Data Analytics for Irish SMEs: A Practical Guide to Getting Real Value from Your Data
COO, Deep Purple AI Consulting
Quick Summary
Irish SMEs make up 99.8% of all businesses in the country and employ over two thirds of the workforce. Most of them are sitting on years of valuable data in spreadsheets, ERPs, paper records, and disconnected systems, but very few are using that data to make better decisions. This guide explains what data analytics actually means for an Irish SME, what it costs in practice, what results to expect, and how government grants can fund 50 to 80% of the work. Whether you run a food manufacturer, a professional services firm, a logistics operation, or a construction business, this is the starting point.
Your Data Analytics Resource Hub
This is our comprehensive guide to data analytics for Irish SMEs. For more detail on specific topics, explore the series:
A step-by-step walkthrough of timelines, phases, and costs
How to move from spreadsheet data to working predictive models
Predicting quality outcomes and reducing waste
Turning compliance data into a competitive edge
Looking for funding? Most data analytics work qualifies for Enterprise Ireland and LEO grants covering 50–80% of costs. See our complete guide to AI grants in Ireland.
Introduction
Data analytics has become one of those terms that means everything and nothing. Enterprise vendors use it to sell platforms that cost hundreds of thousands of euro. Consultancies use it to pitch six-month strategy projects. LinkedIn influencers use it to promote courses. None of that is particularly useful if you are running a 40-person food manufacturer in Cork, a construction firm in Galway, or a professional services business in Dublin trying to figure out whether your data could actually help you run your business better.
This guide is written for you. Not for multinationals with dedicated data teams. Not for tech companies already swimming in analytics. For the 389,000+ SMEs that make up the backbone of the Irish economy and that are, in our experience, almost always sitting on more useful data than they realise.
We are Deep Purple AI Consulting. As a provider of data analytics consulting in Ireland, we are Enterprise Ireland and LEO approved, and we have spent years helping Irish SMEs turn their existing data into operational improvements. This guide draws on that experience. It is honest about what works, what costs, and where the limits are.
What Data Analytics Actually Means for an Irish SME
Forget the enterprise definitions. For a typical Irish SME, data analytics means answering questions like:
- •Why does our yield vary between shifts?
- •Which customers are actually profitable once we account for support time?
- •Where are the bottlenecks in our production process?
- •Can we predict which projects will go over budget before they do?
- •Why is quality inconsistent, and is it related to raw materials, equipment, or process?
These are not theoretical questions. They are the questions that keep operations managers, MDs, and business owners awake at night. And the answers are almost always already sitting in data you already collect — you just cannot see the patterns because the data is scattered across spreadsheets, paper forms, email threads, and disconnected software systems.
Most Irish SMEs do not have a data problem. They have a data structure problem. The information exists. It is just not organised in a way that lets you see what it is telling you. Fixing that structure is the first and most impactful step, and it is often far simpler and cheaper than people expect.
Where Most Irish Businesses Start
In our experience working with SMEs across manufacturing, food production, construction, logistics, and professional services, the starting point is remarkably similar regardless of industry.
Spreadsheets everywhere
Excel is the backbone of most Irish SMEs. Sales data, production records, quality logs, project tracking, financial reporting. It works well enough for individual tasks, but the data lives in silos. No one has ever connected the production spreadsheet to the quality spreadsheet to the customer complaints log.
Paper records that never get digitised
Batch sheets, inspection forms, delivery dockets, timesheets. The information is captured but locked on paper, invisible to any kind of analysis.
Disconnected software systems
An ERP that does not talk to the CRM. An accounting package separate from the project management tool. A quality system that does not link to production data. Each system holds part of the picture, but nobody has the full view.
Reports that look backward
Monthly management reports that tell you what happened last month. By the time you see the problem, it is already weeks old. There is no early warning, no forward-looking analysis.
Gut feeling decision making
Experienced managers making good decisions based on instinct, but struggling to explain or scale those decisions. When that person leaves or retires, the knowledge goes with them.
If any of this sounds familiar, you are not behind. You are where 85% of Irish SMEs are. The CSO's Information Society Statistics show that only 15% of Irish enterprises were using AI in 2024, up from 8% in 2023. The majority of businesses have not yet started their analytics journey, which means there is a genuine competitive advantage for those who do.
What Is Actually Possible: The Four Levels
Data analytics is not a single thing. It is a progression. Here is what each level looks like for a real business, along with what it costs and what it delivers.
Structured Reporting — See what happened, clearly and quickly
What it is
Taking the data you already have (spreadsheets, ERP exports, paper records) and structuring it into consistent formats so you can generate reliable reports and dashboards. For many companies looking for business intelligence in Ireland, this is the foundation.
What it delivers
A single source of truth. No more arguing about whose spreadsheet is correct. Management reports that take minutes instead of days. Visibility of trends, exceptions, and patterns you could not see before.
Investment
Typical cost: €5,000 to €15,000 · Timeline: 4 to 8 weeks · Tools: Power BI, Looker, Excel with structured templates, or similar.
This is where we start with most clients. It is not glamorous but it is where 80% of the value often sits. For a detailed walkthrough of what this phase involves, see our guide to what a data analytics project actually looks like.
Diagnostic Analytics — Understand why things happen
What it is
Going beyond "what happened" to answer "why." Correlating production variables with quality outcomes. Linking customer behaviour to profitability. Identifying which process variables drive your best and worst results.
What it delivers
Root cause identification. The ability to see that yield drops correlate with a specific raw material supplier, or that project overruns cluster around a particular project type, or that quality issues spike when ambient temperature exceeds a threshold.
Investment
Typical cost: €10,000 to €30,000 (often combined with Level 1) · Timeline: 6 to 12 weeks.
This is where most SMEs start to see returns they did not expect. The data reveals patterns that experienced managers suspected but could never prove. We cover this progression in detail in our guide to moving from Excel to predictive analytics.
Predictive Analytics — Know what is likely to happen next
What it is
Using historical data to build models that predict future outcomes. Quality prediction in manufacturing. Demand forecasting. Equipment failure prediction. Project risk scoring. Customer churn prediction.
What it delivers
Early warning. The ability to intervene before problems occur rather than reacting after the fact. Optimised resource allocation based on predicted demand. Reduced waste through better quality prediction.
Investment
Typical cost: €15,000 to €50,000+ · Timeline: 8 to 16 weeks.
This is where the term "AI" becomes genuinely relevant, not as a marketing buzzword but as a practical tool. We have written extensively about what this looks like in practice for food manufacturers predicting quality and for businesses turning compliance data into intelligence.
Prescriptive Analytics — Know what to do about it
What it is
Systems that do not just predict outcomes but recommend actions. Automated alerts when conditions suggest a problem. Optimisation algorithms that adjust production parameters. Decision support systems that surface the right information at the right time.
What it delivers
Faster, better decisions with less reliance on individual expertise. Consistent application of best practice across shifts, sites, and teams. Reduced decision fatigue for managers and operators.
Investment
Typical cost: €30,000 to €80,000+ (builds on Levels 1 through 3) · Timeline: 12 to 24 weeks.
Most SMEs do not need to reach Level 4 immediately. But knowing it exists helps you plan your data architecture properly from the start, so you do not have to rebuild everything when you are ready.
What This Looks Like by Industry
The principles are the same across industries, but the applications vary. Here is what data analytics typically looks like in the sectors we work with most.
| Industry | Common Starting Point | Typical Application | Detailed Guide |
|---|---|---|---|
| Food manufacturing | Paper batch records, disconnected quality and production systems | Quality prediction, yield optimisation, traceability, supplier analysis | Predictive Quality, Batch Traceability |
| Manufacturing (general) | ERP data never fully utilised, production reports in Excel | OEE dashboards, downtime analysis, predictive maintenance, production scheduling | Excel to Predictive |
| Construction | Project management tools not connected to financial data | Project profitability analysis, cost overrun prediction, resource utilisation | Project Process |
| Professional services | Time tracking disconnected from billing and project management | Client profitability analysis, utilisation optimisation, project scoping accuracy | Project Process |
| Logistics and distribution | Route and delivery data captured but not analysed | Route optimisation, delivery time prediction, fleet utilisation, demand forecasting | Excel to Predictive |
| Retail | POS and stock data in separate systems | Demand forecasting, stock optimisation, customer behaviour analysis, margin analysis | Excel to Predictive |
The common thread is always the same: your business already generates the data. The value comes from structuring, connecting, and analysing it.
What It Actually Costs
This is where most guides become vague. We are going to be specific, because understanding costs is essential for making a good decision.
| What You Need | Typical Investment | What You Get |
|---|---|---|
| Data discovery and roadmap | €3,000 to €6,000 | Professional assessment of your current data landscape, gap analysis, and a prioritised roadmap for what analytics could deliver. |
| Structured reporting and dashboards | €5,000 to €15,000 | Connected data sources, clean reporting, management dashboards. The "see clearly" phase. |
| Diagnostic and correlation analysis | €10,000 to €30,000 | Root cause analysis, correlation modelling, pattern identification. The "understand why" phase. |
| Predictive models | €15,000 to €50,000+ | Working prediction systems for quality, demand, risk, or other business outcomes. |
| Integrated decision support | €30,000 to €80,000+ | Automated alerting, optimisation, and decision support systems. |
These are realistic ranges based on our experience with Irish SMEs. Costs vary with the complexity of your data, the number of sources that need to be connected, and how clean your current data is.
The Good News: Government Grants Cover 50 to 80%
Irish SMEs have access to some of the best government funding in Europe for data analytics and digital transformation projects.
Enterprise Ireland's Digital Discovery grant covers 80% of costs up to €5,000 for an initial assessment and roadmap. For an out-of-pocket cost of approximately €1,250, you get a professional data audit, gap analysis, and prioritised plan.
Larger implementation projects qualify for grants covering up to 50% of costs. The €85 million Digital Transition Fund provides additional support specifically for SME digitalisation.
LEO grants are available for businesses with fewer than 50 employees. See our complete guide to AI grants in Ireland for every available option.
How to Know If You Are Ready
Not every business is ready for a data analytics project. Here are the signals that suggest you are, and the signals that suggest you should wait.
You are probably ready if:
- •You have at least 12 months of historical data in any format (spreadsheets, ERP, paper records, databases)
- •You have specific business questions you want to answer, not just a vague sense that data might help
- •Someone in your business has the authority and time to engage with a project (typically 2 to 4 hours per week during the project)
- •You are willing to invest in structuring your data, not just buying a tool
- •Your data sits across multiple disconnected sources and you know the full picture is hidden
You should probably wait if:
- •You do not have a specific business question or problem you are trying to solve
- •Your business processes are changing rapidly (analytics on unstable processes will not be useful)
- •No one in the business has time to engage with the project
- •You are looking for a magic tool rather than a process improvement
Honest Note
We turn away projects where we do not think we can deliver a clear return. If your data is too thin, your processes are too chaotic, or you are not ready to commit the time needed, we will tell you. We would rather have an honest conversation upfront than take your money and deliver something that gathers dust. This is a principle we apply to every engagement, and it is one reason our clients come back.
The Typical Project Process
A data analytics project for an Irish SME generally follows five phases. The timeline varies, but the structure is consistent.
Discovery
We come to your business. We map your data sources, understand your processes, and identify the questions worth answering. This phase is about listening, not selling.
Data Preparation
This is always the hardest part. Approximately 60% of project effort goes into cleaning, structuring, and connecting your data. It is not exciting, but it is where the foundation is built.
Analysis and Modelling
With clean, structured data, we build the reports, dashboards, or predictive models that answer your business questions. This is where the value becomes visible.
Validation and Handover
We test everything against reality, train your team, and make sure the outputs are trusted and usable. A model nobody uses is a waste of money.
Ongoing Support
We do not disappear after delivery. We provide ongoing support, help with adoption, and evolve the solution as your business and data change.
For a much more detailed walkthrough of each phase, including what to expect week by week, see our dedicated guide: What Does a Data Analytics Project Actually Look Like?
Common Mistakes to Avoid
We have seen enough data projects — both our own and those we have been called in to rescue — to know where things go wrong.
Starting with the tool, not the question
"We need Power BI" is not a project brief. "Why does our margin vary by 15% between similar jobs?" is. Start with the business question. The tool follows.
Skipping the strategy
A dashboard that answers three important questions next month requires a clear SME data strategy — understanding which data matters, how it connects, and what decisions it should inform. Buying software without this thinking leads to expensive shelfware.
Underestimating data preparation
Every business thinks their data is "mostly clean." It never is. Budget for 50 to 60% of the project effort going into data preparation. This is normal and expected.
Building for perfection instead of progress
A dashboard that answers three important questions next month is worth far more than a comprehensive analytics platform that takes nine months to build. Start small, prove value, then expand.
Ignoring the people side
The best analytics system in the world is worthless if the people who need to use it do not trust it or understand it. Training, change management, and stakeholder engagement are not optional extras.
Not connecting data sources
Analysing production data without connecting it to quality data, customer feedback, and financial data gives you a partial picture. The most valuable insights come from correlations across data sources.
What Results to Expect
We are careful about making promises, because every business is different. But here is what we typically see across projects.
Within the first 6 weeks
Structured, reliable reporting that eliminates manual report compilation. Management reports that used to take days now take minutes. This alone often justifies the investment.
Within 3 months
Diagnostic insights that reveal patterns you could not see before. Root cause identification for quality issues, cost overruns, or performance variation. These are the "I suspected that but could never prove it" moments.
Within 6 to 12 months
Measurable operational improvements. Reduced waste. Better resource allocation. Faster decision making. More consistent quality. The specific metrics depend on your business, but typical returns range from 3x to 10x the initial investment over 12 months.
These are realistic expectations, not marketing claims. We document our methodology and expected outcomes in every project proposal, and we measure results against those expectations.
Getting Started
If you have read this far and recognise your business in what we have described, here is the practical next step.
Take stock of your data
Spend 30 minutes listing every place your business stores data. Spreadsheets, databases, ERP systems, paper records, email inboxes, shared drives, cloud platforms. Most business owners are surprised by how many sources they have.
Identify your biggest question
What is the single most important business question you wish you could answer with data? Not three questions. One. The answer to that question is your starting point.
Get a funded assessment
Enterprise Ireland's Digital Discovery grant covers 80% of the cost of a professional data assessment and roadmap. For approximately €1,250 out of pocket, you get a clear picture of what your data can deliver, what it would cost, and what the expected return would be.
Frequently Asked Questions
How much does data analytics cost for an Irish SME?
Most projects range from €5,000 for basic reporting and dashboards to €50,000+ for predictive models and decision support systems. The Enterprise Ireland Digital Discovery grant covers 80% of costs up to €5,000 for an initial assessment, bringing your out-of-pocket cost to approximately €1,250. Larger implementation projects qualify for grants covering up to 50% of costs through Enterprise Ireland and LEO.
Do we need to replace our existing software systems?
No. Most data analytics projects start by connecting and structuring the data you already have. Your ERP, spreadsheets, and existing systems remain in place. The analytics layer sits on top, pulling data from your current sources. We do not replace your systems; we make them work harder.
How long does a typical project take?
A data discovery and roadmap project takes 2 to 4 weeks. A reporting and dashboard project takes 4 to 8 weeks. A full predictive analytics project takes 8 to 16 weeks. The biggest variable is data preparation: how clean and structured your current data is. For a detailed week-by-week walkthrough, see what a data analytics project actually looks like.
What data do we need to have before starting?
At minimum, 12 months of historical data in any format. Spreadsheets are perfectly fine as a starting point. Paper records can be digitised as part of the project. The most important thing is not the format of your data but the business question you want to answer with it. See our guide on moving from Excel to predictive analytics for how this works in practice.
Is this just for manufacturing, or does it apply to service businesses?
Data analytics applies to any business that generates operational data. We work across manufacturing, food production, construction, logistics, professional services, and retail. The specific applications differ by industry (quality prediction in manufacturing, project profitability in construction, utilisation analysis in services), but the methodology and approach are consistent. See the industry table above for examples.
What government grants are available for data analytics projects in Ireland?
Several. Enterprise Ireland's Digital Discovery grant covers 80% up to €5,000 for initial assessments. The Digital Transition Fund (€85 million programme) supports SME digitalisation. Enterprise Ireland implementation grants cover up to 50% of larger projects. LEO grants serve businesses with fewer than 50 employees. Ireland's four European Digital Innovation Hubs provide subsidised support for AI and data analytics adoption. See our complete guide to AI grants in Ireland.
How do we know if the investment will pay off?
The Digital Discovery assessment is designed to answer exactly this question. For €1,250 out of pocket, you get a professional evaluation of your data, the opportunities it contains, and the expected return. If the numbers do not make sense, you have spent €1,250 to find that out before committing to a larger project. If they do make sense, you have a funded roadmap to follow. Either way, you are making an informed decision.
What about GDPR and data security?
We operate under strict NDAs with every client. Your data remains secure, GDPR compliant, and within your control at all times. We never use client data to train public AI models or share it with other clients. Where data processing takes place, we ensure it complies with Irish and EU data protection requirements. If your business has specific data residency requirements, we design our approach around them.
Ready to Find Out What Your Data Can Do?
Book a 20-minute call and we will discuss your data, your business questions, and whether a funded Digital Discovery assessment is the right next step. No pressure, no jargon, just an honest conversation.
About Barry Gough
COO, Deep Purple AI Consulting
Barry Gough is the COO of Deep Purple AI Consulting. With an MSc in Computer Science from University College Dublin — where machine learning was a core focus of his studies — and over 20 years building production software systems, Barry brings formal ML training and deep hands-on engineering experience to every AI and data analytics engagement.
Barry completed his masters at UCD in 2011, studying ML algorithms, statistical modelling, and data-driven systems at a pivotal moment — just as big data techniques were maturing and deep learning was about to transform the industry. At Purpledecks (Deep Purple's predecessor consultancy), he spent nearly a decade progressing from Senior Developer to Head of Operations, leading the technical delivery of enterprise projects that increasingly incorporated machine learning, computer vision, data classification, predictive features, and recommendation engines for commercial clients across Ireland and the UK.
In 2023, as CTO of Reactable AI, Barry architected and built an autonomous AI marketing engine from the ground up — a self-learning system that generates and optimises marketing campaigns across channels. This was one of Ireland's earliest production deployments of autonomous AI agents, requiring him to design systems where AI made real decisions with real consequences.
At Deep Purple, Barry leads all technical delivery — AI system architecture, machine learning model development, data pipeline engineering — and manages a team of PhD-level data scientists. His combination of formal ML education, a decade of incorporating AI into commercial projects, and hands-on experience architecting autonomous AI systems means clients work directly with a technical lead who can make genuine engineering decisions about AI.
Related Resources
What Does a Data Analytics Project Actually Look Like?
A step-by-step walkthrough of the full project process.
From Excel to Predictive Analytics
Starting from spreadsheets? Here is the practical journey to prediction.
Predictive Quality in Food Manufacturing
How production data predicts quality, improves yield, and reduces waste.
Data Driven Batch Traceability
Turning compliance data into a competitive edge for food manufacturers.
AI Grants Ireland: Complete Guide
Every grant available for AI and digital transformation projects.
Your Data Is Already an Asset. You Just Cannot See It Yet.
Let us show you what is hiding in your spreadsheets, ERPs, and operational records. Book a free call to discuss how a funded Digital Discovery assessment could reveal the opportunities in your data.