From Excel to Predictive Analytics: A Practical Guide for Irish Businesses
COO, Deep Purple AI Consulting
Quick Summary
Most Irish SMEs are sitting on years of production data in spreadsheets — temperature logs, quality records, batch reports, supplier data. This guide explains what becomes possible when you move beyond Excel: from basic reporting to predictive analytics that can forecast quality, reduce waste, and optimise yield. We walk through a real anonymised scenario, show what the journey looks like, and explain how government grants can cover 50–80% of the cost.
Looking for funding? Most of this work qualifies for Enterprise Ireland and LEO grants covering 50–80% of costs.
Part of Our Data Analytics Series
This article is part of our practical guide to data analytics and AI for Irish SMEs. The series covers:
Start here — the comprehensive overview
A step-by-step walkthrough of the project process
From Excel to Predictive Analytics: A Practical Guide for Irish Businesses
Reading nowHow Irish food producers use data to improve yield and reduce waste
Turning compliance data into competitive advantage
Looking for funding? Most of this work qualifies for Enterprise Ireland and LEO grants covering 50–80% of costs.
Introduction
A food producer came to us with three years of production data — over 500 batches, each with 120+ recorded variables. Temperatures, moisture levels, ingredient ratios, supplier codes, ambient conditions, line speeds. All in Excel. They wanted to know: can we predict final product quality before it leaves the production line?
That question — and the journey from Excel to predictive analytics — is one we see again and again from Irish SMEs. The data exists. It's been collected for years, often for compliance or quality management purposes. But nobody has ever asked what else it could tell you.
This guide is for business owners and operations managers in Ireland who suspect their spreadsheet data could be doing more. We'll walk through what "more" actually looks like, what it costs, and how to get there — step by step.
What Counts as "Data" in Your Business?
Most businesses have more usable data than they think. The challenge isn't usually quantity — it's awareness. People don't realise that the spreadsheets they maintain for compliance, quality control, or operations are exactly the raw material that predictive analytics needs.
In food production and manufacturing:
- •Production batch records (temperatures, times, pressures, line speeds)
- •Incoming raw material specifications (moisture, fat content, supplier, origin)
- •Quality test results (taste panels, lab results, visual inspections, grading)
- •Environmental conditions (ambient temperature, humidity, seasonal variation)
- •Supplier performance data (delivery times, consistency, rejection rates)
- •Equipment maintenance logs (downtime, calibration, cleaning cycles)
- •Yield and waste figures (giveaway percentages, rework rates, non-conformance)
In construction and field services:
- •Project costings and actuals (labour, materials, time)
- •Defect and snag lists (type, location, frequency)
- •Subcontractor performance records
- •Weather and scheduling data
In professional services:
- •Client engagement records (hours, scope, outcomes)
- •Revenue per client, per service, per team member
- •Pipeline and conversion tracking
If any of this sounds familiar, you already have the starting material. The question is what you do with it.
The Excel Problem (And Why It's Not Really About Excel)
Let's be clear: there's nothing wrong with Excel. It's the most widely used data tool in the world, and for good reason. Most Irish SMEs run significant parts of their business through spreadsheets, and that's completely normal.
The limitation isn't Excel itself — it's what happens when the questions you want to ask outgrow what a spreadsheet can answer.
Excel is excellent for:
- •Recording and storing structured data
- •Basic calculations, totals, averages
- •Simple charts and reporting
- •Filtering and sorting
- •Sharing data across a small team
Excel starts to struggle when:
- •You have thousands of rows across multiple sheets with different formats
- •You want to understand which of 50+ variables actually drives a particular outcome
- •You need to find non-obvious patterns across years of data
- •You want to predict what will happen next, not just describe what already happened
- •You need to combine data from multiple sources (ERP, spreadsheets, sensors, lab systems)
To put this in real terms: the food producer we mentioned had 500 batches × 120 variables. That's 60,000 data points. You can store that in Excel. You can't meaningfully analyse it in Excel — not because the software fails, but because a human can't hold 120 relationships in their head at once. That's the point where analytics tools and statistical modelling take over.
The move from Excel to predictive analytics isn't about replacing your spreadsheets. It's about asking different questions of the same data. Your spreadsheets become the input — the raw material — for analysis that Excel was never designed to do.
What Becomes Possible
Once you move beyond reporting into genuine analytics, three categories of insight open up. These are drawn from real projects we've delivered for Irish food producers and manufacturers. In the data analytics food industry space, these opportunities are particularly strong — because food production generates large volumes of consistent, measurable data.
Predicting Product Quality
The Scenario
A food producer records 120+ variables for every production batch — temperatures at multiple stages, raw material specifications, environmental conditions, line settings. Final quality is graded after production, sometimes days later.
What Analytics Can Do
Build a model that predicts final quality grade during production, based on the variables being recorded in real time. Instead of discovering a quality issue after the batch is complete, you catch it while there's still time to adjust.
What This Means in Practice
Fewer batches downgraded. Less product reworked or wasted. Better consistency. And evidence — not just instinct — about which production conditions drive quality.
Forecasting Yield and Reducing Waste
The Scenario
A manufacturer tracks yield and giveaway across every production run, but can't explain why some runs produce 3% waste while others produce 8%.
What Analytics Can Do
Identify the variables most strongly correlated with waste — which might be raw material moisture content, supplier origin, ambient conditions, or equipment settings. Once you know the drivers, you can control for them.
What This Means in Practice
Even a 1-2% improvement in yield on a high-volume line can be worth tens or hundreds of thousands of euro per year. The data tells you where to look.
Detecting Problems Before They Reach the Customer
The Scenario
Non-conformance reports are filed after the fact — a customer complaint, a failed quality check, a product recall. The data to spot the pattern existed in the production records, but nobody connected the dots until it was too late.
What Analytics Can Do
Build anomaly detection models that flag unusual patterns in production data before the product ships. Early warning systems based on the same data you're already collecting.
What This Means in Practice
Fewer customer complaints. Fewer recalls. Better audit performance. And a defensible, data-driven quality system — which is increasingly what retail multiples like Tesco, Musgrave, and M&S expect from their suppliers. With Bord Bia's Origin Green programme placing growing emphasis on measurable quality and sustainability data, having a systematic, evidence-based approach to quality management is becoming a competitive necessity, not a luxury.
The Journey: Spreadsheet → Dashboard → Prediction
Not every business needs to jump straight to predictive models. In our experience, the journey from spreadsheet data to predictive analytics follows a natural progression — and most Irish SMEs are at Level 1 or 2.
| Level | Stage | What It Looks Like | What You Can Do |
|---|---|---|---|
| Level 1 | Spreadsheet Chaos | Data exists but it's scattered across files, inconsistent, and hard to find | Basic record-keeping, manual reports |
| Level 2 | Basic Reporting | Regular reports from consistent data, usually in Excel | Monthly summaries, trend lines, KPI tracking |
| Level 3 | Analytics | Cleaned data feeding dashboards, with patterns identified | Interactive dashboards, correlation analysis, root cause investigation |
| Level 4 | Predictive | Models forecasting outcomes based on historical data | Quality prediction, yield forecasting, demand planning |
| Level 5 | AI-Driven | Automated decisions based on continuous learning | Real-time optimisation, automated quality control, self-adjusting processes |
Where most Irish SMEs sit today: Level 1-2. And that's not a criticism — it's the starting point for almost every project we've worked on. The path from Level 2 to Level 3 is where the biggest immediate value lies for most businesses. The jump from Level 3 to Level 4 is where predictive analytics comes in.
The important thing: This is incremental. You don't need to go from Level 1 to Level 4 in one project. A well-designed analytics engagement can move you from Level 1 to Level 3 in 4-6 weeks, with a clear roadmap for what Level 4 would look like when you're ready.
Why This Matters
A Real Scenario: From 500 Batches in Excel to a Working Predictive Model
Here's how this played out for one of our clients — a food producer with three years of production data sitting in Excel.
What they had:
- •500+ production batches recorded over 3 years
- •120+ variables per batch (temperatures, moisture levels, ingredient specifications, supplier data, environmental readings, equipment settings)
- •Final quality grades assigned after production
- •All in spreadsheets — some well-structured, some not
What they wanted to know:
Can we predict the final quality grade from the production data? And if so, which variables matter most?
What happened:
Data Discovery
We spent the first two weeks cleaning and restructuring their data. Some spreadsheets had different column names for the same measurements. Some variables were recorded differently across years. About 15% of records had gaps. This is normal — and it's why data discovery typically takes 60–80% of a project's time.
Exploratory Analysis
Once the data was clean, we started looking for patterns. Some variables the client expected to matter — didn't. Others they'd never considered turned out to be strongly correlated with quality outcomes. Ambient temperature and one specific raw material specification explained more variation than the production parameters the team had been adjusting for years.
Modelling
We built a predictive model that could estimate final quality grade based on the production variables available during manufacturing. The model was tested on data it had never seen (held back specifically for this purpose) and achieved an accuracy level the client considered commercially useful.
Reporting
We delivered an executive summary, a technical report, and a working prototype the team could use. The key finding wasn't just the model — it was the ranked list of which variables actually drive quality, which allowed the operations team to focus their attention on what the data says matters, not what they'd always assumed.
The outcome: The client now has evidence-based priorities for quality improvement, a working prediction tool, and a clear roadmap for what to collect and monitor going forward.
Honest Note
What You Need to Start
You don't need perfect data, a data science team, or expensive software. Here's what you actually need:
| What You Need | The Ideal | The Reality (And It's Fine) |
|---|---|---|
| Data history | 12+ months of consistent records | "We have 3 years but some months are missing" — that works |
| Data volume | 300-500+ records (batches, orders, projects) | "We have about 200" — possibly enough, depends on the question |
| Recorded variables | 20+ variables per record | "We have maybe 15 columns" — that's a starting point |
| Consistency | Same measurements recorded the same way | "Different people enter it differently" — we deal with that |
| An outcome to predict | A measurable result (quality, yield, cost, time) | "We grade everything A/B/C" or "We track yield %" — perfect |
| A business question | A specific decision you want data to inform | "Why does quality vary?" or "Can we reduce waste?" |
| Someone who understands the data | 3-5 hours/week from your operations or quality lead | Your person doesn't need to understand analytics — just the business |
The most common blocker we see isn't bad data — it's no question. If you don't know what you're trying to predict or understand, the best data in the world won't help. Start with the business problem, not the technology.
A Note on Data Security
What It Costs
A typical engagement to move an Irish SME from Excel data to working analytics or a predictive model costs between €15,000 and €40,000 for a 3-6 week project. The range depends on data complexity, scope, and whether the project includes a working prototype.
| Starting Point | What We Deliver | Typical Cost | Duration |
|---|---|---|---|
| Level 1 → Level 3 | Cleaned dataset, interactive dashboards, correlation analysis | €15,000–€20,000 | 3–4 weeks |
| Level 2 → Level 4 | All above + predictive model + prototype | €20,000–€30,000 | 4–5 weeks |
| Level 1 → Level 4 | Full data audit, analysis, predictive model, dashboard suite | €30,000–€40,000 | 5–6 weeks |
What drives the price: Data quality is the biggest factor. If your data is well-structured and consistent, we spend less time on discovery and more on analysis. If we need to reconcile data from five different spreadsheet formats and three different systems, that takes longer — and the price reflects it.
For a detailed walkthrough of what each phase involves and where the effort goes, see our guide to what a data analytics project actually looks like.
Most of this work qualifies for Irish government grants covering 50–80% of costs:
- •Starting with an assessment? Enterprise Ireland's Digital Discovery grant covers €5,000 at 80% funding — your cost: €1,250 for a professional data audit and roadmap.
- •Ready for a full project? The Digital Process Innovation grant covers up to €150,000 at 50% funding.
- •Under 50 employees? LEO grants offer similar support through digital consultancy schemes.
A €25,000 analytics project with a 50% grant becomes a €12,500 investment. The data you already have does the rest.
Frequently Asked Questions
Can predictive analytics really work with Excel data?
Yes. Most of our projects in Ireland start with data that lives in Excel. Spreadsheets are a perfectly valid data source. The key is whether the data is consistent enough over time and has enough records to analyse. We've built working predictive models from data that arrived as a folder of spreadsheets on a shared drive.
How much data do I need for predictive analytics?
As a general rule, 300-500+ records (batches, orders, projects) with at least 12 months of history gives us enough to work with. Fewer records can sometimes work depending on the question, but below 100-150 records it's difficult to build reliable models. More important than volume is consistency. Are the same variables recorded the same way over time?
What if our data is messy?
Most data is messy. In our experience, 60-80% of any analytics project is spent cleaning and restructuring data, not building models. Missing values, inconsistent formatting, duplicate entries. These are normal, not disqualifying. We deal with them as part of the process. What matters is whether enough usable data exists underneath the mess.
How long before we see results?
A typical project runs 3-6 weeks from data handover to final deliverables. You'll see first findings (exploratory analysis) by week 2-3. The timeline depends on data complexity. Well-structured data means faster results.
What's the difference between analytics and AI?
In practical terms, analytics means finding patterns and relationships in your data, understanding what happened and why. Predictive analytics (a subset of AI) means using those patterns to forecast what will happen next. Most businesses benefit from analytics first, prediction second. We don't push AI where simpler analysis would do the job.
Do we need to stop using Excel?
No. Your Excel spreadsheets become the input for the analytics. We extract, clean, and analyse the data, but you can keep recording in whatever system works for your team. If the project leads to ongoing monitoring or dashboards, we'll recommend the right tool for that. However, it doesn't have to replace your existing workflow.
Ready to Find Out What Your Data Can Tell You?
Not sure if your spreadsheet data is ready for analytics? Book a 20-minute call and we'll assess your situation, explain what a project would look like for your business, and check your eligibility for the 80% Digital Discovery Grant — before you commit to anything.
Book a 20 minute discovery callAbout 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?
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Enterprise Ireland Grants Guide 2026: Up to €400,000
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LEO Grants Guide 2026: Free Consultancy to €150,000
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AI Grants Ireland: Complete Guide to Funding in 2026
Every AI and digital transformation grant available to Irish businesses, in one place.
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