Power BI consultancy in Dublin: what it costs, what breaks and what to build first

The call from Dublin usually starts the same way: two reports disagree, both have a defender, and the meeting where it matters is on Thursday.

Dublin is large enough that most organisations here already have several analytics teams and the problem is reconciling them. With around 590,000 people in the city itself and roughly 1,460,000 across the wider urban area and an economy built on software and technology, asset and wealth management, and retail and commercial banking, the reporting questions here are specific rather than generic, and so are the ways they go wrong. This page sets out what a Power BI engagement in Dublin actually involves: the metrics we check first, what it costs, how licensing should be chosen, whether you would be better off hiring, and what the compliance position looks like under the EU GDPR and the Data Protection Act 2018.

Why reporting breaks in Dublin specifically

Generic advice about Power BI is not very useful, because the failure modes follow the industry rather than the tool. Dublin's economy is weighted towards software and technology, asset and wealth management, and retail and commercial banking, with the European headquarters of a large share of the global technology industry and a major funds and aircraft leasing centre shaping much of the local employment base. That mix produces a predictable set of reporting problems, and we have seen each of them here more than once.

Asset and wealth management

Position, transaction and valuation data arriving from custodians and portfolio systems on different cycles, with market data licensed under terms that restrict redistribution.

The characteristic failure is this: reporting built on the custodian feed and reporting built on the portfolio system disagree, and nobody owns the reconciliation between them. Where that has already happened, the first deliverable is not a dashboard. It is a written reconciliation that establishes which number is correct and why. In practice we start by building a position and valuation model with an explicit as-at dimension, so every figure states the valuation date it was computed on.

Typical source systems: portfolio management system, custodian files, order management system, market data feeds and CRM. Client identity is subject to professional secrecy duties in several of our markets, and market data licences frequently prohibit onward distribution through a BI tool.

Retail and commercial banking

High-volume transactional data at account and posting grain, with a core banking system that was never designed to be queried analytically and a general ledger that is authoritative but coarse.

Here the recurring problem is different: each function builds its own extract from core banking with its own as-at timing, and the resulting small variances are never reconciled. They are simply explained away each month. The fix is a conformed account and customer dimension with a balance and transaction fact, which immediately resolves the usual disagreement between finance, risk and the front line. Account-level data is both personal data and commercially sensitive, so row-level security and export control are design requirements rather than later hardening.

Software and technology

Product event streams at very high volume alongside subscription, billing and support data that must be joined at account grain to be useful. ARR comes from a finance spreadsheet with manual adjustments, so restating a prior period requires archaeology and diligence becomes painful.

We would usually begin with an effective-dated contract and movement model so ARR is reproducible for any historical date, then product events joined at account grain. Source systems to expect: billing and subscription platform, CRM, product event pipeline, support platform and finance system.

The metrics we check first in a Dublin engagement

Sector knowledge in analytics is mostly knowing which definitions destroy a number when they drift. Before we design anything we go through the measures that matter to you and establish, in writing, exactly how each one is calculated and who owns that definition. The table below is the starting list for Dublin, derived from the industries that dominate here.

None of this is exotic. A measure with two plausible definitions will acquire both, and the resulting variance is then explained away every month instead of being fixed once. The output of this stage is a measure dictionary: every KPI, its business definition, its owner, and the DAX that implements it.

What a first Power BI engagement looks like

We work the same way whether the client is a large group with nine existing workspaces: decisions first, sources second, model third, reports last. Reports built before the model are the single most common cause of rework.

1. Decision inventory, week 1We start from the recurring decisions that need numbers, not from the data that happens to be available. Who decides, on what cadence, and what currently blocks them. For a Dublin business in asset and wealth management, that usually surfaces three or four decisions worth building for and a dozen reports nobody needs.
2. Source profiling, weeks 1 to 2Each source system profiled for grain, volume, change tracking, quality and refresh window. portfolio management system, custodian files and order management system are the usual candidates here. This is where unpleasant surprises belong, not in week ten.
3. Model build, weeks 3 to 7A star schema with conformed dimensions, measures written against the agreed definitions, and row-level security applied from the start. For asset and wealth management the argument that has to be settled first is normally assets under management, where multi-share-class and mandate-level double counting, plus FX translated at the wrong rate date. Settle it before any report exists, not after two of them disagree.
4. Report build and validation, weeks 6 to 10Reports built with the people who will use them and validated line by line against the source of record until the numbers tie out. Disagreements get resolved and written down, never averaged.
5. Handover, weeks 10 to 14Workshops with your team, documentation in your own systems, and a thirty-day period where we fix what breaks while your people drive. Trinity College, UCD and DCU supply strong technical graduates, though multinational employers make the market extremely competitive.

What that costs

Build cost follows project size rather than report count, the number of source systems and their state drive almost all of it. These are the bands we quote in Ireland, and we publish them because a buyer cannot start an approval chain without a budget figure.

Running it afterwards is a separate line: €3,000 to €8,000 a month for a small estate, €8,000 to €20,000 for a mid-sized one, €20,000 to €50,000+ for a large multi-domain platform. Microsoft licensing is separate again, and is covered next.

Licensing: what Dublin organisations should actually pay

This is where money is most often wasted, in both directions. Power BI Pro is listed at around US$14 per user per month and Power BI Premium Per User at around US$24 following the 2025 price revision. Local list prices in sterling, euro and Swiss francs track those figures with regional adjustment, and enterprise agreements usually land below list.

The arithmetic that matters: F64 only becomes cheaper than per-user licensing somewhere in the region of 350 to 600 readers, depending on the discount you actually pay and whether you reserve capacity. Below that, Pro or PPU per user is normally the right answer, and buying capacity for prestige is a straightforward waste of money.

For larger Dublin organisations with hundreds of report consumers, a capacity SKU is usually the right answer, and which one depends on measured consumption rather than headcount. We profile workloads on a short-lived trial capacity before anyone signs.

Always price from the current Microsoft rate card for your region and agreement. Any figure in an article older than a quarter should be treated as an order of magnitude, not a quote.

Hiring in Dublin versus engaging a consultancy

This is a fair question and the honest answer is sometimes "hire". Trinity College, UCD and DCU supply strong technical graduates, though multinational employers make the market extremely competitive. Locally, a mid-level analytics engineer costs roughly €55,000 to €85,000, with Dublin at the upper end. An experienced external consultant runs at roughly €700 to €1,300 per day.

Where a Dublin client has told us they intend to build an internal team, we design for it from the start. The assumption throughout is that your team takes it over. Models are source-controlled and scriptable, naming follows a convention rather than a habit, documentation lands in your own wiki, and the measure dictionary is written to be read by whoever inherits it. Several clients have gone on to need us only for quarterly review, which is a perfectly good outcome.

Data residency and compliance for Dublin organisations

North Europe sits in Dublin, so Irish organisations get in-country residency on the region most of Europe treats as its default. The practical consequence is excellent latency and a short list of residency questions, though cross-border processing still has to be documented where a service falls back to another region. In practice, Dublin deployments use North Europe (Dublin).

Regulatory oversight relevant to analytics comes from the Data Protection Commission (DPC) under the EU GDPR and the Data Protection Act 2018, plus the sector bodies that matter here: Central Bank of Ireland for banking, insurance and funds reporting, Data Protection Commission for data protection, with a notably active enforcement record and HIQA for health and social care quality reporting.

• Ireland hosts the EU establishment of many multinationals, so the DPC is frequently the lead supervisory authority, which raises the bar on analytics documentation well above local headcount.
• The Central Bank's outsourcing and operational resilience expectations apply to cloud analytics platforms used by regulated firms.
• EU-level obligations (DORA, NIS2, CSRD and the AI Act) apply in full, and the Irish implementation timetable is worth tracking per obligation.

We cover this properly on the data governance and compliance page, and in more depth in our article on data governance for Dublin organisations.

How we deliver into Dublin

• English-language delivery with strong overlap with UK working practices makes mixed UK and Irish programmes straightforward.
• Multinational subsidiaries often work to a group architecture decided elsewhere, so local projects need an explicit position on group standards.
• The analytics labour market in Dublin is tight, which pushes many organisations towards a hybrid of a small internal team and external depth.

Delivery is remote-first with on-site workshops at the points where being in the room genuinely helps: definition sessions, validation and go-live. We work in English and Irish, and stakeholder sessions run in whichever of those the room prefers.

How a Dublin organisation actually buys this

Most consultancy pages stop at what the work is and leave the buying process to email. English-language and UK-like in tone, but dominated by multinational subsidiaries whose procurement happens elsewhere. Vendor onboarding often takes longer than the commercial negotiation.

In the private sector: A local sponsor agrees the work, then the supplier is onboarded into a group procurement system (Ariba, Coupa or similar), which can take several weeks and is a separate process from winning the engagement. The group master agreement usually overrides local terms, and a purchase order must exist before any invoice will be paid.

In the public sector: Public bodies buy through eTenders, with framework agreements run by the Office of Government Procurement. Above EU thresholds the requirement also appears on TED. Many framework mini-competitions are decided on a scored submission rather than a meeting, so the written response matters more than the relationship.

Documents you should expect to request, and that any supplier worth shortlisting will have ready, are tax clearance certificate from revenue, checked routinely, including for non-resident suppliers, certificate of incorporation and company registration details, insurance certificates, with professional indemnity at the level the group policy requires and data processing agreement under eu gdpr article 28.

Our own entity details, insurance, processing agreements and contracting shapes are published on the procurement page, so your buying team can start without waiting for a reply.

Signals it is time to call someone

The usual triggers are two reports disagreeing on a headline number, an overnight refresh someone re-runs by hand each morning, a largest model that is one file owned by one person, finance still rebuilding the board pack in a spreadsheet, a legacy BI renewal whose price no longer makes sense, or a question about who can see a sensitive dataset that took a week to answer honestly. If two or more are true, a two to three week paid discovery will tell you what to do about it, and you keep the output whether or not you engage us further. Book a technical review, or read what we actually build:

We work across Ireland and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Cork, Galway, Swords, Bray, Greystones and Limerick.

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