Purchase Orders Over €20,000 Q3 2014

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Sep 2014	BYTES SOTWARE SERVICES	ICT Capital Acquisition	Purchase Order	€26,439.00
30 Sep 2014	UNITY TECHNOLOGY SOLUTIONS	ICT Managed Services	Purchase Order	€63,283.50
30 Sep 2014	PREMIER RECRUITMENT INTERNATIONAL	Agency Staff Fees	Purchase Order	€22,691.29
30 Sep 2014	PRICEWATERHOUSECOOPERS	Professional Fees	Purchase Order	€47,970.00
30 Sep 2014	CAPITA IB SOLUTIONS (IRELAND) LTD	Database Support Maintenance	Purchase Order	€27,060.00
30 Sep 2014	VITAE CONSULTING	Training & Development	Purchase Order	€28,440.68
30 Sep 2014	ARTHUR COX	Legal Fees	Purchase Order	€74,415.00
30 Sep 2014	PREMIER RECRUITMENT INTERNATIONAL	Agency Staff Fees	Purchase Order	€23,665.20
30 Sep 2014	PREMIER RECRUITMENT INTERNATIONAL	Agency Staff Fees	Purchase Order	€21,062.03
30 Sep 2014	PREMIER RECRUITMENT INTERNATIONAL	Agency Staff Fees	Purchase Order	€21,062.03
30 Sep 2014	HAYS RECRUITMENT	Agency Staff Fees	Purchase Order	€24,671.43
30 Sep 2014	HAYS RECRUITMENT	Agency Staff Fees	Purchase Order	€23,915.51
30 Sep 2014	HAYS RECRUITMENT	Agency Staff Fees	Purchase Order	€22,277.15
30 Sep 2014	PREMIER RECRUITMENT INTERNATIONAL	Agency Staff Fees	Purchase Order	€45,614.67

^* 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