Overview
With data cleaning, you can clean transaction data specifically for Analytics to obtain reliable price developments and KPIs – without removing data from the system. The feature provides a central, reusable basis for Analytics instead of manual exclusion filters in individual views.
Data cleaning consists of two building blocks: central filter rules and the labeling of individual transactions with predefined labels.
What this means in practice:
More reliable results: price developments and KPIs are based on relevant transactions.
No data loss: all transactions remain fully preserved and visible.
Central logic: rules are defined once and apply consistently to all reports.
1. Filter rules
You define which transactions are excluded, e.g. by Supplier, Category, Article or label. There are two separate filter rules:
KPI Analysis (also applies to Delivery Reports)
Price Development (also applies to Currency Reports)
A preview shows how many transactions are excluded by the rule. Excluded data remains available in the system and can be included again at any time.
2. Labeling individual transactions (labels)
Individual Orders and Invoices can be labeled in the transaction tables via the "Assign labels" action:
Sample: the transaction deviates from the regular price due to sample costs
Credit note: e.g. for returns or quality problems
Error: a posting error that can no longer be corrected
Launch: the transaction is affected by a ramp-up or launch phase
Cancellation: the transaction was cancelled
These labels can then be specifically included or excluded in the filter rules or as a filter in Analytics.
3. Application in Analytics
The filter rules can be applied in Analytics by default. Use the "Data cleaning active" toggle in the transaction type menu to switch them off temporarily if needed. This state can also be saved in a custom view.
Typical use cases
Excluding collective articles: avoids distorted price developments, while the purchasing volume remains available in the KPI Analysis as usual.
Excluding transactions with labels: data errors that can no longer be corrected retroactively in the ERP system are cleaned up using the labeling feature combined with an exclusion in the filter rules.
Excluding indirect spend: not relevant for many analyses and can therefore be excluded by default.
Data cleaning settings
Open the Settings in the left-hand navigation.
Under "Tools", navigate to "Analytics".
In the "Data cleaning" section, enable the option "Apply data cleaning filters by default" and define the filter rules for KPI Analysis and Price Development.
Permissions & access
Filter rules: can be managed by Users with permission to edit the organization settings (by default Admins and Key Users).
Assign labels: requires the "manage Analytics data cleaning" permission (by default Admins and Key Users; can be assigned to further groups).
All other Users: use the centrally defined Analytics logic.
Known limitations
The filter rules apply uniformly to all Users of the organization. Restricting them to individual Users is not possible.