We turn the data you already collect into decisions you can act on — descriptive and predictive analytics, forecasting, and machine-learning-driven insight for customer and operations questions. Tool-agnostic, senior-led, and built so you own the result.
Last updated: 11 June 2026
Data analytics consulting turns raw data into decisions: descriptive analytics explains what happened, predictive analytics forecasts what is likely next, and AI-driven analytics recommends what to do. Crux Digits builds this insight and modelling layer tool-agnostically, integrating with whatever BI tool you already use, EU AI Act and GDPR first.
Most companies are not short on data. They are short on answers. Reports pile up, spreadsheets multiply, and yet the questions that actually move the business — which customers are about to leave, where next quarter's demand is heading, which operational bottleneck is quietly costing the most — stay unanswered. Data analytics consulting exists to close that gap: to turn the numbers you already collect into decisions a manager can act on this week. That is the work Crux Digits does. We are a boutique AI consultancy in Nieuwegein, in the province of Utrecht, founded in 2022, and we build the analytics and modelling layer that sits on top of your data and produces insight you can defend.
We deliberately position this as the insight layer. The plumbing underneath it — the pipelines, the warehouse, the clean and governed tables — is a separate discipline, and we treat it as one. If your foundation is not yet solid, that is data engineering work, and we will tell you so honestly before anyone starts building models on sand. This page is about what happens once the data is reliable: the descriptive and predictive analytics, the forecasting, and the machine-learning-driven insight that turns a warehouse into a decision engine.
The phrase "data analytics services" covers a wide range, so it helps to be concrete about the layers we work across. Each one answers a different kind of question, and a good engagement usually moves through them in order.
This is the foundation of understanding — clean metric definitions, a single agreed version of each KPI, and the segmentation that lets you see which customers, products or regions drive a number rather than just the aggregate. A surprising amount of value lives here. When two departments finally agree on what "active customer" or "on-time delivery" means, decisions stop being arguments about whose number is right.
This is where predictive analytics earns its keep. Instead of reporting last month, you forecast next month: demand for a SKU, the probability a customer churns, the expected load on a support queue, the risk that a shipment arrives late. These are models, and building them well is a craft — but the output is plain: a ranked list, a probability, a forecast with a confidence range that a planner can plug straight into a decision.
The most useful analytics does not stop at a prediction; it recommends an action. AI-driven analytics means using machine learning to surface the patterns a human analyst would never find in time — anomalies in operations data, the combination of factors that precedes a cancellation, the early signal of a quality problem — and routing that insight to the person who can act on it.
Across our 13 delivered case studies, the engagements that paid back quickest tended to cluster in a few areas. These are good first projects because the data usually already exists and the decision they support is frequent and measurable.
You will notice we describe outcomes, not tools. That is on purpose. The point of an analytics project is the decision it improves, not the technology stack behind it.
Here is where we differ from a lot of firms that show up under data analytics company searches. Crux Digits is not a Power BI shop and not a Tableau specialist, and we will not pretend to be one to win the work. Plenty of agencies sell dashboard-building as the whole job. We think that gets the value backwards.
What we build is the analytics and modelling layer — the forecasts, the scores, the customer and operations models, the governed metric definitions and the machine-learning that produces the insight. That layer is tool-agnostic. If your team already lives in a particular BI tool, we integrate with it and feed it clean, modelled outputs through a well-defined interface. If you have a data team that prefers a different visualisation front end next year, the insight layer does not need rebuilding, because the intelligence was never trapped inside a single vendor's dashboard. You own the models and the logic; the chart is just the last centimetre.
This matters for a practical reason. The half-life of a BI tool inside a growing company is short. The half-life of a well-built churn model, a trusted forecast or a clean metric definition is measured in years. We invest your budget in the part that lasts.
Three of our services sit close together, and clients sometimes ask where one ends and the next begins. The distinction is worth being precise about, because spending in the wrong layer is a common and expensive mistake.
Data engineering is the plumbing: ingestion pipelines, the warehouse or lakehouse, transformation, data quality and governance. It answers the question "is the data reliable, complete and in one place?" Nothing on this page works without it. If your data is scattered across systems, undocumented or untrustworthy, that is where the first euro should go — and we say so even when it delays the more exciting analytics work.
This page — the insight layer — assumes that foundation exists and asks the next question: "what does the data tell us, and what should we do?" It produces the metrics, the forecasts, the customer and operations models, and the decisions they support. It is where data becomes useful to a non-technical leader, and where most of the visible value of an analytics programme actually shows up.
When an analytics question needs a trained model — a churn classifier, a demand forecaster, an anomaly detector — that modelling work is machine learning. On smaller engagements the line blurs, and the same senior people handle both. On larger ones we scope them separately so you can see exactly what you are paying for. Either way, the model is in service of the insight, not the other way round.
Put simply: data engineering gets the data right, machine learning builds the model, and data analytics turns both into a decision. For the strategic view across all three, our AI consulting page maps how they fit a full roadmap.
We deliver fixed-scope projects, not staff. There is no body-shop, no nearshore pool and no "dedicated team" you rent by the month. Senior people stay on your project from first call to handover, and at the end the client owns the solution — code, models, documentation and all. The pricing follows a clear ladder, excluding VAT: an AI Audit and Strategy at €2,500 to find the highest-value analytics opportunity and check the data behind it; a Proof of Concept at €20,000 to prove the model and the decision it supports on your real data; and a production launch from €50,000 when the insight needs to run reliably and feed live decisions. Work outside that ladder runs at roughly €150 per hour.
Every engagement is EU AI Act and GDPR first. Because analytics so often touches customer and personal data, we handle compliance at the design stage — data minimisation, lawful basis, and risk classification — rather than retrofitting it after a model is already in production. Founder and MD Tom Joseph stays close to the work, and the same hands that scope the project write the analytics that ships.
If you want to see how this lands in practice, our case studies show analytics and forecasting projects across several sectors, and the full pricing breakdown sits on the pricing page. When you are ready to talk through where data analytics would pay off for your business, the fastest route is to get in touch for a free consultation — bring your messiest question and we will tell you honestly whether the data can answer it.
Data engineering is the foundation: the pipelines, warehouse and data-quality work that get your data reliable, complete and in one place. Data analytics is the insight layer that sits on top of it, turning that reliable data into metrics, forecasts and decisions. They are separate disciplines, and spending in the wrong one is a common, costly mistake. If your data is scattered or untrustworthy, we will tell you to start with our data engineering service before any analytics work begins, because no model is better than the data underneath it.
No, and we will not pretend to. Crux Digits is not a Power BI shop or a Tableau specialist. What we build is the analytics and modelling layer — the forecasts, scores, customer and operations models, and governed metric definitions — which is tool-agnostic by design. If your team already lives in a particular BI tool, we integrate with it and feed it clean, modelled outputs through a well-defined interface. The chart is the last centimetre; we invest your budget in the intelligence behind it, which is what actually lasts as tools change.
A concrete, usable output rather than another report about the past. Predictive analytics produces things like a demand forecast for a product with a confidence range, a probability that a specific customer will churn, the expected load on a support queue, or the risk that a shipment arrives late. Each of these is a model output a planner or manager can plug straight into a decision. We agree the target and how success is measured up front, and we test the model on your real data so the accuracy you see reflects your environment, not a clean demo set.
We work to a fixed-step ladder, excluding VAT. An AI Audit and Strategy is €2,500 and finds the highest-value analytics opportunity while checking whether your data can support it. A Proof of Concept is €20,000 and proves the model and the decision it supports on your real data. A production launch starts from €50,000 when the insight needs to run reliably and feed live decisions. Work outside that ladder runs at roughly €150 per hour. You see the scope and price before we start, and the audit fee is credited toward the next step.
Yes, completely. Crux Digits delivers fixed-scope projects, not staff — there is no body-shop, nearshore pool or rented dedicated team. Senior people stay on your project from the first call to handover, and at the end the client owns the solution: the models, the code, the metric definitions and the documentation. We structure the build so your own team can run and extend it, and we retain no rights to what we make for you. There is no black box to keep renting.
Compliance is built in at the design stage, not retrofitted. Because analytics so often touches customer and personal data, we address data minimisation, lawful basis and EU AI Act risk classification while the model is being scoped, rather than after it is already in production. This is the same EU AI Act and GDPR-first approach we apply across every engagement, so legal and data-protection considerations never become the surprise that freezes a project just before launch.
Usually, yes. The fastest-paying projects across our 13 case studies tended to use data the company already collected — customer records for churn and lifetime-value modelling, sales history for demand forecasting, and process logs for operations analytics and anomaly detection. Messy or scattered data is the normal starting point, not a blocker. The AI Audit and Strategy step gives you an honest read on what your data can support today and what, if anything, needs cleaning up first.
Bring us your messiest forecasting, churn or operations question and we will tell you honestly whether the data can answer it — and what it would take. Start with a free, no-pressure consultation.
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