Teams often wait for a finished PIM project before automating datasheets. In most cases, that wait is unnecessary — and costly.
The common assumption
Many industrial and manufacturing companies already have product data somewhere. Some of it sits in a PIM, some in the ERP, some in spreadsheets that a product manager maintains by hand. Some of that data may already be structured well enough for a defined first flow.
What is missing is not the data. What is missing is a repeatable way to turn that data into a finished datasheet without a person assembling it in a template every time.
A common response to that problem is to postpone it. The reasoning is straightforward: the PIM programme is not finished, the data model will change, and automating on top of an unstable foundation feels premature. So datasheet production stays manual until the PIM is "done."
That plan has one flaw. Product updates, new SKUs and market launches do not pause for a data programme.
The document flow does not wait for the architecture roadmap.
The real question is not whether a PIM should exist. It is whether datasheet automation has to wait for it.
What a PIM is good at
A PIM is built to manage product data: attributes, translations, classifications, variants and channel content in one governed place. For any company selling technical products across multiple markets, that is a sound investment.
It gives product owners and data teams one place to correct an error once instead of six times, and one source that sales, marketing and e-commerce can all rely on.
That is what a PIM is designed to do, and it does it well.
What a PIM does not automatically solve
A PIM manages information. A datasheet is a designed document with a template, a layout, a review path and an approved output format. Those are two different disciplines, and a mature PIM does not remove the second one.
PIM output modules may handle straightforward publishing, but complex template logic, market-specific exceptions and formal sign-off often require a separate production layer. Teams still export data, open a template, correct the layout by hand and route the file for approval. Where PIM systems struggle with document production covers this distinction in more depth.
For this article, the point is narrower: a PIM programme and a datasheet automation project solve different problems, so one does not have to be finished before the other can start.
Starting without a mature PIM
Datasheet automation does not need perfect, centralised data. It needs data that is structured enough and stable enough for one defined use case.
In practice, that data can come from several sources at once: an Excel export, a CSV feed from the ERP, an API connection, or even a temporary export while the PIM programme is still in progress. What matters is not where the data lives, but whether the fields, identifiers and rules behind one datasheet type are consistent enough to automate.
This is a different starting point than most teams expect. Instead of "wait for one clean source," the working assumption becomes "start with the sources that already produce reliable output for a defined scope."
What must be structured first
Starting early does not mean starting without preparation. A first automated flow needs a small number of conditions in place before it can run reliably.
- Stable identifiers. Every product needs one consistent reference across the sources being used.
- Complete required fields. The attributes that appear on the datasheet need to be populated for the products in scope, not for the entire catalogue.
- Repeatable rules. The layout logic — what appears, what is optional, what triggers a warning — needs to be defined once and apply consistently.
- A named reviewer. Someone needs to be accountable for approving output before it goes out.
How Pulse fits in
2imagine Pulse sits in the layer between data sources and finished output. It connects to whatever holds the relevant data today — PIM, ERP, a spreadsheet, or a combination — and generates the datasheet from defined templates and rules.
As the underlying data environment matures, Pulse does not need to be replaced. The connections and rules are updated; the output logic stays in place. Wolf Oil is a useful reference point here: its datasheet flow now runs on a fully structured, PIM-triggered process, generated through the same kind of output layer described in this article. It shows where a flow can end up once the data foundation is mature — not a requirement for where a flow has to start.
Pulse delivers output to the systems and locations a team already uses, configured per workflow rather than through a single fixed connector.
A realistic first project
A useful starting point is one product family, one datasheet template and one market. Not the full catalogue, and not every language variant at once.
For that scope, the team defines the identifiers, confirms the required fields are populated, sets the layout rules and names the reviewer. Pulse generates the datasheet from that setup, and the team checks the first batch of output against what the manual process used to produce.
Once that flow is reliable, it extends — to more product families, more markets, more document types. Each extension reuses the same structure instead of starting over.
Review one datasheet flow before redesigning your data landscape
Start with one product family, one data source, one template, one review path and one delivery destination. That quickly shows which data is already usable, and which gaps are actually blocking automation.
When waiting makes sense
Starting early is not always the right call. Waiting makes sense when:
- product identifiers are not yet consistent across the sources involved, and no interim mapping is realistic;
- the required fields for the datasheet in scope are largely missing, not just incomplete;
- the layout and approval rules are still actively changing week to week; or
- no one is available to own the review and sign-off step.
In those situations, automating first would mean encoding an unstable process. It is more efficient to fix the specific blocker, then automate.
The better question
The question is not "is our PIM finished?" It is "is there one datasheet flow, today, with stable identifiers, complete fields and a named reviewer?"
If the answer is yes, that flow can be automated now, independently of the wider data programme. If the answer is no, the fix is usually narrower than a full PIM rebuild — it is one missing field, one undefined rule, or one unassigned reviewer.
Datasheet automation does not replace the PIM project. It shows, quickly and concretely, where the organisation's data is genuinely ready to be used — and where it only looks ready on paper.