7 Questions to Ask Before You Renew Your Process Mining Contract

Most process mining renewals are decided long before anyone reads the contract. The platform is in place and the team knows the tool. Renewing feels safe, and changing feels like a project nobody has time for.

But a renewal is the one moment every few years when you can ask a simple question without starting a fight: is this still the right fit?

For many organizations, the honest answer has shifted. Process mining (today increasingly called process intelligence) was often bought by a business unit, with its own way of handling data: copy it from source systems into the tool and analyze it there. Since then, many companies have moved their enterprise data into a cloud data platform and built their governance and AI plans around it. The process mining platform is now one of the systems still asking for its own copy.

In renewal conversations, I see the same tension again and again. The process excellence team wants the insights to keep flowing. The data platform owner asks why the same data is stored and secured twice. Procurement wants to know the real three-year cost. These seven questions are the ones I would want answered before signing.

The seven questions at a glance

1. Where does your process data live, and does it have to move?

2. What does it cost to keep moving and copying your data?

3. How does the cost behave as you scale?

4. Are you getting answers, or just dashboards?

5. What would it cost to leave, and who owns what you have built?

6. Does the platform fit your governance, data residency and AI compliance rules?

7. Is the platform ready for AI agents?

1. Where does your process data live, and does it have to move?

Start with a map, not a feature list. Where does the event data behind your process analyses come from today, and where does it end up?

Many established process mining platforms work on a copy-and-load model. Data is extracted from source systems or the data warehouse, copied into the vendor's environment and loaded into memory for analysis. It works, but it means your process data lives in two places, under two sets of access rules, and stays fresh only as long as the pipelines between them keep running.

If your company has consolidated its data in a cloud data platform, that second copy deserves a closer look. Some process mining applications can now run inside the data platform itself, for example as a native application in Snowflake, reading only the tables you grant them access to. Others connect to your platform and load the data into their own engine. This is where process mining architectures differ most, and it is a fair question to ask any vendor, including us.

Ask your vendor: Which data is copied out of our data platform, how often, and where is it stored? Could the same analysis run where the data already is?

2. What does it cost to keep moving and copying your data?

The license fee is the number everyone sees. The cost of keeping a second copy of your data is usually spread across several budgets, so nobody sees it in one place.

When I help customers add it up, the same items keep appearing:

• Storage and compute for the copy, paid on top of what you already pay for your data platform

• Pipelines that extract, transform and refresh the data, and the engineers who keep them running

• Security reviews and access management for one more system holding sensitive data

• Delays, because insights are only as current as the last successful data load

None of these is dramatic on its own. Together they often amount to a meaningful share of what you spend on process intelligence. Every copy you can avoid removes some of them.

Ask your vendor: Which of these costs disappear if the analysis runs inside our own data platform, and which remain?

3. How does the cost behave as you scale?

Every process intelligence platform looks affordable in a pilot. One process, a few analysts, a limited data set. The real cost shows up when the program succeeds and you want to roll it out.

So model the situation you are aiming for in three years, not the one you have today. How many processes? How much event data? How many people using the insights, and how many of them only occasionally? Then ask for the commercial cost of that scenario over three years. The operational costs from the previous question come on top.

Look at the price triggers in particular, because plans rarely hold. Pricing per process can make teams hesitant to analyze the next one. Pricing per user can keep insights inside a small expert team, when the real value comes from managers using them every day. And count time as a cost: every month spent on procurement, security reviews and new data pipelines is a month without results.

Ask your vendor: Can you price our three-year target scenario in full, and tell us exactly what happens to the price if we double the number of processes or users?

4. Are you getting answers, or just dashboards?

Process mining earned its place by showing what happens: where the bottlenecks are, how long things take, where cases deviate from the normal path. That is valuable, but it is only the starting point. The people who own processes need to know why it happens and what to change.

So look at how far the platform takes you beyond the process map. Can it find the factors behind delays, rework and exceptions without an analyst spending a week on it? Can it compare how the process actually runs with how it was designed to run? Can a finance manager or a customer service lead get an answer without learning a new tool?

That last point often limits value the most. When only a handful of specialists can work with the platform, most process questions never get asked. In my experience, programs grow when insight reaches the people who make everyday decisions, not when the dashboards get more detailed.

Ask your vendor: Show us how a business user would find the root cause of a delay in our process, from question to answer.

5. What would it cost to leave, and who owns what you have built?

Few buyers ask this at renewal, because nobody plans to leave. But the exit cost is part of the total cost, and it shapes your negotiating position for every renewal that follows.

Think about what you have built over the years: data models, process definitions, KPIs, dashboards, scripts. Where do they live? If you switched tomorrow, which of them could you take with you, and which would you have to rebuild?

The data model matters most. If the process data model is built in your own data platform, it stays there whatever happens to the application, and your other analytics and reporting tools can use it too. If it only exists inside the vendor's platform, leaving means rebuilding it.

Ask your vendor: If we end the contract, what do we keep, in what format, and how much work would it take to reuse it?

6. Does the platform fit your governance, data residency and AI compliance rules?

Process data is rarely harmless. Event logs from finance, HR, customer service or lending processes contain personal data, commercial terms and information your auditors care about. Every copy of that data has to be governed, and every transfer has to be justified.

Privacy and AI rules are tightening, and data sovereignty has become a board-level topic in many industries. In practice, this pushes process intelligence toward a simple principle: analyze the data where it is governed, and move as little of it as possible.

If your organization has spent years setting up access controls, data classification and residency rules in its data platform, the question is whether your process intelligence follows those rules or works around them. Every system that holds its own copy of the data needs its own controls, its own security reviews and its own answer to where the data is processed.

Ask your vendor: Where is our data processed and stored, under whose access controls, and how do we show that to an auditor?

7. Is the platform ready for AI agents?

A renewal commits you for years, and in that time AI agents will become part of how many processes run. That changes what you need from process intelligence in two ways.

First, process insight has to reach more people. AI assistants make that possible, if the process intelligence platform can serve them. Through MCP (Model Context Protocol), an open standard, people can ask process questions in tools they already use, such as ChatGPT, Claude or Microsoft Copilot. The platform analyzes the real process data, and the AI presents the answer. The answer rests on evidence, not on the model's best guess. At Fiduciary Trust International, which analyzes around 500,000 process cases across critical workflows, Leszek Kaldus, Head of Data Strategy & Managing Director, puts it simply: “Through QPR MCP, we can ask any question about our processes and get near real-time answers.”

Second, agents themselves need oversight. An agent run is a process, and process mining can show which paths agents take across thousands of runs, where they loop and what drives their cost. Because agent traces contain prompts and business data, they are best analyzed where they are already stored. (More on AI agent mining)

Ask your vendor: Can our people and AI agents ask questions of our process data in the AI tools we already use? And can we analyze how our own agents work without exporting their traces?

And one question to ask yourself: would you choose it again today?

Put the implementation work and the familiarity aside for a moment. If you were selecting a process intelligence platform for the first time today, with your current data strategy, security requirements and AI plans, would you choose the one you have?

The honest answer to this one tends to tell you more than the other six together.

Put the right people in the room

No single team can answer all seven questions. That is the main reason renewals go on autopilot: the process excellence team owns the contract, but the data, security and cost questions belong to others.

My advice is to start the conversation six to twelve months before the renewal date and to bring the stakeholders together once, early.

Your current platform may well pass this review. If it does, you can renew with confidence and a stronger negotiating position. If it doesn't, you will know early enough to look at alternatives properly.

What a Snowflake-native approach changes

For organizations that already run Snowflake as their enterprise data platform, these questions open up another option: running process intelligence directly where the data lives. We built QPR ProcessAnalyzer as a Snowflake Native App for exactly this reason. It runs in your own Snowflake account, reads your tables with the permissions you grant, and keeps the process data model where your other tools can use it. There is no separate copy of your data to store, secure or keep up to date.

The same architecture carries into AI. With QPR AI Agent Mining for Snowflake, you can turn your agents' own execution traces into process maps and see how they behave, inside your Snowflake account.

If your data lives elsewhere, the other questions still apply, and so do our answers to them. QPR ProcessAnalyzer connects to SQL databases, SAP, Salesforce and other sources through standard interfaces and combines them into one event log. Our licensing doesn't limit the number of processes you analyze. And through the QPR MCP Server, people and AI agents can ask process questions in the AI tools they already use.

If a renewal is coming up, the easiest way to answer these questions is to test with your own data. If your data is in Snowflake, you can install QPR ProcessAnalyzer from Snowflake Marketplace and try it free. Wherever your data is, you can book a session with our team to walk through your renewal scenario.