Critical success factors for realising value from process intelligence

Since the beginning of the Industrial Revolution, technological advancements have been expected to produce positive outcomes such as ensuring task efficiency, cost savings or improved product quality. As organisations have increased investments in systems over the years, the need to identify, evaluate, and justify the contribution of these systems to productivity, quality, and competitiveness has become a key issue [1]. Given this, research fields that focus on the intersection of business and technology, such as the Information Systems (IS) field, have developed traditions of analysing and establishing benchmarks for measuring the success and impact of organisational systems [2].

As process mining (PM) tools continue to gain a strong reputation for their ability to enhance operational efficiency, cost savings, and customer satisfaction, more organisations are eager to adopt this technology. Similar to previous organisational systems, the need to investigate measures or criteria that ensure the successful implementation of PM investments has become equally critical. One such approach adopted from Project Management is the concept of critical success factors by John Rockart [3]. Critical success factors refer to the “limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance for the organisation.”  [3]. In the BPM and PM space, several studies from academic [4-6] and practitioner [7, 8] outlets have investigated critical success factors as a precursor to PM success.

In recent times, PM tool vendors have expanded their capabilities, incorporating other forms of process analytics such as task mining, process simulation and machine learning and AI techniques. This new suite of tools is referred to as process intelligence (PI). By consolidating perspectives from academia and practice, this article presents critical success factors for the successful implementation of process intelligence (PI) in organisations. These factors are grouped under three main categories: foundational, intermediary and enabling.

Foundational

These success factors constitute the fundamental requirements for any PI endeavour.

1. The involvement and support of key organisational stakeholders is critical. Securing the buy-in and ongoing support of Top management (e.g., senior executives) ensures PI initiatives are strategically aligned with IT goals and funding allocation is secured. Process managers also provide domain knowledge of how processes are executed and what role external collaborators, such as suppliers, play during process execution.
2. Data governance issues related to the availability, management and quality of data are critical. It is important to have a realistic view of access and viability of digital traces of event data and contextual information like business rules, policy documents or KPIs for supporting PI initiatives. It is well known that without event data, little can be achieved using techniques such as process mining or task mining. While event logs provide a replay of the process, contextual information on the process offers additional insights for understanding and interpreting process analysis results. Ethical and regulatory provisions surrounding the extraction, analysis, and use of event data for PI initiatives, whether from single or multiple sources, and quality considerations must also be considered to ensure strict adherence during PI initiatives.
3. A third requirement is a process intelligence tool with functionality for process analytics, such as process discovery, conformance checking, or predictive analytics. The tool must also have filtering, drill-down and integration capabilities for other enterprise systems, data analytics tools, or AI and machine learning tools.
4. The next critical requirement is technical expertise – first, a PI specialist with the knowledge to optimise the PI tool and extract valuable insights from available event data. Second, a data extraction professional capable of creating pipelines for extracting and cleaning relevant event and contextual data from legacy systems and multiple sources. Finally, a process analyst experienced in streamlining and re-engineering business processes. Often, one person can possess more than one of these skill sets.

Intermediary

These success factors act as mediators between foundational and enabling factors.

5. Furthermore, a well-structured methodology is highly beneficial. For organisations experienced in IT project management, methodologies like Agile, Scrum, or Waterfall may already be established. Common PI methodologies typically follow project phases such as planning, data extraction and processing, mining and analysis, and evaluation and improvement. Running these phases iteratively enables continuous engagement between stakeholders and technical experts, resulting in a faster turnaround to achieve quick wins that validate the value of PI. Essentially, there must be a clear question or hypothesis to guide the analysis and interpretation of PI results, which helps in identifying tangible benefits.
6. After establishing a solid methodology, change management ensures that the necessary recommendations are put into action to generate tangible value. From a strategic viewpoint, running a PI initiative is inherently a change management endeavour. On a more detailed level, the real change involves efforts to execute the proposed initiatives from PI recommendations. Therefore, while change management should be considered from the beginning, it becomes fully active when change initiatives are being put into place. As part of the change management plan, it is crucial to have champions and communities that share success stories or showcase the positive outcomes of PI throughout the organisation. This approach will influence other stakeholders to adopt PI initiatives. Additionally, training end users, such as front-line personnel, improves their knowledge and skills regarding PI tool functionality and best practices for analysing and implementing PI results. Training also promotes the sharing of experiences among user groups, especially for ongoing initiatives.

Enabling

These success factors influence the effectiveness of foundational and intermediary factors in a moderating way.

7. Often, adopters with in-house expertise would configure a team responsible for planning and executing PI initiatives across the organisation. This team usually consists of technical experts and key subject matter experts such as process managers or change managers. This could be an ad-hoc team in charge of one-off initiatives or an established team (such as a centre of excellence) to manage PI initiatives going forward. Adopters without in-house technical capabilities often engage external consultants to execute pilot projects, thereby building internal capabilities for future projects.
8. For most organisations, PI initiatives are part of a broader digital transformation strategy. Therefore, project management is crucial for defining scope, managing budgets, and overseeing these initiatives. It also helps assign ownership and responsibility for PI initiatives to the appropriate stakeholders such as process managers, who can provide direction in implementing changes and monitor progress to ensure PI efforts achieve their intended objectives. This is particularly important if the aim is to establish PI as a long-term organisational capability.

Food for thought

PI adopters:

• How many of these success factors were necessary for your first process mining tryout?
• What additional investment will be required to scale process mining in your organisation?

PI non-adopters:

• How will the prevalence of these factors in your organisation influence your decision to adopt process mining?

Conclusion

As organisations continue to adopt and scale their use of PI tools, it is essential to ensure that the factors critical for the sustenance of these initiatives are constantly considered and assessed. A thoughtful, well-supported approach to PI adoption can empower organisations to derive a tailored implementation strategy that increases the likelihood of unlocking significant value and remaining competitive in an evolving digital landscape.

References

[1]             S. Petter, W. DeLone, and E. R. McLean, “The Past, Present, and Future of “IS Success”,” J Assoc Inf Syst, vol. 13, pp. 341-362, 2012.

[2]             M. Tate, D. Sedera, E. McLean, and A. Burton-Jones, “Information Systems Success Research: The “20-Year Update?” Panel Report from PACIS, 2011,” Communications of the Association for Information Systems, vol. 34, pp. 1235-1246, 2014.

[3]             J. F. Rockart, “Chief executives define their own data needs,” Harvard Business Review, vol. 57, pp. 81-93, 1979.

[4]             R. Mans, H. Reijers, H. Berends, W. Bandara, and R. Prince, “Business process mining success,” in 21st European Conference on Information Systems, Utrecht, Netherlands, 2013.

[5]             A. Alibabaei, W. Bandara, and M. Aghdasi, “Means of achieving Business Process Management success factors,” in 4th Mediterranean Conference on Information Systems, Athens, Greece, 2009.

[6]             A. Mamudu, W. Bandara, M. T. Wynn, and S. J. J. Leemans, “Process Mining Success Factors and Their Interrelationships,” Business & Information Systems Engineering, 2024/03/07 2024.

[7]             L. Biermann, “Deloitte Global Process Mining Survey 2021,” Centre for Bionics2021. Available: www2.deloitte.com/de/de/pages/finance/articles/global-process-mining-survey-2021.html

[8]             L. Reinkemeyer and T. Davenport. (2023) Transform Business Operations with Process Mining. Harvard Business Review.