Payments Over €20,000 Q1 2019

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2019	BDO	Audit Fees	Purchase Order	€21,000.00
31 Mar 2019	Micromail	Licensing	Purchase Order	€21,000.00
31 Mar 2019	Smith and Williamson	Audit Fees	Purchase Order	€22,000.00
31 Mar 2019	Media Central Ltd	Future Proof Partnership	Purchase Order	€22,000.00
31 Mar 2019	Trinity College Dublin	Science Week Evaluation	Purchase Order	€25,000.00
31 Mar 2019	Science Europe C/o FWO	Annual Membership fees	Purchase Order	€29,000.00
31 Mar 2019	Silicon Republic Knowledge & Events Managmnt	Silicon Republic Content Partnership	Purchase Order	€31,000.00
31 Mar 2019	Elsevier Ltd	Membership Subscription	Purchase Order	€50,000.00
31 Mar 2019	Drury (Porter Novelli)	Advertising Cross Charges	Purchase Order	€55,000.00
31 Mar 2019	The Irish Times Ltd	Innovation Partnership	Purchase Order	€62,000.00
31 Mar 2019	Optimum Media Direction (Ireland) Ltd	Advertising Campaign	Purchase Order	€89,000.00
31 Mar 2019	Michael Collins Associates Ltd	Design Team Professional fees for Office Move	Purchase Order	€130,000.00
31 Mar 2019	PFH TECHNOLOGY GROUP UNLIMITED COMPANY	Computer Hardware	Purchase Order	€260,000.00
31 Mar 2019	IDA Ireland	WPH Rent, Rates, Service Charge, Restaurant, Facilities, Utilities & 3PP Rates & Service Charge	Purchase Order	€345,000.00

^* 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