Where we work
Data analytics for small business uses the same methods in every sector, but each sector asks different questions. These are the four where we can help quickly, because we already understand the decisions people make in them. If yours isn't here, we'd ask more questions first.
Our AI assistant asks about your project and can build you a free plan.
Financial services Where we start
Small firms: independent advisors, lenders, insurance agencies, accounting practices and independent traders. The weekly numbers (client reporting, pipeline, exposure, renewals) usually live in spreadsheets, rebuilt by hand each month by someone who has better things to do.
Typical first projects: a client-reporting pack that builds itself and gives back the two days a month it costs now; one dashboard showing pipeline and renewals together; a reconciliation that stops being manual.
In this sector the data is usually clean and the reporting obligations are real. What hurts is building the same reports again and again against deadlines. The systems are typically a CRM, a custodian or carrier portal that exports on its own schedule, and a practice-management tool that talks to neither.
We start by asking which report has a date attached to it, because automating that one pays for itself right away.
We provide analytics, automation and research tooling. We do not provide investment advice, signals or portfolio management.
It's also why our trading research lives in the Quant Lab as method and education, never as recommendations.
For firms with compliance obligations, we work inside your systems and under your rules, with an NDA in place before any data is shared.
Manufacturing
The numbers that matter are output, quality and downtime. A small forecasting gain pays for itself quickly here, because the cost of being wrong shows up in machine hours and expedited freight.
Typical first projects: demand forecasting for the top product lines, given as a range; a schedule that respects the constraints the floor works under; one screen showing whether this week is on track while there is still time to change it.
The hard part here is that the useful data often sits on the machines and in people’s heads, outside any business system, and the standard times in the ERP stopped matching reality years ago.
Our first question is what the schedule is optimized for today: throughput, on-time delivery, or keeping a particular machine busy. Those goals pull in different directions, and most shops have never had to say which one wins.
Logistics
The decisions are about demand, routing and inventory, and here the cost of being wrong is unusually easy to measure, because a stockout, a half-empty truck and an extra day of inventory each have a price.
Typical first projects: reorder points built from how much your suppliers’ lead times vary; a dashboard that shows what will run out before the next delivery window; allocation across locations when there is not enough to go round.
In logistics the difficulty is lead-time variability. An average lead time hides the two weeks in the year that cause the stockouts, and safety stock set from an average is wrong in both directions at once.
We’d ask first how late your suppliers are when they’re late. We mean the worst case, not the average, because a reorder point should be built from that distribution.
Professional services
Utilization, pipeline and margin per client, usually spread across three tools that were each bought to solve one problem. Often the only way to answer "which clients are profitable?" is for someone to spend a morning in a spreadsheet.
Typical first projects: utilization and write-offs you can see every month without putting them together by hand; a client-profitability view that still works when someone is on vacation; monthly reporting packs that build themselves.
Here the trouble starts with definitions. Two people in the same firm often mean different things by "utilization", so they argue about the number when they should be using it.
We begin with the client you suspect is unprofitable. The suspicion is usually right, and following it shows exactly which costs you can’t see today. That gets to a useful answer faster than defining every metric first.
Not listed
Retail, healthcare, construction and professional practices of other kinds have the same shape of problem, and the methods transfer. We haven't listed them, because a page that claims eleven industries says nothing useful about any of them.
The work itself is described on the solutions pages: dashboards, automations, forecasts, optimization and data plumbing.
Tell us the decision you are trying to make
A few messages are enough. If we're not the right people, we'll say so.
Our AI assistant asks about your project and can build you a free plan.