Your data has answers to questions nobody asked
DecisionBox asks your data the questions nobody thought to ask. Dashboards, BI reports and most AI assistants start from a question someone already has, while DecisionBox explores on its own. It checks what it finds and estimates what each finding is worth.
Analysis today starts with a question
Think about where the numbers in your weekly meeting come from. Someone chose the metrics on the dashboard. Someone raised the request behind each report. When people use an AI assistant, it answers what they think to type, and an AI agent carries out the task someone wrote for it.
All of these are useful, and all of them start with a person who already has the question. Without that question, the numbers that would show a problem go unread.
The limit is scale
Take a database with 200 tables. There are 19,900 possible pairs of tables in it, and about 1.3 million possible groups of three. A costly pattern can sit in any of those combinations, for example sales next to staffing, or bookings next to who was working that day.
No team can search all of that by hand, and adding people doesn't change the arithmetic much. So teams look where they already expect to find something, and the rest goes unexamined.
An example of a question nobody asked
Here's an illustration, with a fictional home-improvement retailer and invented numbers, of the kind of finding we mean.
Customers can book a kitchen design appointment in store, and the bookings dashboard looks healthy. But customers whose appointment falls on a day when no certified designer is working are only about a third as likely to buy as customers who meet one. Over a year, that affects 9,240 appointments.
The cause sits in two places nobody had joined. Only a certified designer can give a customer a price, and the booking system doesn't know which designers are certified. Nobody asked whether bookings land on days when a certified designer is in, because nobody suspected it. Changing the booking rules is estimated to be worth about $22.4 million a year, with no extra staff.
Every part of that finding was already in the company's data. The booking records, the staff schedule and the sales were all there. What was missing was someone asking the question that connects them.
What teams can do about it
You don't need new software to start looking differently. A few habits help any team:
- Start from decisions. List the decisions your data should support this quarter, such as pricing, staffing or stock, and check which of them no report covers.
- Look across sources. Put the systems that touch the same customer or the same order side by side, even when nobody has a hypothesis yet.
- Ask for the evidence. Before a number reaches a decision, ask which query produced it and whether anyone tried to prove it wrong.
- Rank by money. Put a rough value on each finding, so the weekly meeting is about which ones to fix first rather than what to look at.
These habits still depend on people having time to look. That's the part we built DecisionBox to take over.
Where DecisionBox fits
DecisionBox asks its own questions. You connect your data with read-only access, and it learns your business from the data and your documents. Then it explores on its own, the way an analyst would, without anyone on your team writing a query. Each finding is tested again with separate queries, and a second agent tries to prove it wrong, as we showed in how DecisionBox checks its findings. Each recommendation comes with steps, the findings behind it, and an expected impact.
Every finding carries a number, so you can rank findings by what they're worth. DecisionBox sits next to the dashboards you already use, and it doesn't act on its own: it finds and recommends, and your team decides. First findings arrive on day one, and your data isn't copied or moved.
If you want to see what it finds in your own data, talk to us about a free proof of concept.