Purchase Orders in Excess of €20,000 Q2 2021

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2021	KPMG	Purchase order	Purchase Order	€44,427.50
29 Jun 2021	KPMG	Purchase order	Purchase Order	€27,680.00
29 Jun 2021	Crowleys DFK	Purchase order	Purchase Order	€109,694.55
28 Jun 2021	Kilkenny Education Centre	Purchase order	Purchase Order	€32,000.00
28 Jun 2021	Pricewaterhouse Coopers	Purchase order	Purchase Order	€237,788.52
25 Jun 2021	Shane Leavy	Purchase order	Purchase Order	€27,060.00
25 Jun 2021	Conscia Limited	Purchase order	Purchase Order	€38,164.64
25 Jun 2021	KPMG	Purchase order	Purchase Order	€45,568.60
24 Jun 2021	KPMG	Purchase order	Purchase Order	€21,033.00
24 Jun 2021	Indecon Economic Consultants	Purchase order	Purchase Order	€55,883.00
09 Jun 2021	Crystal Cleaning Maintenance Services Ltd	Purchase order	Purchase Order	€21,451.84
13 May 2021	KPMG	Purchase order	Purchase Order	€36,257.33
06 May 2021	KPMG	Purchase order	Purchase Order	€44,833.50
06 May 2021	Version 1 Software	Purchase order	Purchase Order	€88,483.74
27 Apr 2021	The Abbey Tavern (John Kendrick)	Purchase order	Purchase Order	€24,174.30
08 Apr 2021	Grant Thornton Corporate Finance Limited	Purchase order	Purchase Order	€49,200.00
08 Apr 2021	Crowleys DFK	Purchase order	Purchase Order	€101,421.74

^* 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.

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