Data Nexus

Decisions

The layer between the data a company has and the decisions it makes. Built to support judgement, not to automate it away — and instrumented so that when it is wrong, you find out.

01/Symptoms

You are probably reading this because:

  • There are dashboards and nobody opens them.


  • Numbers are argued about in meetings because two systems disagree.


  • Problems are noticed by customers before they are noticed internally.


  • Someone exports to Excel before every decision, and that export is the real reporting layer.

02/The work
  1. 01

    One definition per metric

    Revenue, active customer, churn — defined once, computed once, used everywhere. Most reporting disputes are definition disputes wearing a data costume.


  2. 02

    Anomaly and pattern detection

    Detecting the structural change rather than the noise, and surfacing it to a person with enough context to act. An alert nobody can act on is a cost.


  3. 03

    Decision support, not replacement

    Models that narrow the options and show their reasoning. Where a decision is consequential, a person makes it and the system tells them what it thinks and why.


  4. 04

    Reporting built for the operator

    Designed around the decision a specific person makes on a specific rhythm. If the daily view answers a quarterly question, it will not be opened twice.

03/What you get
  • 01

    A metric layer with single, documented definitions

  • 02

    Operational reporting aimed at named decisions and named people

  • 03

    Anomaly detection with routing and thresholds you can tune

  • 04

    A reconciliation between the analytics layer and the accounts

Invariant

A metric with two definitions has none.

04/Questions
Do we need a data warehouse first?
Often not. Companies underestimate how far a well-defined metric layer over the operational database goes, and overestimate how much a warehouse solves on its own.

Will this replace our analyst?
No, and that is deliberate. It removes the reconciliation work analysts spend most of their time on, which is not the same thing.

How large can the data be?
Our own research work resolves transaction graphs of fifteen million rows in under two minutes on ordinary hardware. Scale is rarely the limiting factor; definition is.
Next

We don’t start with solutions. We start with understanding the system.