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Data Analytics

Answers to the questions the business is actually asking

Data only helps when someone turns it into an answer. We work with your teams to frame the questions that matter, analyze the data you already have, and present the findings clearly enough to act on, from a one-off deep dive to analysis that runs every week.

What's included
In this engagement06
  • Question framing
  • Exploratory analysis
  • Customer and product analytics
  • Operational analytics
  • Experiments and testing
  • Clear reporting

Why it matters

Good analysis starts with a sharp question and ends with a decision. Everything in between should be traceable, repeatable and honest about its limits.

What this includes

Six parts of the work. Which of them you need depends on where you are starting from, and we agree that before anything is built.

  1. 01

    Question framing

    Turning a broad request into a specific question the data can actually answer.

  2. 02

    Exploratory analysis

    Finding patterns, outliers and drivers in your data, and checking them before they become conclusions.

  3. 03

    Customer and product analytics

    Understanding behavior, retention and segments, and what drives each of them.

  4. 04

    Operational analytics

    Analysis of processes, costs and performance that shows where time and money actually go.

  5. 05

    Experiments and testing

    A/B tests and experiments designed and read correctly, so noise is not mistaken for a result.

  6. 06

    Clear reporting

    Findings in plain language, with the charts and caveats a decision needs.

  7. Not sure which of these you need?

    Tell us where things stand for you today and we will suggest where to start.

How we deliver it

Four stages, agreed with you before the work starts, with a working result at the end of each rather than one reveal at the end.

  1. Frame

    We agree the question, the decision it supports and how the answer will be used.

  2. Gather and check

    We collect and validate the data, and note any gaps that limit the answer.

  3. Analyze

    We explore, test and model, reviewing interim findings with you as we go.

  4. Present

    We share the findings, the method and the limits, and hand over work you can rerun.

The stack

Technologies we work with

The tools we reach for most often in this work. We fit in with what you already run rather than asking you to change it.

11Tools across
3 groups
  • 01 / 03

    Analysis

    4
    • Python
    • pandas
    • SQL
    • Jupyter
  • 02 / 03

    Visualization

    4
    • Power BI
    • Amazon QuickSight
    • Tableau
    • matplotlib
  • 03 / 03

    Data

    3
    • Snowflake
    • PostgreSQL
    • Excel

What you receive

Yours to keep when the engagement ends, whether we carry on working together or not.

  • An analysis report

    The findings, the method and what they mean for the decision.

  • Reusable notebooks and queries

    The analysis in code, so it can be rerun and checked.

  • Clear visuals

    Charts designed to be read quickly and shared.

  • A findings session

    A walkthrough with your team, with time for questions.

Common questions

The ones that come up in almost every first conversation about this work.

  • Dashboards track known measures over time. Analytics answers specific questions, often new ones, and may lead to a dashboard once a question becomes a routine one.

  • Usually, yes. We check quality early, work around gaps where possible, and are clear about how they affect the answer.

  • Yes. The work is delivered as documented code and queries, so it can be rerun on new data.

Let's talk about Data Analytics

A short call is usually enough to work out whether we can help and where it makes sense to start. No preparation needed.

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