Purchase Orders Over €20,000 Q1 2025

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2025	DELL (IRELAND)	Purchase order over €20,000	Purchase Order	€34,715.52
31 Mar 2025	DELL (IRELAND)	Purchase order over €20,000	Purchase Order	€44,064.75
31 Mar 2025	DELL (IRELAND)	Purchase order over €20,000	Purchase Order	€44,064.75
31 Mar 2025	DELAP & WALLER LTD	Purchase order over €20,000	Purchase Order	€30,135.00
31 Mar 2025	CONTENT ONLINE AB	Purchase order over €20,000	Purchase Order	€47,252.81
31 Mar 2025	COLLIN LAB & PILOT SOLUTIONS GmbH	Purchase order over €20,000	Purchase Order	€377,831.40
31 Mar 2025	COLEMAN ELECTRONICS LTD	Purchase order over €20,000	Purchase Order	€59,048.58
31 Mar 2025	CLARE GAA COUNTY BOARD	Purchase order over €20,000	Purchase Order	€35,000.00
31 Mar 2025	ATHLETIC ASSOCIATION OF IRELAND	Purchase order over €20,000	Purchase Order	€25,041.90
31 Mar 2025	ASCENSION LIFTS LTD	Purchase order over €20,000	Purchase Order	€68,979.12
31 Mar 2025	ALVATEK LTD	Purchase order over €20,000	Purchase Order	€23,251.99
31 Mar 2025	AGILENT TECHNOLOGIES IRELAND LTD	Purchase order over €20,000	Purchase Order	€56,834.98
31 Mar 2025	AFARA ED LTD	Purchase order over €20,000	Purchase Order	€24,600.00
31 Mar 2025	ADVANCED SAFETY MANAGEMENT LTD TA ASM GROUP	Purchase order over €20,000	Purchase Order	€105,059.72

^* Payment date may be the end of the reporting quarter when the entity does not publish an exact date.

How to read these figures

These are reported values from published purchase order and payment records — not verified cash paid, and not total spending by this organisation. A row may be a purchase order, a payment, or another reported transaction; the source files often do not say which.

Coverage and quality vary. Some fields are blank, supplier names are free text (the same firm can appear under many labels), and descriptions are inconsistent rather than a controlled taxonomy. An amount alone says nothing about value for money and it may or may not include VAT.

Records are retrieved from each organisation’s published files such as spreadsheets, PDFs, and other formats of uneven quality. Extraction techniques (including automated parsing) can introduce gaps, misreads, or incomplete rows when source layout or quality is poor. Treat the data as a starting point for investigation, not a definitive ledger.

Here is a project that encountered similar issues