Purchase Orders Over €20,000 Q4 2020

Spending records

Payment date^*	Supplier	Description	Kind	Amount
22 Dec 2020	Plus Architecture	Professional fees	Purchase Order	€30,403.09
22 Dec 2020	Fitzgerald Kavanagh & Partners	Professional fees	Purchase Order	€35,011.30
17 Dec 2020	McCann Fitzgerald	Professional fees	Purchase Order	€36,900.00
11 Dec 2020	Nicholas O'Dwyer	Professional fees	Purchase Order	€76,560.12
08 Dec 2020	O'Donnell Tuomey	Professional fees	Purchase Order	€1,135,899.28
02 Dec 2020	Premier AV	Professional fees	Purchase Order	€21,622.70
02 Dec 2020	Nolan Group	Professional fees	Purchase Order	€62,039.10
27 Nov 2020	EY Ernst & Young	Professional fees	Purchase Order	€47,232.00
16 Nov 2020	Farrell Brothers (Ardee) Limited	Services	Purchase Order	€86,335.92
03 Nov 2020	Hays Recruitment	Professional fees	Purchase Order	€32,705.31
14 Oct 2020	Nicholas O'Dwyer	Professional fees	Purchase Order	€29,485.56
14 Oct 2020	Fergus Martin	Professional fees	Purchase Order	€80,000.00
08 Oct 2020	Purcell Construction	Construction	Purchase Order	€97,264.31
06 Oct 2020	Alex Carr	Professional fees	Purchase Order	€35,000.00
06 Oct 2020	Cabinpac Construction	Construction	Purchase Order	€718,678.60
06 Oct 2020	Weslin Construction	Construction	Purchase Order	€1,183,556.64

^* 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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