Purchase Orders over €20,000 Q2 2022

Spending records

Payment date^*	Supplier	Description	Kind	Amount
11 Apr 2022	Ernst & Young	Information and communication testing, ICT Security and Finance Resources	Purchase Order	€176,600.00
11 Apr 2022	Ernst & Young	Information and communication testing, ICT Security and Finance Resources	Purchase Order	€165,000.00
11 Apr 2022	Apex Surveys Ltd	Topographical surveying	Purchase Order	€21,246.55
11 Apr 2022	Ernst & Young	Information and communication testing, ICT Security and Finance Resources	Purchase Order	€42,447.88
11 Apr 2022	ESB Networks Ltd	Utilities	Purchase Order	€50,003.14
08 Apr 2022	Jacobs Engineering Ireland Ltd	Engineering and Design services	Purchase Order	€33,799.96
08 Apr 2022	Fimak Group Limited t/a Credit Card Systems Ireland Limited	Leap Card Services	Purchase Order	€45,000.00
07 Apr 2022	Aecom Ireland Limited	Technical and Engineering Design	Purchase Order	€186,456.00
06 Apr 2022	Codec-css Limited	Web Site Hosting Costs	Purchase Order	€40,000.00
04 Apr 2022	Cawley NEA Ltd T/A TBWA Dublin	Advertising	Purchase Order	€115,575.00
04 Apr 2022	KPMG	Contracting services	Purchase Order	€228,000.95
01 Apr 2022	Havas media Ireland Ltd	Marketing	Purchase Order	€170,939.35
01 Apr 2022	Eyecue	Graphic design and creative	Purchase Order	€31,274.93

^* 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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