What Does a Data Analytics Project Actually Look Like?

Every project starts the same way with a conversation. Not a sales pitch. Not a demo of software you don't need yet. Just a straightforward discussion about your business and what you're trying to achieve whether that's yield optimisation, waste reduction, quality prediction, or simply understanding what your data is telling you.

In our experience, the most useful first calls cover three things: what's actually going wrong (or what could be going better), what data you think you have, and what you've already tried.

What we'll ask you:

• What business decisions are you currently making based on gut instinct that you'd rather make based on evidence?
• Where do you record your operational data today? (Excel is a perfectly valid answer.)
• What does your team measure already, even informally?
• Have you worked with a data analytics partner before?

What you should ask us:

• Have you worked in our sector before?
• What happens if the data doesn't support what we're hoping to find?
• What will you need from our team, and how much of their time?
• Can we see an example of what a final deliverable looks like?

What "good fit" looks like: You have a specific business question, you have at least some data (even messy data), and someone on your team is willing to work with us for a few hours per week during the project.

What "not yet" looks like: You have no data at all, or the question is so vague that we'd be guessing what to look for. In that case, we'll tell you. Sometimes the honest answer is "start collecting this data for six months, then call us back."

This is where most of the heavy lifting happens and its also where most of the surprises are.

We'll ask you to share whatever data you have. In Ireland, for most SMEs, that means Excel spreadsheets. Sometimes it's a mix of spreadsheets, an ERP system, paper records, and data locked inside equipment that nobody has ever exported. All of that is normal.

What "data readiness" actually means:

It doesn't mean your data is perfect. It means we can work with it. In our experience, 60–80% of any data analytics project is spent cleaning, restructuring, and understanding data and not building models. This isn't a sign something is wrong. It's how the process works.

What happens when data is messy:

We deal with it. That's part of the job. Messy data doesn't disqualify you from a data analytics project. It just means the discovery phase takes longer. What can disqualify a project is insufficient data. If you've only been recording something for three months and you want to predict annual trends, the data may simply not be enough to work with. We'll tell you that early rather than take your money and hope for the best.

Once the data is clean and structured, we start looking for patterns. In plain English, "exploratory data analysis" means: we look at your data from every angle to understand what's actually in there before we try to predict anything.

This is where the first honest conversations happen. Sometimes the data confirms what you expected. Sometimes it contradicts it. Both are valuable.

What we look for:

• Correlations — which factors in your business actually move together? (You might assume product quality depends on one thing, but the data shows it depends on something else entirely.)
• Outliers — unusual data points that might indicate problems, opportunities, or data entry errors.
• Gaps — periods of missing data that affect what we can reliably analyse.
• Distribution — how your data is spread. If 95% of your quality results cluster in one narrow range, predicting variation is harder than if results are spread across a wider range.

First findings:

At this stage, we'll sit down with your team and share what we've found. This isn't the final answer, it's a checkpoint. We might say: "The data suggests your batch yield is strongly linked to ambient temperature and supplier origin, but not to the shift patterns your team suspected. Here's the evidence. Should we pursue this direction?"

This is one of the most important moments in the project. It's where we separate signal from noise and where we agree on what's worth investigating further.

Now we build. This is where the heavy lifting happens. Based on what we found in Phase 3, we develop a predictive model. This is a proof of concept that learns from your historical data to make predictions about new data.

What "training a model" means, in plain English:

First, we do the feature engineering by identifying which variables actually drive your outcomes. In a food manufacturing context, that might mean ambient temperature, raw material moisture content, or supplier origin. Not every variable matters, and part of the expertise is knowing which ones to test. Then we show the model thousands of examples from your past data: "Here's what the conditions were, and here's what happened." Over many iterations, the model learns the patterns. Under the bonnet, it's maths but the output is practical: a tool that can make predictions about new situations it hasn't seen.

How we test it:

We never test a model on the same data we used to build it. We hold back a portion of your data (typically 20–30%), train the model on the rest, and then check its predictions against the data it has never seen. This tells us how well the model will perform in the real world, not just on paper.

What success looks like:

This depends entirely on the problem. For some projects, predicting an outcome with 80% accuracy would be transformative. For others, 95% isn't good enough. We define success criteria with you in advance so there's no ambiguity.

What failure looks like and why it's still valuable:

Sometimes the data doesn't support the prediction. A well-executed analysis that concludes "the data doesn't support this" is a valid outcome. It saves you from investing further in a direction the data does not support. In our experience, even "negative" results reveal something useful. They often point to what data you should be collecting, or which question you should actually be asking.

The project ends with deliverables you can actually use:

• Executive summary — a plain-English document for your leadership team explaining what we found, what it means for the business, and what we recommend. No jargon.
• Technical report — detailed methodology, data quality assessment, model performance metrics, and limitations. This is for your R&D or technical team.
• Working prototype — if applicable, an interactive dashboard or model your team can use. Not always included — it depends on the project scope.
• Recommendations — concrete next steps. What to do with the findings. Where to invest further. What data to start collecting if you aren't already.

What you own:

Everything we produce is yours. The analysis, the models, the reports, the recommendations. We don't hold your data or insights hostage behind a subscription.