Most analytics projects do not fail at the dashboard. They fail because nobody agreed what a customer is, or which of the four systems holding revenue is correct, and the dashboard then reports a confident number that two departments both know is wrong. The modelling work that prevents this is unglamorous, comes first, and is what separates a useful analytics engagement from an expensive screenshot.
ScienceSoft’s own stated range. Honest, and useless until you know what moves you along it.
Not the dashboard. Two departments disagreeing on what revenue means.
Metrics defined once, in code, versioned. Every answer inherits them.
Layer it over governed data, never instead of it. A fluent answer is not a correct one.
How much do data analytics services cost?
Google lists this as the second question on this search, and one competitor answers it publicly, so we will point at that rather than pretend the range is secret. ScienceSoft, which ranks on page one for this term, publishes $10,000 to $1,000,000+ depending on service type and complexity. That span is honest and also useless on its own, so here is what moves you along it.
| Driver | Cheaper end | Expensive end |
|---|---|---|
| Sources | One or two systems with clean APIs | Many systems, some legacy, some only exporting files overnight |
| Definitions | The business already agrees what the metrics mean | Every department has its own definition of revenue and churn |
| History | Report from today forward | Backfill and reconcile years of inconsistent history |
| Latency | Daily refresh | Streaming, with correctness guarantees under failure |
| Governance | Internal reporting only | Regulated data, lineage, retention and access audits |
| Who runs it after | Your team owns the models | Nobody owns them, so it silently rots |
Can ChatGPT do data analysis?
On a clean, small dataset that fits in a prompt, genuinely yes, and it is very good at the exploratory pass. That is not what a data analytics engagement is for.
The difficulty in real organisations is upstream of analysis: the data is spread across systems, the join keys disagree, the same field means different things in two places, and the history contains a migration that changed the meaning of a column three years ago. A language model given that mess will produce a fluent answer, and there is no marker on the output distinguishing a correct one from a confident one. The value of a modelled warehouse is precisely that the definitions are settled once, visibly, and every answer inherits them.
Where it genuinely helps: drafting transformations, explaining unfamiliar schemas, and letting non-technical users ask questions of a warehouse that has already been modelled properly. Layer it on top of governed data, not instead of it.
What we build
- Data platform. Ingestion, a warehouse or lakehouse, transformation with tests, and orchestration that alerts when a load fails rather than serving stale numbers silently.
- Semantic layer. Metric definitions written once, in code, versioned. This is the artefact that stops two dashboards disagreeing.
- Reporting. Dashboards people actually open, which usually means fewer of them, answering questions someone named actually asks.
- Quality and observability. Freshness, volume and schema-drift checks, so you find out before the board meeting rather than during it.
- Governance. Access by role, lineage from source to number, retention rules, and deletion that reaches every copy.
What to ask any data analytics vendor
- Who decides what a metric means? If the answer is the vendor, you will get numbers nobody in the business trusts.
- Show me a lineage view. From a number on a dashboard back to the source column. If they cannot, nobody can debug a wrong figure later.
- What happens when a source changes shape? Silent breakage is the norm without explicit schema checks.
- Will my team be able to add a metric without you? Ask for the actual pull request that adding one would require.
- What did you refuse to build? Every honest analytics engagement includes talking someone out of a real-time dashboard they do not need.
Data analytics with Sthenos
We build data platforms and reporting for organisations across Maryland and the Washington DC region, delivered through an established engineering partnership with NeoSOFT. We start with definitions and lineage rather than with a dashboard, because the dashboard is the cheap part and the disagreement about what the numbers mean is the expensive one. To scope a platform or fix reporting nobody currently trusts, talk to our engineers. Related: our big data and analytics practice.