Trading Companies & Distributors Data Provider · Head-to-head
UCI Wholesale Customers Dataset vs ECB Payment Statistics – TARGET Services Monthly Settlement Traffic
Which trading companies & distributors data provider data fits your job: UCI Wholesale Customers Dataset, or ECB Payment Statistics – TARGET Services Monthly Settlement Traffic. API, files, or your warehouse. Daily, weekly, or hourly.
UCI Wholesale Customers Dataset
ECB Payment Statistics – TARGET Services Monthly Settlement Traffic
Where the fields line up
No shared field names. These two answer different questions.
| Field | UCI Wholesale Customers Dataset | ECB Payment Statistics – TARGET Services Monthly Settlement Traffic |
|---|---|---|
Channel | documented | not in this set |
Region | documented | not in this set |
Fresh | documented | not in this set |
Milk | documented | not in this set |
Grocery | documented | not in this set |
Frozen | documented | not in this set |
Detergents_Paper | documented | not in this set |
Delicassen | documented | not in this set |
month | not in this set | Calendar month column (Jan-Dec) inside each reported year - the period axis of every cell. |
year | not in this set | Reference year of the annual table the cell belongs to. Aggregate volumes and values run 1999 through 2026; the account-level families start later. |
service_segment | not in this set | Which slice of the Eurosystem's settlement machinery the row reports: all TARGET payments, Central Liquidity Management related payments, real-time gross settlement related payments, transactions on T2S Dedicated Cash Accounts, or transactions on TIPS Dedicated Cash Accounts. |
total_volume | not in this set | Count of payments settled in the month for the named segment - the throughput column, and the one instant-payments adoption reads come from. |
What each contains
Pick by fit, not by loyalty.
| UCI Wholesale Customers Dataset | ECB Payment Statistics – TARGET Services Monthly Settlement Traffic | |
|---|---|---|
| Row identity | `Channel`, `Region` — one row per customer | `Month` column read under a per-year header |
| Volume measure | Six spend integers (`Fresh`, `Milk`, `Grocery`, `Frozen`, `Detergents_Paper`, `Delicassen`) in unlabeled monetary units | `Total volume` — payments settled in the month, per service section |
| Size/value measure | None beyond the spend integers themselves | `Settled values (EUR billions)` |
| Intensity | Not published | `Daily average` per settlement day |
| Segment labels | `Channel` (Horeca 1 / Retail 2), `Region` (Lisbon 1 / Oporto 2 / Other 3) | Service sections: All TARGET payments, CLM, RTGS, T2S DCA, TIPS DCA, split EUR vs DKK |
| Time axis | Single annual snapshot donated March 2014 | Monthly cells across annual tables from 1999 to 2026 |
What each does better
UCI Wholesale Customers Dataset
A segmentation benchmark that needs no cleaning. 440 rows, eight columns, zero missing values — the whole table fits in memory and in your head. Channel and region labels ship with every row, so PCA, k-means or hierarchical clustering separates Horeca from Retail on the Detergents_Paper versus Fresh/Frozen contrast with no feature engineering at all; see customer segmentation.
Published statistics to sanity-check against. Category distributions are documented up front — Fresh spending runs from 3 to 112,151 monetary units with a mean near 12,000 — so a model that misbehaves is your model's fault, not the data's.
Decades of comparability. Donated by Margarida Cardoso in March 2014 and used as a teaching and benchmarking standard ever since, results on this table can be lined up against a long published history — useful context when the job is ML model training and you need a baseline everyone recognizes.
ECB Payment Statistics – TARGET Services Monthly Settlement Traffic
System-scale throughput nobody else documents this completely. Six table families per year: settled volumes and settled values for all TARGET Services back to 1999, traffic on T2 accounts per national component from 2009, T2S Dedicated Cash Accounts from June 2015, plus legacy cross-border and domestic RTGS breakdowns covering 1999–2008.
Infrastructure shifts you can date precisely. TARGET commenced 4 January 1999; T2S went live June 2015; TIPS launched November 2018; the consolidated T2 platform replaced TARGET2 on 17 March 2023, with T2 figures since 2023 revised in March 2025. Any study of euro-area payment migration needs those breakpoints, and only this record carries them.
Volume and value, counted separately. January 2026: 138,507,792 payments worth 73,846.1 billion euros. Within that, instant settlement dominates the count — TIPS Dedicated Cash Account traffic ran 112,978,592 transactions in January against 8,509,478 RTGS-related payments — the kind of structural fact a customer-level ledger structurally cannot supply.
The verdict
Verdict: sample both, pick by fit. Let the question decide. If the question is about customers — whether a channel can be predicted from a basket mix, how spend concentrates across fresh, grocery and frozen, how to teach clustering on real labels — UCI Wholesale Customers Dataset is shaped for it, and slots straight into distributor benchmark work. If the question is about the payment system — euro-area settlement volumes, the drift of traffic into instant payments, seasonal peaks between January and July — ECB Payment Statistics – TARGET Services Monthly Settlement Traffic answers it directly.
Because both records carry the same 8/10 score, neither wins on quality; they win on different questions. Cut each sample to the fields, segments and dates you actually model, and let the returned rows make the call rather than the brand names.
Sample both, pick by fit. See UCI Wholesale Customers Dataset · See ECB Payment Statistics – TARGET Services Monthly Settlement Traffic
Fair questions
Is UCI Wholesale Customers Dataset better than ECB Payment Statistics – TARGET Services Monthly Settlement Traffic?
Different instruments, tied on craft — both score 8/10. UCI wins whenever the subject is a customer: 440 labeled rows of annual spend that support segmentation, classification and teaching out of the box. The ECB wins whenever the subject is the payment system: monthly volumes and values for T2, T2S and TIPS reaching back to 1999. Many desks keep both.
Which dataset reaches further back in time?
The ECB record, decisively: its aggregate tables start with TARGET's first settlement day on 4 January 1999 and run through 2026, with dated breakpoints at T2S (June 2015), TIPS (November 2018) and the consolidated T2 cutover (17 March 2023). UCI holds a single year of spending frozen in March 2014 — deep in comparability, not in calendar.
Do the two datasets overlap anywhere?
Only conceptually. Both measure flows of money and both pre-segment their populations — channel and region labels on one side, service sections and currency sections on the other. But no field, entity or period connects them: UCI names no banks, the ECB names no customers, and neither record can stand in for the other.
Which one should an analyst sample first?
Follow the unit of analysis. Modeling customers or testing a segmentation method — sample UCI first; its eight columns resolve in minutes. Sizing or timing euro-area settlement activity — sample the ECB tables first, starting with the volumes and values families for the years you care about. When the brief spans both, request both cuts together.
Can Datadory deliver both datasets together?
Yes. Either record comes alone or merged onto one calendar alongside the rest of Datadory's trading and distribution coverage, delivered daily, weekly, or hourly — your call. Or take both in one feed.