Purchase Orders Over €20,000 Q2 2014

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2014	Gartner Ireland Ltd	IT Executives CIO & GTP,	Purchase Order	€172,077.00
30 Jun 2014	Dell Computer	dell equalogic 6210,	Purchase Order	€22,981.50
30 Jun 2014	Storm Technology	SharePoint Support May'14,	Purchase Order	€47,844.54
30 Jun 2014	DQ Networks Ltd	Inv 15395 7/6/14 Re: May'14,	Purchase Order	€22,156.85
30 Jun 2014	Equita Consulting	Wm Beausang Consultancy 2014,	Purchase Order	€49,200.00
30 Jun 2014	VIL (ECS)	550 phone handset upgrades,	Purchase Order	€48,031.50
30 Jun 2014	IBM Global Services	3 IBM 3550 Servers,	Purchase Order	€27,859.50
30 Jun 2014	Storm Technology	SharePoint Sup Apr'14 Inv5164,	Purchase Order	€55,762.05
30 Jun 2014	Storm Technology	SharePoint sup Mar'14 Inv5135,	Purchase Order	€58,603.35
30 Jun 2014	Eolas Recruitment	Employment costs	Purchase Order	€22,693.50
30 Jun 2014	Microsoft Ireland	Premier Support Agreement,	Purchase Order	€57,238.05
30 Jun 2014	Storm Technology	Inv No. 5081 Sharepoint Feb'14,	Purchase Order	€61,330.88
30 Jun 2014	Storm Technology	Inv No 3979 - Support 2014,	Purchase Order	€20,664.00
30 Jun 2014	Davy Corporate	Davy Cor Fin(5 phase Nat.Lot),	Purchase Order	€282,900.00
30 Jun 2014	VIL (ECS)	HP 5900 switches	Purchase Order	€27,322.49

^* 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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