tools is done through:

• Native connectors of mining platforms (such as Celonis, UiPath Process Mining, or Apromore), which integrate directly with the ERPs’ databases;
• APIs or ETL services, when the company already has an intermediate data layer (such as a data lake or warehouse);
• Exporting logs in standardized formats (such as CSV or XES) for manual ingestion into analytical tools.

This data is then translated into events with attributes such as process ID, timestamp, responsible party, status, and duration, enabling the reconstruction of the real flow.

Integration with CRM and service systems

In platforms like Salesforce, process mining helps to understand the behavior of sales flows, customer service, and post-sales. The integration allows:

• Tracking average lead conversion time;
• Analyzing most frequent follow-up paths;
• Identifying delays in high-value opportunities;
• Detecting bottlenecks in commercial approval processes.

Integration without operational impact

An important point is that data extraction for process mining is usually done non-intrusively, meaning it does not impact operations or require changes to the source systems. Moreover, the use of automation and ready connectors speeds up the initial configuration, allowing the first dashboards and analyses to be available within a few days.

By integrating with the systems that support business processes, process mining becomes a bridge between operational data and strategic decision-making — without requiring major changes to the existing technological architecture.

What challenges arise when starting a Process Mining project?

Despite the high potential impact of process mining, adopting this technology is not without obstacles. Starting a Process Mining project requires more than just acquiring a tool: it is necessary to align strategy, data, and people. Understanding these challenges is essential for ensuring a successful implementation.

1. Data quality and availability

Process mining relies on the existence of rich and well-structured event logs. However, not all companies have their systems prepared to generate the necessary data. Incomplete information, lack of timestamps, or absence of unique identifiers can compromise analysis or require more complex technical adjustments.

2. Alignment between business and IT areas

Process mining projects require collaboration between technical teams and business areas. IT facilitates integration with systems and data extraction, while operational teams are responsible for validating analyzed flows and implementing improvements. Communication breakdowns between these groups can delay or limit the strategic use of the solution.

3. Choosing processes with real improvement potential

It is common for companies to want to apply process mining to various flows simultaneously. However, an inadequate choice — such as starting with overly simple processes or those with little operational impact — can generate frustration and fail to demonstrate the real value of the tool. Ideally, start with a critical, recurring process that has a history of deviations or bottlenecks.

4. Learning curve and cultural change

Although mining tools offer intuitive visualizations, correctly interpreting the data and transforming insights into action requires training. Moreover, using this technology often challenges the existing culture, revealing structural problems or previously invisible deviations. Leadership engagement is necessary to ensure that findings are used as a starting point for evolution — not as a punishment mechanism.

5. Continuous maintenance and evolution

Process mining is not a project with a start, middle, and end — it is a continuous practice. Therefore, a recurring challenge is to ensure resources, data updates, and constant monitoring over time. Without this, the use of the tool may become sporadic and lose strategic value.

Overcoming these challenges requires planning, multidisciplinary engagement, and a long-term vision. When well conducted, a process mining project not only delivers quick gains but also consolidates a new way of thinking and operating based on real data.

What signs indicate that your company is ready to invest in Process Mining?

The decision to adopt process mining should be based on a realistic analysis of the company’s stage. Although the technology brings significant benefits, it becomes especially effective when there is a minimum maturity level in terms of processes, data, and organizational culture. Below are some clear signs that your company may be ready to take this step.

1. Structured processes, but with low visibility over actual execution

If the company already has defined workflows but struggles to understand how they happen in practice — with recurring deviations or a lack of clarity about the real journey — it is a sign that process mining can generate value quickly.

2. Presence of multiple systems and information silos

Organizations that operate with decentralized ERPs, CRMs, and other platforms often suffer from data fragmentation. If you already recognize this challenge and seek ways to connect them to improve governance and the fluidity of processes, process mining could be the missing link.

3. Ongoing automation, continuous improvement, or digital transformation initiatives

Companies investing in RPA, BPM, Lean, or other operational efficiency approaches have much to gain from process mining, as the technology can precisely indicate where to prioritize those actions — functioning as a radar for strategic initiatives.

4. Growing focus on data as a basis for decision-making

If the company already uses operational indicators, BI, or dashboards and seeks to elevate analysis beyond what is “visible,” process mining helps cross-reference information with execution context — something that traditional analytics solutions do not always deliver.

5. Leadership support for data-driven innovation

Finally, one of the main signs of readiness is leadership commitment to data-driven initiatives. Since process mining reveals inefficiencies and deviations, manager support is essential to converting findings into concrete actions — ensuring that the tool is used as a vehicle for evolution, not just diagnosis.

Recognizing these signs helps avoid premature investments and ensures that the project has adherence, impact, and continuity. When the company is ready, process mining becomes a powerful lever for accelerating operational gains with precision.

What is the expected return from a Process Mining project within 12 months?

When considering the adoption of process mining, one of the most recurring questions among managers is: “How long will it take to see concrete results?”. The good news is that when well-implemented, the return can be noticeable within the first few months — especially in areas with intensive processes and low operational visibility.

Typical gains in the short and medium term

Companies that start with well-defined scopes usually observe, within 12 months:

• Reduction of up to 30% in cycle time of critical processes;
• Decrease in reworks and exceptions that consume operational resources;
• Improvement in process compliance, with more control and fewer deviations;
• Agility in decision-making, based on real and updated data;
• Identification of automation and standardization opportunities with quick ROI.

Along with quantitative gains, there are also qualitative advancements: greater collaboration between areas, data-driven culture, and increased maturity to evolve in digital transformation initiatives.

For these results to be sustainable and scalable, it is essential to have the support of experts capable of translating data into real value. Objective works alongside companies seeking to implement process mining strategically, from selecting the ideal process to continuously analyzing KPIs and evolving the project.

Want to know if your company is ready for this journey? Speak with Objective experts and discover how to boost operational efficiency with process mining.

Description: Understand how process mining works in practice, what gains to expect in 12 months, and find out if your company is ready to take this next step.