Payments Over €20,000 Q1 2018

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2018	Version 1 Software	FMS support for 2018	Purchase Order	€98,893.23
31 Mar 2018	University of Sheffield	Modelling of comparative impacts of unit	Purchase Order	€64,270.04
31 Mar 2018	Tomorrow's World Ltd.	Helpdesk DOH December 2017	Purchase Order	€22,829.39
31 Mar 2018	Tobacco Free Research	Research services re ESPAD	Purchase Order	€23,872.31
31 Mar 2018	Raymond Comyn	Discovery services	Purchase Order	€21,330.80
31 Mar 2018	PA Consulting Services Ltd	Final invoice for Capacity Review 2018	Purchase Order	€152,372.40
31 Mar 2018	McKinsey and Co Inc	Development of a Response to Slaintecare Report	Purchase Order	€217,398.43
31 Mar 2018	Irish Estates Mgt Ltd	Service charge/Rent Q2	Purchase Order	€32,234.95
31 Mar 2018	Irish Estates Mgt Ltd	Rent Q1	Purchase Order	€24,625.00
31 Mar 2018	Energia	Jan 2018	Purchase Order	€23,745.28
31 Mar 2018	Dept of Children and Youth Affairs	Documentary Counsel Work	Purchase Order	€26,225.78
31 Mar 2018	Deloitte Ireland LLP	Work undertaken for EMA bid	Purchase Order	€25,000.00
31 Mar 2018	Advertising Standard Authority	Services for AMCMB	Purchase Order	€20,000.00

^* 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