Payments Over €20,000 Q2 2018

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2018	COPENHAGEN ECONOMICS	Consultancy/Policy Review Expenditure: BREXIT STUDY ON TRADE IMPACTS	Purchase Order	€51,321.75
30 Jun 2018	OECD	Consultancy/Policy Review Expenditure: OECD Review of SME and Entreprenuership	Purchase Order	€85,000.00
30 Jun 2018	AECOM LIMITED	Consultancy/Policy Review Expenditure: Study of Skills Implications of Brexit	Purchase Order	€68,277.30
30 Jun 2018	INDECON	Consultancy/Policy Review Expenditure: Evaluation of Seed and Venture Capital Scheme	Purchase Order	€28,852.85
30 Jun 2018	EVROS	Server Hardware	Purchase Order	€67,700.43
30 Jun 2018	EVROS	Server Hardware	Purchase Order	€47,891.48
30 Jun 2018	EVROS	ICT Helpdesk Services	Purchase Order	€20,532.15
30 Jun 2018	EVROS	Managed Hosting Service	Purchase Order	€27,225.81
30 Jun 2018	MICROMAIL	Microsoft Licences	Purchase Order	€47,733.13
30 Jun 2018	ORACLE EMEA LIMITED	Oracle License Support Renewal 2018/2019	Purchase Order	€273,999.62
30 Jun 2018	M J FLOOD	.6 Backup software licence renewal	Purchase Order	€28,929.00
30 Jun 2018	STATE CLAIMS AGENCY	Reimbursement of funds to State Claims Agency	Purchase Order	€141,796.42

^* 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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