Purchase Orders Over €20,000 Q1 2015

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Dec 2015	Aramark	Building Maintenence	Purchase Order	€43,000.25
30 Dec 2015	GG & MA CQ 34930	Rent	Purchase Order	€313,558.59
22 Dec 2015	PWC	IT Consultancy	Purchase Order	€20,586.78
21 Dec 2015	Arthur Cox	SIDS Refund	Purchase Order	€72,050.00
21 Dec 2015	PWC	IT Consultancy	Purchase Order	€21,475.25
15 Dec 2015	PWC	IT Consultancy	Purchase Order	€47,305.30
08 Dec 2015	HSE	SIDS Refund	Purchase Order	€28,130.00
10 Nov 2015	PWC	IT Consultancy	Purchase Order	€28,383.21
02 Nov 2015	Rathowen	Building Fit Out	Purchase Order	€31,597.89
20 Oct 2015	GG & MA	Rent	Purchase Order	€314,381.57
19 Oct 2015	PWC	IT Consultancy	Purchase Order	€51,328.28
05 Oct 2015	PCP	IT Maintenance	Purchase Order	€22,493.63
15 Sep 2015	Cork County Council 034777	B129379 Fee Refund	Purchase Order	€30,000.00
04 Aug 2015	PWC	ICT Consultant	Purchase Order	€36,438.90
04 Aug 2015	PWC	ICT Consultant	Purchase Order	€36,904.33
14 Jul 2015	Mediavest	Public Notices	Purchase Order	€21,634.59
13 Jul 2015	GG & MA	Rent	Purchase Order	€314,381.57
26 May 2015	Aramark	Facilities Management	Purchase Order	€21,343.05
05 May 2015	NRH	SIDS LAPS Refund	Purchase Order	€61,926.00
27 Apr 2015	GG & MA	Rent	Purchase Order	€314,381.57

^* 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