You want to look ahead
Forecasts for revenue, cashflow, stock, capacity, churn or demand matter more than historical figures alone.
Analytics & AI
Analytics & AI starts with a sharp business question
Datalab helps organisations move beyond looking back. We build analyses, models and AI applications that predict, explain, advise or take work off people's hands. Always with attention to the data foundation, definitions, security, adoption and explainability.
When analytics & AI become useful
Dashboards & BI show what is happening and where you can drill down. Analytics & AI become interesting when you want to predict, prioritise, explain, recommend or make knowledge from text and processes available.
You are looking for causes
A dashboard shows deviations, but the team wants to know which factors explain them.
Decisions need prioritising
Not every customer, order, case or deviation deserves the same attention. Models can help rank them.
Knowledge is scattered through text
Documents, emails, tickets, notes or manuals contain information that should be easier to find or use.
Work can be partly automated
Think of classifying, summarising, matching, signalling or drafting responses with human review.
AI must remain responsible
You want to experiment, but with control over data, roles, logging, privacy and explainability.
Basics
What are analytics & AI?
Analytics & AI is the step from insight to prediction, explanation, recommendation or automation. Where dashboards mainly show what is happening, analytics and AI help answer follow-up questions: what is likely to happen, why is this happening, which action has priority and what knowledge can we extract from text or data?
In practice, this often starts with BI. First we make sure definitions, dashboards and data quality are sound. Then it becomes visible which questions can no longer be answered with reporting alone. That is usually the moment to grow into forecasting, machine learning, LLM applications or other forms of advanced analytics.
Further reading
Data model
Analytics & AI with and without a data model
Not every analysis immediately needs an extensive data model. For an experiment, prototype or standalone dataset, a compact approach may be enough. That helps you quickly learn whether a question is technically and commercially worth pursuing.
A data model becomes important once analyses need to be repeatable, several teams use the same concepts or models are fed with features that must remain reliable. You do not want revenue, customer status, product group or margin to be derived differently in every experiment.
The same applies to AI: good prompts, models and workflows are only as strong as the context they receive. A clear data model makes that context more consistent, more explainable and easier to manage.
- Without a data model: suitable for exploration, prototypes and scoped analyses.
- With a data model: needed for repeatable features, consistent definitions, monitoring and management.
- The route can be step by step: first learn with a small use case, then structure what has value.
Further reading
Technology
Technique deep dive: MCPs for LLMs
LLMs only become truly useful when they can work safely with the right context. A chatbot that only uses general language model behaviour knows little about your systems, definitions, documents or processes. MCP, the Model Context Protocol, is a way to give LLMs controlled access to sources and actions.
With MCPs, you can let an AI assistant interact with documentation, databases, ticketing, CRM, files, reports or internal tools, for example. The model does not get unlimited access; it uses predefined connectors with clear boundaries, logging and permissions.
That makes MCP especially interesting for organisations that want to use LLMs without pulling knowledge and data away from existing systems. Think of an internal knowledge assistant, support assistant, analysis assistant, reporting helper or a workflow that retrieves and summarises information for human review.
- MCP connects an LLM to sources and tools through agreed interfaces.
- Permissions, logging and scope remain explicit parts of the setup.
- The value is not in the chatbot itself, but in reliable context and well-chosen actions.
Further reading
Value
What changes for your organisation?
Analytics & AI make data more active. Instead of only looking back, your organisation gains tools to predict, prioritise, explain and accelerate. The greatest value arises when models connect to existing decision moments and employees understand when they should or should not trust the outcome.
Experience with organisations that want to use data further
Datalab works for retail, accountancy, public organisations, healthcare, logistics and knowledge-intensive teams.
They also trust Datalab
Approach
From use case to reliable analytics & AI
We start with the question and the decision, not with the model. Which prediction, recommendation or assistance would really create value? Which data is available? Who uses the outcome? And what happens if the model is uncertain or wrong?
Then we build iteratively. First a compact analysis or prototype, then evaluation with users, and only then production setup with a data model, monitoring, documentation, permissions and adoption. This keeps AI practical and manageable.
Independent and responsible
We choose the technology that fits the question: statistics, machine learning, LLMs, simple rules or simply better BI. Not every problem needs AI.
1.
Clarify use case and value
We determine which decision, workflow or user question the analysis or AI application should improve.
2.
Assess data and context
We examine quality, availability, definitions, privacy, bias and the role of a data model or data warehouse.
3.
Build and evaluate a prototype
We test with real examples, measure performance and jointly decide whether further development makes sense.
4.
Production, monitoring and adoption
We set up management, logging, explanation, permissions and training so the solution can be used safely.
Partner
CFO and controller expertise when models affect management information
In samenwerking met
Analytics & AI often touch forecasting, cashflow, margins, budgets, customer value or risks. Technical model quality is then not enough: the business logic must be sound and the outcome must fit the way management steers.
When financial or control expertise is needed, we can work with BPO - De Administratie. Datalab sets up the data, analyses and AI applications; BPO helps as a part-time CFO/controller with KPI choices, interpretation, budget control and the management rhythm around the insights.
Frequently asked questions
Questions about analytics & AI
When is analytics & AI more useful than dashboards & BI?
Dashboards & BI are usually the right step when you need current management information, fixed KPIs and drill-down options. Analytics & AI become useful when you want to predict, explain, prioritise, recommend or use text and knowledge sources in processes. Often the route starts with dashboards & BI and then grows from there.
Do we already need a data warehouse for AI?
Not always. For a prototype or standalone analysis, a limited dataset can be enough. For repeatable models, monitoring, history, data quality and multiple sources, a data warehouse is often sensible.
What is the difference between machine learning and an LLM?
Machine learning learns patterns from structured or historical data, for example for prediction, classification or scoring. An LLM is a language model that can understand, summarise and generate text, and answer questions. Sometimes you combine both, but they do not solve the same problem.
When is deep learning needed?
Deep learning is especially relevant for complex patterns in large amounts of data, such as language, images, sound, time series or unstructured data. For many business problems, simpler statistics, machine learning or clear rules are more explainable and cheaper to manage.
What is an MCP for LLMs?
MCP stands for Model Context Protocol. It is a way to give an LLM access to sources and tools through controlled connectors, such as documentation, databases or internal systems. This gives an AI assistant context without unlimited access to everything.
Can you build a chatbot on our own data?
Yes, provided the use case and information sources are suitable. We look at document quality, authorisations, logging, evaluation, privacy and whether a chatbot is really the best form. Sometimes a smart search function, dashboard or workflow is better. For complete workflow projects, Process Automation fits; for temporary Windmill or automation expertise, our AI Process Automation & Windmill Consultant fits.
How do we prevent AI from giving wrong answers?
You cannot prevent this completely, but you can strongly reduce the risks with good context, scoping, evaluation sets, source references, human review, logging and clear boundaries. Sensitive applications also require governance and ethical assessment.
Can you help with forecasting?
Yes. We help with demand forecasting, cashflow, stock, capacity, churn or revenue expectations, for example. We look not only at model quality, but also at data quality, interpretation, uncertainty and how the forecast is used in decision-making. For retail applications such as demand, stock, margin and consumer trends, Retail Studio often fits directly.
Do we need a data model for analytics & AI?
For a first experiment, not always. As soon as analyses or models recur, several teams work with them or features need to remain stable, a data model becomes important. Also read about DBT & data modelling.
Are you tied to an AI platform or vendor?
No. We advise independently. Depending on the situation, solutions can connect to existing cloud environments, open-source components, your own infrastructure or managed AI services. The choice depends on security, cost, management, performance and portability.
Next step
Discuss your analytics or AI challenge with a specialist
In a short conversation, we map out your data, models and goals and outline a feasible first step.
Schedule a call