Process intelligence in the continuous improvement cycle: A comprehensive approach

In today’s dynamic business world, competitiveness has become a constant challenge for companies. The speed at which markets evolve, changing consumer demands and the emergence of new technologies create a highly volatile environment. In this context, companies face the challenge of staying agile and adapting quickly to changing conditions to maintain or improve their market position.

One key to excelling in this competitive environment is making strategic data-based decisions. This is where process intelligence comes into play – a software-based methodology that enables companies to understand and analyze their internal processes comprehensively. The ability to collect, visualize and assess real-time operational data provides a deep insight into efficiency, identifies areas for improvement and provides the necessary information for informed decision-making.

Responding rapidly to changing market conditions is essential, and process intelligence enables companies to anticipate issues, optimize processes and make more informed strategic decisions. In a business environment where agility is key, the ability to continuously understand and improve processes through process intelligence becomes a fundamental component for achieving and maintaining competitiveness.

Watch the on-demand webinar Demystifying Process Intelligence: A Fireside Chat to learn why intelligent processes may be the key to unlocking true business transformation.

4 types of data for continuous business process improvement

Data and its analysis are crucial for organizations, both public and private, to make the right decisions. Data-driven decision-making involves using data to determine how to improve decision-making processes. This leads to the idea of a decision model, which may include prescriptive analytical techniques (generating results that can specify actions to take), descriptive analytics (explaining what happened) or predictive analytics (seeking patterns and trends to decipher what might happen, allowing corrective actions if necessary).

The analysis of data should consider the entire context to make the right decisions. There are four types of data to consider for continuous improvement of business processes, always aligned with the strategic objectives of any organization. These are business data, operational data, system data and desktop data. Each type of data focuses on a different approach, complementing each other.

1. Business data: All data obtained by the organization from executing various activities to meet the needs of its customers (both external and internal).
2. Operational data: All data related to the efficiency, effectiveness and efficacy of the execution of strategic and support processes of an organization.
3. System data: All data known as “system event logs” that are stored during the execution of applications used for performing activities (CRM, ERP, SCM, custom applications, etc.).
4. Desktop data: All data related to how employees resolve each activity of a process from their workplace, captured from user interaction to accomplish daily tasks (keyboard, mouse clicks, etc.).

3 types of software for mining organizational data

It is not enough to focus solely on data storage and analysis; it is necessary to understand how data correlates to the generated patterns to comprehend the impact of each operational action on business outcomes. Three disciplines – data mining, task mining and process mining – can appear in different moments and independent projects within an organization. Combining them can have a more significant impact on the quest for operational excellence as they provide business intelligence. Each discipline focuses on a different approach that complements the others.

1. Data mining: The process of analyzing massive amounts of raw data (big data) statically to discover relationships, patterns and predict future trends, using data screening and classification to transform data into actionable information for different departments of the company.
2. Process mining: The automatic and visual analysis of business processes based on event logs and data paths from the transactional technology systems of an organization to identify specific areas for operational improvement and uncover hidden issues.
3. Task mining: The capture of user interaction data (what happens on desktops for a process to actually occur) to analyze how employees are doing their work and how they can do it better.

Data-driven versus process-driven

Organizations are faced with two key approaches that have emerged to guide decision-making and improve efficiency: the data-driven approach and the process-driven approach. Although these approaches may seem opposed in their philosophy, they must coexist and complement each other to drive business success.

• A data-driven approach: This focuses on the analysis and interpretation of data to inform business decisions. Here, data is the compass and tools such as statistical analysis and machine learning are used to uncover patterns, trends and opportunities. This approach provides more objective, evidence-based and real-time decision-making, enabling companies to adapt quickly to market changes.
• A process-driven approach: This focuses on procedures and operations, seeking to optimize the organization’s processes, standardize and improve efficiency in task execution. This approach is essential to ensure that operations flow smoothly and resources are used effectively, thus enhancing the quality and consistency of products or services.

The main difference lies in perspective. While data-driven focuses on what the data reveals, process-driven concentrates on how activities are carried out. Data-driven seeks to understand the “why” behind the results, while process-driven deals with the “how” to achieve those results.

The synergy between data-driven and process-driven can be powerful. The wealth of data collected in a data-driven approach can help identify specific areas of processes that need improvement. On the other hand, an effective implementation of processes can ensure accurate and timely data collection.

The key is to leverage the information generated by one approach and use it to optimize the other. While data-driven and process-driven represent distinct approaches, their strategic integration can result in a more agile, efficient and adaptable company.

Process intelligence in the BPM lifecycle for continuous process improvement

Process intelligence serves as an essential catalyst in the cycle of continuous improvement. From strategic planning to process optimization, its ability to analyze data, foresee challenges and adapt to dynamic changes makes it an invaluable asset. Organizations embracing and leveraging process intelligence through business process management (BPM) technologies are better positioned to innovate continuously, ensuring their relevance and success in an ever-evolving business world.

The BPM lifecycle for process improvement includes six stages:

1. Strategic planning: Involves focusing on the process to be optimized, aligning it with the business strategy, understanding the impacts and risks of changes, planning the changes, prioritizing and deciding what to do and in what order, allocating resources, etc. Process intelligence begins its impact in the strategic planning phase, setting organizational objectives and defining specific process goals.
2. Analysis and logical process modeling: Involves discovering business processes, defining the current (AS-IS) and desired (TO-BE) states, analyzing process improvement or redesign, modeling the business process and simulating its execution to detect possible operational errors and inconsistencies. Process intelligence uses advanced tools to visualize and understand every aspect of the operational flow.
3. BPM design or physical process modeling: Involves implementing the business process model designed by the business into the BPM engine. This stage includes adding all necessary details for optimal implementation, ensuring the perfect execution of the process, defining data models, rules controlling activities, flows between different functional units, defining metrics, defining web services, designing forms for interacting with data, analyzing necessary integrations with systems and applications required for process execution. Process intelligence guides the design of physical processes, aiming not only for operational efficiency but also adaptability to future changes.
4. Automation and integration of the designed business process: Involves automating possible tasks of the process, integrating with existing applications, systems, services and data, linking roles with company personnel and connecting to the rule system that allows validations and compliance with business policies. Process intelligence greatly benefits the automation and integration phase. Its ability to analyze and understand complex data helps identify optimal areas for automation, and system integration is more effective as intelligence facilitates workflow synchronization and minimizes errors.
5. Monitoring of process instances: Involves tracking and controlling the execution to identify execution anomalies, analyzing key indicators according to business objectives that can alert potential problems for immediate attention, situational reporting and evaluating system performance, etc. Process intelligence, by analyzing large amounts of information, turns monitoring into a proactive tool. Early detection of performance deviations, combined with automatic alerts, enables quick and precise intervention.
6. Optimization of the business process: With the use of dashboards from the monitoring stage aligned with the strategic objectives, it is possible to define a process optimization plan for continuous improvement (applying methodologies such as Lean, Six Sigma or Theory of Constraints) and the fulfillment of the defined business strategy. Possible process optimizations found should be subject to strategic study in the strategic planning stage and never directly lead to the analysis and modeling stage, ensuring strategic coordination of process optimization changes.

In today’s dynamic business landscape, accurate and real-time data is paramount for informed decision-making. However, relying solely on collecting static business data is insufficient for identifying improvement opportunities. To truly make informed decisions you need to bring all the information together, including the information coming from how processes operate.

The ARIS Suite (above) orchestrates the full business spectrum to understand, monitor, improve and control processes. An integrated process intelligence suite emerges as a powerful tool for driving operational excellence. Its ability to integrate diverse data sources and provide a unified view of processes empowers organizations to make data-driven decisions with confidence. By adopting a holistic process intelligence approach, businesses can streamline operations, reduce costs, enhance customer experiences and ultimately gain a competitive edge.

In conclusion, embracing process intelligence is no longer an option but a necessity for organizations striving for continuous improvement. By harnessing the power of accurate data and advanced analytics, businesses can unlock their full potential and thrive in an increasingly complex world.