How to Set Your DPP Data Granularity
One of the first questions when building a Digital Product Passport (DPP) is how detailed the data should be. Too little data risks non-compliance; too much means needless cost. This article shows how to approach that decision methodically.
Two dimensions of detail
Level of detail has two distinct dimensions that are easy to confuse:
- Granularity — whether the passport describes a model, a batch or a single item.
- Depth — how many fields, and how precise, each data layer contains.
You make these two decisions separately. A product can be described at model level yet carry a very rich data set — and the other way round.
Start from the delegated act
The starting point is always the sector. ESPR is a horizontal framework, and the required level of detail is set by the delegated act for a given product group. That act fixes the minimum you cannot go below.
If the delegated act for your sector has not yet been adopted, make reasonable assumptions and design the structure so it can be enriched later. We describe the phased mechanism in the ESPR timeline, and the levels themselves in Model, batch, item.
Who will use the data
The level of detail also depends on the passport's audiences. Different users need different data:
- market-surveillance authorities — data that evidences compliance,
- consumers — information on use, durability and repair,
- recyclers — composition and how to disassemble,
- repairers and B2B partners — technical data and part identification.
If a given type of user does not need certain data, simply adding it does not increase the passport's value.
Value versus effort
A useful approach is to weigh each data item by value and effort. A methodology developed by the EU's research centre (the JRC) sorts data into three groups: essential, strongly recommended and voluntary — precisely on the basis of value against effort.
This is a helpful way of thinking even beyond a formal obligation. You always implement essential data, add recommended data when it brings clear benefit, and add voluntary data when you can get it at low cost. We expand on this in Essential, recommended and voluntary data.
Detail versus maintenance cost
Higher granularity is not only an implementation cost but a maintenance one. Item-level data has to be updated for every unit, and a rich set of fields demands constant attention to quality.
That is why a sensible strategy is to start with the minimum your sector requires and increase detail where it brings real value — for example in after-sales service or recycling.
Design for change
Requirements will change with successive delegated acts. That is why it is worth designing the data structure so it can be extended without a rebuild. A flexible data model is cheaper to maintain than one that has to be recreated every few years.
Example: same product, different levels
Picture the same product described in two ways. In the first, the passport works at model level and carries a rich set of data common to the whole line. In the second, it works at batch level, because composition differs between runs.
Both approaches can be correct — the sector and the variability of the data decide. This example shows that granularity and depth really are separate decisions, worth taking consciously rather than automatically.
Too much data also costs
It is easy to assume that more data is always better. In practice, excess raises maintenance cost and the risk of errors, without necessarily adding value. Data nobody uses still has to be kept up to date.
That is why it is worth asking not only "can we add this" but "who needs it and what for". This discipline keeps the passport concise, complete and easier to maintain.
Common mistakes when setting the level of detail
The most frequent slips are:
- confusing granularity with depth and taking them as one decision,
- going below the minimum your sector requires,
- adding data "just in case", with no audience and no update plan,
- a rigid structure that cannot be extended without a rebuild.
A conscious approach to these limits both the risk of non-compliance and needless cost.
Key takeaways
- Distinguish granularity (model, batch, item) from depth (the number and precision of fields).
- The starting point is the delegated act for your sector — it sets the minimum.
- Match the data to its audiences: authorities, consumers, recyclers and B2B partners.
- Weigh data items by value and effort; the JRC methodology sorts them into three groups.
- Design the structure so it can be enriched when new requirements arrive.
See how CyfroPass lets you match your level of data detail to your sector's requirements. Visit cyfropass.pl and start with a minimum you can build on.