Data analytics consulting: numbers you can bet the business on
Pipelines, warehouses, and dashboards built with production discipline — by the team behind real-time market data at StockZ and The Whisper Number and the ML projections at DraftEdge.
Data analytics consulting, done properly, starts with a trust problem rather than a data problem: three dashboards that disagree, a warehouse nobody documented, and a Monday meeting that starts with an argument about whose numbers are right. We fix that. Etlon builds data pipelines and warehouses with the same discipline we apply to production software, because that is what they are. Every metric gets one definition, one owner, and one place it is computed. Pipelines are tested, monitored, and alerted, so a silent failure upstream does not quietly poison a quarter of reporting. The dashboards we deliver are built for the people who read them, which usually means fewer charts, clearer definitions, and numbers an executive can repeat in a board meeting without a caveat. If a report does not change a decision, we cut it.
We have done this where the stakes are highest. Etlon built the real-time market data systems behind StockZ and The Whisper Number, delivering live prices, earnings estimates, and whisper numbers to people who act on them within seconds. When data is wrong or late in that business, users lose money, so the engineering has to hold. We brought the same standard to DraftEdge, where our machine learning models generate player projections users compare against real results every week. That is our bar for ML: it goes in when it measurably beats the simple approach, and not before. Plenty of analytics problems are solved with a well-built pipeline and a clear query. When yours genuinely needs models, we build, validate, and monitor them like the production systems they are.
- Data pipelines with tests, monitoring, and alerts, so failures surface in minutes instead of appearing as wrong numbers weeks later
- A documented warehouse on PostgreSQL and AWS, with one agreed definition per metric and a schema your team can read
- Dashboards designed for the people who use them: a focused executive view and deeper operational views underneath it
- Real-time reporting where timing matters, built by the team that delivered live market data for StockZ and The Whisper Number
- Machine learning only where it earns its place, validated against simple baselines and monitored for drift after it ships
- Full handover: documentation, source access, and working sessions with your team, so you are never dependent on us to run it
How we work
Audit and define
We map your sources, find where numbers disagree and why, and write down the metric definitions everyone will use from now on.
Build the pipeline
We construct ingestion, transformation, and warehousing with tests and alerts at every stage, on infrastructure you own.
Ship the dashboards
We deliver reports with the people who will read them in the room, cutting anything that does not inform a decision.
Monitor and extend
We watch data quality and pipeline health, tune what the usage shows, and add real-time feeds or ML where the case is proven.
Work we have shipped
StockZ & The Whisper Number
Real-time market data, earnings estimates, and whisper numbers — where a stale figure costs the user money.
Real-time through peak load Read the case studyDraftEdge
Machine-learning player projections that keep pace with live injury news right up to lineup lock.
ML pipeline in production Read the case studyCommon questions
Maybe not, and we will tell you if so. Most reporting problems are solved by clean pipelines, agreed definitions, and a decent query. ML earns its place when you need prediction, not description: demand forecasting, projections, anomaly detection. When we do build models, as we did for DraftEdge's player projections, we validate them against a simple baseline first. If the model cannot beat the baseline, you do not pay for the model.
That is the normal starting condition, not a disqualifier. We begin with a short audit: what lives where, which numbers conflict, and which decisions are blocked by the confusion. Then we build in order of business value, usually one pipeline and one trustworthy dashboard first, so people see a number they can rely on within weeks.
It depends on the decision the data feeds. We built systems for StockZ and The Whisper Number where seconds mattered, so we know what real-time genuinely costs to run correctly. For most business reporting, hourly or daily is fine and far cheaper. Where speed changes the decision, we build streaming. Everywhere else, we save you the money.
Tired of arguing about whose numbers are right? An engineer replies within one business day.
One business day. From an engineer. No sales sequence.
or email customers@etlon.net