There is an irony at the heart of modern process mining: most platforms that promise clarity have been delivering confusion! Sounds outrageous? Let me explain.
For years, the implicit sales pitch has been maximalist —more event logs, more connectors, more dashboards, more granularity. Enterprises warehoused billions of processed events, built data lakes that resembled data swamps, and paid enterprise-grade licensing bills priced by the terabyte.
However, in return, many of them received something they did not expect. It’s best termed as analytical paralysis — a state of inertia where decision making slows down, cutting progress.
The mid-market cannot afford a repetition of that in their process intelligence story. But more importantly, it shouldn’t want to.
Data obesity is not a sign of analytical maturity. It is a symptom of strategic immaturity
I have spent a significant portion of my career building process intelligence tools for organisations that operate between the agility of the mid-market and the complexities of the large enterprises. More often than not, we come across lean organisations where the CFO also runs IT, the ops director also serves as the process analyst, and the CTO also serves as the data steward. For such companies, the most valuable thing to offer is not comprehensive but precision and the luxury of saved time!
The Weight of What We Collect
The process mining market is projected to reach USD 9.49 billion by 2030, and the velocity of that growth has pulled vendor incentives in a predictable direction: towards volume. Today, most process mining platforms are built to ingest everything. Pricing is structured to reward scale. Implementation timelines can stretch up to 24 months, with a single process analysis in year one costing between USD 50,000 and USD 100,000 when internal resource allocation is factored in. These are enterprise economics applied to mid-market realities.
The result is what I would describe as data obesity: organisations that have consumed far more process data than they can meaningfully digest, and whose analytics infrastructure is heavy, slow, and expensive to maintain. The condition is widespread. This research shows that 42% of data models built by data scientists are never used within their organisations.
And yet we keep feeding the machine!
The cost is not just financial, though the financial burden is real. The deeper implications are cognitive. Leaders experiencing information overload are, according to published research by The Harvard Business Review, 7.4 times more likely to regret their decisions and 2.6 times more likely to avoid making decisions altogether. Process mining tools that surface a thousand inefficiencies at once don’t empower operations teams — they immobilise them.
What Minimum Viable Data Actually Means
Minimum Viable Data is neither a compromise nor a consolation prize for organisations that cannot afford an enterprise-grade process mining deployment. It is a philosophy that holds that the right signal, cleanly captured, consistently acted upon, outperforms a noisy dataset by an order of magnitude!
Consider what process mining fundamentally does: it reconstructs how work actually flows through an organisation, versus how it was designed to flow. The gap between those two realities is where value lives. To identify that gap, you do not need every event log from every system over the last five years, but rather the right events from the right systems across a timeframe that reflects current operational reality.
The broader shift happening in analytics, from big data to right data, reflects this exact maturation. As one leading industry insider recently noted, “organisations are realising they don’t need to bring all their data to solve a problem — they need to bring the right data.” Indeed, the overwhelming abundance of data has only made it harder to extract the insights that matter.
On the other hand, if you are a mid-market manufacturer running an ERP, here’s what Minimum Viable Data might mean for you:
Order-to-ship event logs for the last 18 months, filtered to your top three product lines by revenue and have its conformance checked against your baseline lead-time commitment.
That is a tractable problem, solving which yields decision-ready insights within hours and not weeks. Therefore, for a mid-market business, it is worth doing even on a busy Tuesday morning rather than keeping it shelved for the next month.
The right signal, cleanly captured, consistently acted upon, outperforms a noisy dataset by an order of magnitude.
Summaries Over Raw Feeds
There is a related discipline that has been largely undervalued in conversations: the power of the intelligent summary. Most platforms today are built on the assumption that every user wants to explore the full event graph — to drill down, pivot, filter, and slice. While this is valuable for career data professionals and experts, it is of little use to the decision-makers who are making hard choices under pressure!
For instance, the CFO of a USD 200 million revenue distribution company does not have time to roleplay as a process analyst to get the numbers she needs. She needs hands-on insights on: where is my Order-to-Cash cycle losing more than three days, and what is the most likely root cause? That is a summary which allows her to walk into a conversation with her COO with something actionable rather than something exploratory.
The discipline of building summary-first outputs requires a fundamentally different product design philosophy. It means pre-computing the conclusions that matter most to specific roles and industries, instead of building an infinitely flexible exploration environment that assumes expertise the user may not have. It needs an intelligence layer alongside the data layer.
For the mid-market, this fusion is existential. These organisations do not have extensive process mining centres of excellence and in-house data teams. Most of them have a few sharp operators who need to pick up the pace from the very beginning, with minimal handholding and knowledge transfer.
The Cost Equation That’s Mostly Overlooked
Enterprise process mining pricing was built for a world where massive data volume is a badge of credibility. There, even an entry-level process mining offering can cost over USD 3000 per month for a single analyst and millions of events. Clearly, this is a domain built for enterprise-scale data appetites.
However, there is a second cost dimension that rarely surfaces in vendor conversations: compliance costs. GDPR’s data minimisation principle, laid down in Article 5(1)(c), is not just a bureaucratic shackle upon businesses.
It is an architectural constraint that should be reshaping how every European mid-market company thinks about what process data it stores, for how long, and for what purpose. In 2025, according to IBM, the average cost of a data breach hovered around USD 4.4 million.
Yes, the figure represents a 9% decrease from the previous year.
However, organisations holding excessive operational data, much of which contains personally identifiable information embedded in process logs, are still carrying undisclosed regulatory and reputational risk on their balance sheets.
Therefore, data minimisation, when practised properly in process mining, is ethically sound and financially prudent.
Smaller, more purposefully curated event logs cost less to store, easier to secure, simpler to audit, and require less explanation to a regulator. They also perform better analytically: reducing redundant, obsolete, and trivial data produces more accurate and reliable insights.
A Question You Can Lead With
The conversation the mid-market leaders need to have right now is not “what data should we collect?” but “what decisions do we need to make, and what is the minimum data footprint needed to make them confidently?”
That pivot changes everything.
It gives a refreshed look at what you deploy, what you pay, how quickly your team adopts the tool, and how quickly you see a return. It also changes the nature of your vendor relationship from a data maximisation exercise to a machine that churns out actionable insights for your decision-makers.
At FUTUROOT, we designed our platform around this conviction from the start.
Our architecture is built on the premise that mid-market business leaders should be able to get a meaningful process diagnostic running in days, not months, and that the outputs should cater to their priorities, not an analyst’s curiosity. For us, the question driving every product decision is: what is the minimum viable data surface that allows this organisation to act with confidence on its most important operational questions?
The next frontier in process intelligence for the mid-market is not in the watered-down enterprise platforms that retain complex connectors and the biggest event log capacity. Instead, it is in their ability to see with a clearer lens and with the sharpest editorial discipline. The future belongs to mid-market organisations that can curate and consume fast rather than accumulate and wait!
The next wave of process mining will not come from those with the most connectors. It will come from those with the sharpest lens.
The Competitive Advantage of Restraint
Restraint in data collection is not a limitation, but a competitive posture. Mid-market companies that are serious about defining their Minimum Viable Data footprint and building their process intelligence practice around it will move faster, spend less, comply more easily, and make better decisions than their peers who are still waiting to put together all the data.
The intelligence advantage has never been about who has the most. It has always been about who extracts the most meaning from what they have. Process mining, at its most powerful, is exactly that: the discipline of reading what your organisation’s data is actually telling you, without the noise of everything it isn’t.
In a world obsessed with volume, that is the genuinely radical position to hold and it is one that the mid-market is uniquely positioned to lead.
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