Process modelling & data mapping: the foundation of any AI that reaches production.
Before automating anything, you need to know how your processes really run and what your data is worth. We reconstruct your processes through process mining and map your structured and unstructured sources, with an AI exploitability score.
The problem
You rarely automate what you don’t understand
Most AI and automation projects fail upstream, not on the model. A process gets automated as it is described in a procedure, while the field actually runs three different variants, with manual rework, exception cases and invisible loops. On the data side, you find out too late that the source is incomplete, poorly qualified, has no clear usage rights, or is locked away in PDFs and emails that simply can’t be exploited as they are.
The result: brittle automations and AI assistants that hallucinate for lack of reliable material. The cause is almost always the same: a poorly modelled process and data whose true exploitability is unknown.
Our approach in 4 steps
Process mining (as-is)
We reconstruct your real processes from the logs and event journals of your systems (ERP, CRM, ticketing), not from what’s declared on paper. Variants, bottlenecks, rework and exception cases appear exactly as they exist.
Target processes (to-be)
We rethink each process for AI and automation, estimating the expected gain step by step: time, quality, automation rate, workload avoided.
Data mapping
An inventory of structured sources (ERP, CRM, databases, APIs, warehouse) and unstructured ones (documents, contracts, emails, tickets, PDFs, images), linked to the process steps they feed.
AI exploitability scoring
For each source: availability, quality, usage rights, governance and AI exploitability. You know what is ready, what needs to be prepared and what is blocking.
Deliverables
What you walk away with
- As-is process maps reconstructed through process mining, variants included
- To-be target processes redesigned for AI, with the gain estimated per step
- A map of your structured and unstructured data sources
- An AI exploitability score per source (availability, quality, rights, governance)
- The data prerequisites and compliance workstreams to clear before industrialisation
- A prioritised view of high-gain, low-prerequisite automations
Industrialising AI, backed by the data & compliance DNA of Datanaos
Frequently asked questions
What is process mining, in practical terms?
It’s the reconstruction of a process from the traces left in your systems: event journals, timestamps, status transitions. You get the real sequence, with all its variants, where a workshop based on what people declare only ever shows the idealised process.
Why map unstructured data as well?
Because a large share of AI’s value lives there: contracts, emails, tickets, PDFs, images. We assess their exploitability (extraction, RAG, classification) on the same footing as ERP or CRM data.
What data do we need to provide for process mining?
Extracts of logs or event journals from the relevant systems (case ID, activity, timestamp). The exact scope is defined during framing, within a strict confidentiality framework.
How does Datanaos strengthen this engagement?
The engagement draws on Datanaos’s data and compliance DNA: data governance, usage rights and quality are taken seriously, which secures the move of use cases into production.
Move from experimentation to AI in production
Start with a short, fixed-price assessment: maturity, high-ROI use cases, and a prioritised roadmap. No commitment.