Descriptive Analytics

Know exactly what happened.

A clear, agreed picture of past performance, so every plan starts from the same facts.

What changes

Three teams, three answers to the same question about last quarter.

One agreed picture of what happened, and where it changed.

Undisputed history

Every KPI defined once and measured the same way everywhere.

Trends you can see

Variance and anomalies surfaced before they become surprises.

A baseline for planning

The foundation every forecast and target is measured against.

What you get

What you walk away with.

KPI definitions

Each metric named, defined, and owned, so the arguments stop.

Historical baselines

Clean, comparable history across periods, units, and segments.

Trend and variance analysis

What moved, by how much, and where the outliers are.

Standard views

The handful of reports and cuts that answer the recurring questions.

How it runs
  1. Step 1

    Define

    Agree the metrics and the questions that matter most.

  2. Step 2

    Clean

    Reconcile sources, fix the gaps, and build a comparable history.

  3. Step 3

    Analyze

    Surface the trends, variances, and anomalies in that history.

  4. Step 4

    Standardize

    Turn the useful cuts into repeatable views the business can rely on.

Is this for you

A good fit if this sounds familiar.

  • Teams that cannot agree on last quarter's numbers
  • Companies standardizing KPIs across departments or regions
  • Leaders who want forecasting but know the history is not clean yet
Questions

What people ask first.

Isn't this just reporting?

Reporting shows the numbers. Descriptive analytics makes sure they are defined consistently, comparable over time, and explained. It is the groundwork that makes reporting, forecasting, and everything after it trustworthy.

How much history do we need?

Enough to see a pattern, which is usually a few years for planning questions and much less for operational ones. We work with what you have and are honest about what it can and cannot support.

What if our historical data is messy?

It almost always is. Cleaning and reconciling it is part of the work, and the result is a baseline you can keep building on.

Next step

Start with the number nobody agrees on.

Tell us the metric with three definitions and we will show you what one agreed history looks like.