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Service 03 / 05 · Data Analytics

We clean, connect, and model your data so the numbers your team looks at every morning are the ones that drive decisions.

Discuss Data AnalyticsAll services
Performance analytics graphs on a laptop screen
  • Data cleaning
  • Business intelligence
  • Dashboards

Many businesses have plenty of data and little clarity. We clean and connect your data, then build reports and dashboards that show what is actually happening.

The aim is simple: the numbers your team looks at each morning are accurate, agreed on and tied to decisions.

Who it is for

  • Teams that rely on spreadsheets and manual reports
  • Businesses with data spread across several tools
  • Managers who want clear KPIs and regular reporting

What you get.

  • 1

    Data cleaning

    Fixing duplicates, gaps and inconsistent formats so the data can be trusted.

  • 2

    Business intelligence

    Combining data sources into one view of how the business is performing.

  • 3

    Dashboards

    Interactive screens that show key figures and let your team explore them.

  • 4

    KPI tracking

    Defining the measures that matter and tracking them over time.

  • 5

    Reporting

    Regular, consistent reports delivered in a format your team will actually use.

  • 6

    Predictive analytics

    Models that use past data to estimate what is likely to happen next, where the data supports it.

Tools we use

  • Python
  • Pandas
  • NumPy
  • PostgreSQL
  • Jupyter
  • Plotly
  • BigQuery
  • scikit-learn

Process

Four steps. No surprises.

  1. Discover

    Understand your business, goals, challenges, and existing systems.

  2. Strategize

    Develop a clear technical and digital strategy.

  3. Build

    Design and develop the required solution.

  4. Optimize

    Measure results, improve performance, and continuously optimize.

Questions about Data Analytics.

Not here? Ask us directly and a person will reply.

Ask a question
  • What tools do you use for data analytics?

    Commonly Python with Pandas and NumPy, SQL and PostgreSQL, Jupyter, Plotly and BigQuery. We pick tools that fit your data and your team.

  • Can you work with data in spreadsheets?

    Yes. Spreadsheets are a common starting point. We can clean them, move the data to a proper database if useful, and build reports on top.

  • How accurate are predictions?

    That depends on the quality and amount of data. We explain what a model can and cannot tell you and do not promise specific outcomes.

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