Data and analytics
Business Intelligence on data you can trust
Business Intelligence is the work of turning a company's data into decisions: metrics, dashboards and reports that answer business questions. The visible part is the dashboard; the part that decides whether it is any use is the data engineering underneath — extracting, transforming and storing the information so that the number on the dashboard is the right one.
People almost always call us about the dashboard, and the dashboard is almost never the problem.
The dashboard is the tip of the iceberg
A beautiful dashboard on dirty data is a machine for making bad decisions confidently. If two departments calculate «sales» differently, if the data arrives two days late, or if nobody knows which system each figure came from, the problem is not fixed by changing tools.
So we start at the bottom: where each piece of data comes from, how it arrives, what happens to it on the way, and where it ends up. The dashboard is the last thing, and by then it is almost trivial.
How the foundation is built
We extract from wherever the data is: APIs, other systems' databases, scraping, files somebody exports by hand every Monday, and systems nobody documented. We move it with orchestrated processes — DAGs, and streams when the business needs the data now and not tomorrow.
And we leave it in a layered store: raw as it arrived, transformed with the business rules, and final ready to query. That separation is what lets you audit a number back to its origin when somebody questions it — and the day somebody questions it always comes.
Data science and machine learning
When the question stops being «what happened» and becomes «what is going to happen» or «who does it happen to», modelling comes in: demand forecasting, segmentation, anomaly detection, classification.
A machine learning model needs exactly what a dashboard needs, with less margin: consistent historical data and a stable definition of what you want to predict. Which is why starting here almost never makes sense — if the foundation is not there, the model learns the noise.
About the tools
We work with Power BI, Tableau or whatever you already have and already know how to use. Changing tools is one of those decisions that is hardest to justify and least often solves anything: the problem is almost always two layers further down.
If you already have licences and trained people, that is an asset, not a limitation.
Questions
What people usually ask us
- What is Business Intelligence?
- It is turning a company's data into decisions: metrics, dashboards and reports that answer business questions. It covers both the visible part — the dashboard — and the data engineering feeding it, which is what determines whether the number is correct.
- What is the difference between data engineering and Business Intelligence?
- Data engineering builds the pipeline: it extracts the information from its sources, transforms it and stores it in a queryable form. Business Intelligence uses that foundation to answer business questions. Without the first, the second is guesswork with a good presentation.
- Do I have to change my dashboard tool?
- Almost never. We work with Power BI, Tableau or whatever you already use. If you already have licences and trained people, that is an asset. When a dashboard is not useful, the problem is usually two layers further down, not in the tool.
- When is a machine learning model worth it?
- When the question stops being «what happened» and becomes «what is going to happen» or «who does it happen to»: demand forecasting, segmentation, anomaly detection. It needs consistent historical data; if the foundation is not sorted, the model learns the noise.
- What is an ETL?
- It is the process of extracting data from its sources (Extract), transforming it according to the business rules (Transform) and loading it into the destination where it will be queried (Load). It is the pipeline that keeps the dashboard fed with correct, current data.
Related topics
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Blockchain and digital assets
Blockchain is useful when the real problem is that nobody trusts the ledger. For almost everything else a database is better — and we say so before charging.
Not sure which one you need?
If you're not sure, that's what the assessment is for: we look at it with you and tell you what's needed — even if it's less than you thought.