Operate
Data Science & Analytics
Forecasting, anomaly detection, experimentation and decision analytics that give leadership a quantified basis for operational choices.
About Data Science & Analytics
Data science and analytics services answer specific operational questions with defensible quantitative methods. The questions are concrete: what demand will be, which transactions or readings are anomalous, whether a change produced the effect claimed for it, and which option the evidence favors. The method follows from the question, and each engagement concludes with a recommended action and the quantified case for it.
We apply the same engineering standard to analytical work as to production systems. Each question is stated in writing with the decision it informs, the data is audited before modeling, and results are reported with their uncertainty and the assumptions on which they rest. Where an analysis is to be repeated, we deliver it as a maintained pipeline that the organization's team operates.
What you receive
- Forecasting and anomaly detection models in production
- Experiment design and causal inference reports
- Decision analytics dashboards and pipelines
- Documented methodology with stated assumptions and uncertainty
When to engage
Engage when an operational decision is being made on intuition or on analysis that cannot be reproduced, and the data exists to support a reproducible, quantified analysis.
Data Science & Analytics case studies
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Travel tech
Travel agencies book, get paid and close the month on one platform.
Booking engine, payments, back office and white-label portals for travel agencies, tour operators and corporate travel desks, delivered as one product in eight months.
Read the case study -
Ticketing
Event parking revenue up 3.7x, at 45% of the former operating cost.
We built and now operate point-of-sale software, an automation engine and an AI recommendation assistant for a national event parking reseller.
Read the case study
Common questions about Data Science & Analytics
How do you show that a change caused an effect?
We use a controlled experiment where one is possible, and causal inference methods where it is not. Results are reported with their uncertainty and the assumptions behind them, so leadership can judge how far to rely on them.
Is analysis delivered once or as an ongoing pipeline?
Either. A one-off question receives a documented analysis and a recommended action. A recurring analysis becomes a maintained pipeline, with its methodology documented so your team can run and extend it.
What data is needed to begin?
Only the data the question depends on. It is audited before any modeling begins, and where it cannot support a reliable answer, the audit says so before further work is commissioned.
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Discuss this service on a call.
Describe the objective and the systems it affects, and we will outline how an engagement would proceed.