New
(2026) AI Processed Human Approved
The Process of Process Science – Generating Actionable Insights from Digital Trace Data
This paper presents a generalizable framework for conducting process science studies that leverage digital trace data and computational methods like process mining. Guided by principles of contextual grounding, iterative reasoning, and combining computational with human sense-making, the paper structures process science inquiry into three recursive cycles: descriptive, explanatory, and prescriptive.
Problem
While process science provides strong conceptual foundations for studying socio-technical processes, concrete methodological guidance on how to conduct empirical process science studies in practice remains underdeveloped. Existing computational techniques excel at revealing what happens in processes, but they lack the contextual and interpretive mechanisms needed to explain why processes unfold as they do or how to design effective interventions.
Outcome
- Defines three core methodological principles for process science: contextual grounding, iterative reasoning, and the integration of computational and human sense-making.
- Proposes three interconnected, recursive cycles of inquiry: a descriptive cycle (from scope via data to description), an explanatory cycle (from analysis via pattern to explanation), and a prescriptive cycle (from intervention via evaluation to prescription).
- Demonstrates the practical execution of these cycles through a real-world case study of a financial institution's digital customer onboarding process.
- Provides concrete guidance for researchers and practitioners on combining qualitative insights with computational techniques to move from descriptive analytics to actionable organizational interventions.
- Proposes three interconnected, recursive cycles of inquiry: a descriptive cycle (from scope via data to description), an explanatory cycle (from analysis via pattern to explanation), and a prescriptive cycle (from intervention via evaluation to prescription).
- Demonstrates the practical execution of these cycles through a real-world case study of a financial institution's digital customer onboarding process.
- Provides concrete guidance for researchers and practitioners on combining qualitative insights with computational techniques to move from descriptive analytics to actionable organizational interventions.
What it means for you
- CIO / IT Executive: Schedule a 30-minute meeting with your IT Manager for Monday morning to initiate the 'descriptive cycle' by identifying a specific digital process (e.g., customer support ticket resolution) and confirming the availability of its digital trace data for analysis.
- IT Manager: On Monday morning, begin by downloading and organizing the raw digital trace data logs (e.g., timestamps, event names, user IDs) for the chosen IT process from your systems and prepare to import them into a process mining tool.
- Business Strategist: On Monday morning, prepare a concise list of 3-5 key business questions you want to understand about a specific digital process (e.g., 'Why is our customer onboarding taking so long?') to inform the 'explanatory cycle'.
- Researcher: On Monday morning, review the provided case study from the research paper and begin to outline a potential scope for a process science study based on a digital process you have access to data for, focusing on defining the initial research questions.
- Policymaker: On Monday morning, request a brief overview from your IT Executive and Business Strategist regarding the current state of understanding and identified inefficiencies within a key digital customer-facing process, preparing to guide future intervention strategies.
Transcript
Host: Welcome to A.I.S. Insights, powered by Living Knowledge. I'm your host, Anna Ivy Summers. Today we are diving into a fascinating new study titled "The Process of Process Science – Generating Actionable Insights from Digital Trace Data." Joining me is our resident expert, Alex Ian Sutherland. Alex, great to have you here.
Expert: Thanks, Anna. It's great to be here to discuss this study.
Host: So, Alex, to kick things off, what is this study really about?
Expert: At its core, the study introduces a generalizable framework for conducting process science. Businesses today collect massive amounts of digital trace data—event logs from software platforms, CRM systems, and workflow tools. Tools like process mining are great at showing us what happens in these systems. But this study offers a structured roadmap for taking that raw data and turning it into real, actionable business improvements.
Host: That sounds incredibly valuable. But why do we need a new framework? What problem is this study trying to solve?
Expert: That's the key question. In recent years, process mining has become huge. Companies use it to discover process flows, find bottlenecks, and check compliance. But computational tools have a major blind spot: they can show you what is happening, like a massive delay, but they can't tell you why it's happening or how to fix it effectively. Without context and human interpretation, digital trace data is just a static snapshot.
Host: Right, knowing there's a bottleneck doesn't tell you if it's caused by a software glitch, a lack of staff, or a confusing policy. So how does this study bridge that gap?
Expert: The study proposes three core principles. First is contextual grounding, which means looking at data through the lens of the environment, routines, and culture where it was generated. Second is iterative reasoning—using a step-by-step approach of hypothesis testing rather than expecting instant answers. And third is integrating computational analysis with human sense-making—combining data algorithms with interviews, domain expertise, and qualitative insights.
Host: Combining machine precision with human intuition. That makes total sense. And how does the study organize this into a working process?
Expert: The study structures the methodology into three interconnected, recursive cycles. First, the Descriptive Cycle, moving from scope and data to an accurate process description. Second, the Explanatory Cycle, taking computational analysis and patterns to uncover true explanations. And third, the Prescriptive Cycle, moving from design interventions and evaluation to actionable prescriptions.
Host: Can you share a real-world example from the study to illustrate how these cycles work in practice?
Expert: Absolutely. The study highlights a case study involving customer onboarding at a Central European financial institution. Initially, management just wanted to fix long throughput times for loan applications. In the Descriptive Cycle, researchers adjusted the scope and realized delays were tied to tensions between relationship managers and the compliance department.
Host: So the data alone didn't tell the whole story until they adjusted the scope?
Expert: Exactly. Then, in the Explanatory Cycle, process mining showed massive loops between account managers and compliance. But when they integrated qualitative interviews, they discovered that a recent IT update to the onboarding tool had introduced an error. To keep working, managers used informal workarounds, which triggered compliance red flags and endless review loops.
Host: Wow, so an IT update meant to help actually created hidden workarounds and delays. What happened in the Prescriptive Cycle?
Expert: In the Prescriptive Cycle, they tested specific interventions, like automating routine compliance checks and updating low-code questionnaires. They evaluated these changes both through new event logs and staff feedback. Interestingly, fixing one module actually caused new temporary delays elsewhere before stabilizing. This iterative evaluation prevented them from rolling out bad fixes permanently.
Host: That really highlights why it's so critical for business leaders. What are the key business takeaways here?
Expert: First, don't rely solely on automated dashboards or process mining tools to solve organizational problems; you must include human context. Second, approach process optimization as an iterative cycle—test interventions, measure results using trace data, and refine continuously. And third, use stopping rules: aim for explanations and fixes that are "good enough" to act on rather than chasing theoretical perfection.
Host: Powerful insights for any leader driving digital transformation. Alex, thank you so much for breaking down this study for us today.
Expert: Always a pleasure, Anna.
Host: And thank you to our listeners for tuning into A.I.S. Insights — powered by Living Knowledge. Until next time, stay curious and keep building smarter processes!
Expert: Thanks, Anna. It's great to be here to discuss this study.
Host: So, Alex, to kick things off, what is this study really about?
Expert: At its core, the study introduces a generalizable framework for conducting process science. Businesses today collect massive amounts of digital trace data—event logs from software platforms, CRM systems, and workflow tools. Tools like process mining are great at showing us what happens in these systems. But this study offers a structured roadmap for taking that raw data and turning it into real, actionable business improvements.
Host: That sounds incredibly valuable. But why do we need a new framework? What problem is this study trying to solve?
Expert: That's the key question. In recent years, process mining has become huge. Companies use it to discover process flows, find bottlenecks, and check compliance. But computational tools have a major blind spot: they can show you what is happening, like a massive delay, but they can't tell you why it's happening or how to fix it effectively. Without context and human interpretation, digital trace data is just a static snapshot.
Host: Right, knowing there's a bottleneck doesn't tell you if it's caused by a software glitch, a lack of staff, or a confusing policy. So how does this study bridge that gap?
Expert: The study proposes three core principles. First is contextual grounding, which means looking at data through the lens of the environment, routines, and culture where it was generated. Second is iterative reasoning—using a step-by-step approach of hypothesis testing rather than expecting instant answers. And third is integrating computational analysis with human sense-making—combining data algorithms with interviews, domain expertise, and qualitative insights.
Host: Combining machine precision with human intuition. That makes total sense. And how does the study organize this into a working process?
Expert: The study structures the methodology into three interconnected, recursive cycles. First, the Descriptive Cycle, moving from scope and data to an accurate process description. Second, the Explanatory Cycle, taking computational analysis and patterns to uncover true explanations. And third, the Prescriptive Cycle, moving from design interventions and evaluation to actionable prescriptions.
Host: Can you share a real-world example from the study to illustrate how these cycles work in practice?
Expert: Absolutely. The study highlights a case study involving customer onboarding at a Central European financial institution. Initially, management just wanted to fix long throughput times for loan applications. In the Descriptive Cycle, researchers adjusted the scope and realized delays were tied to tensions between relationship managers and the compliance department.
Host: So the data alone didn't tell the whole story until they adjusted the scope?
Expert: Exactly. Then, in the Explanatory Cycle, process mining showed massive loops between account managers and compliance. But when they integrated qualitative interviews, they discovered that a recent IT update to the onboarding tool had introduced an error. To keep working, managers used informal workarounds, which triggered compliance red flags and endless review loops.
Host: Wow, so an IT update meant to help actually created hidden workarounds and delays. What happened in the Prescriptive Cycle?
Expert: In the Prescriptive Cycle, they tested specific interventions, like automating routine compliance checks and updating low-code questionnaires. They evaluated these changes both through new event logs and staff feedback. Interestingly, fixing one module actually caused new temporary delays elsewhere before stabilizing. This iterative evaluation prevented them from rolling out bad fixes permanently.
Host: That really highlights why it's so critical for business leaders. What are the key business takeaways here?
Expert: First, don't rely solely on automated dashboards or process mining tools to solve organizational problems; you must include human context. Second, approach process optimization as an iterative cycle—test interventions, measure results using trace data, and refine continuously. And third, use stopping rules: aim for explanations and fixes that are "good enough" to act on rather than chasing theoretical perfection.
Host: Powerful insights for any leader driving digital transformation. Alex, thank you so much for breaking down this study for us today.
Expert: Always a pleasure, Anna.
Host: And thank you to our listeners for tuning into A.I.S. Insights — powered by Living Knowledge. Until next time, stay curious and keep building smarter processes!