Payments Over €20,000 Q4 2019

Spending records

Payment date^*	Supplier	Kind	Amount
23 Dec 2019	DNV GL Limited Professional Services	Purchase Order	€29,221.98
23 Dec 2019	Qualcom Systems Ltd IT Managed Services	Purchase Order	€51,448.47
13 Dec 2019	Jack O'Connell Professional Services	Purchase Order	€22,353.23
13 Dec 2019	Cambridge Economic Policy Ass. Professional Services	Purchase Order	€37,130.00
13 Dec 2019	NCPS Rent and Managed Services	Purchase Order	€60,921.90
06 Dec 2019	Cambridge Economic Policy Ass. Professional Services	Purchase Order	€30,148.52
28 Nov 2019	Easter Bay Consultants Ltd Professional Services	Purchase Order	€24,360.00
28 Nov 2019	Qualcom Systems Ltd IT Managed Services	Purchase Order	€34,509.65
28 Nov 2019	Energy Exemplar (Europe) Ltd. IT Licence Fees	Purchase Order	€41,854.00
14 Nov 2019	GH Investments Ltd Rent and Managed Services	Purchase Order	€102,983.25
07 Nov 2019	Yellow Wood Energy Consulting Professional Services	Purchase Order	€27,144.00
17 Oct 2019	DNV GL Limited Professional Services	Purchase Order	€30,849.18
10 Oct 2019	Benchmark Property Rent and Managed Services	Purchase Order	€39,878.65
03 Oct 2019	Willis Towers Watson Insurance Professional Services	Purchase Order	€72,482.11
03 Oct 2019	NERA UK Limited Professional Services	Purchase Order	€250,880.11

^* 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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