Data & AI

Data Analysis & Processing

We turn scattered, messy data into clean datasets, automated pipelines and reports your team can trust. Practical data engineering, from spreadsheets and CSV exports to warehouses and dashboards.

Discuss your project How we work
Analyst working on a laptop at a desk in a bright office
pipelines/monthly_revenue.py
orders = (
    pd.read_csv("exports/orders.csv", parse_dates=["created_at"])
      .drop_duplicates(subset="order_id")
      .query("status != 'test'")
)
monthly = orders.resample("MS", on="created_at").amount.sum()
monthly.to_sql("monthly_revenue", engine, if_exists="replace")

01 · Overview

From data chaos to decisions you can defend

Useful analysis depends on unglamorous foundations: consistent formats, removed duplicates, documented definitions and pipelines that run on schedule. We build those foundations first, so the numbers in your reports mean the same thing every time.

We work in Python with pandas and SQL, schedule pipelines with tools such as Airflow or simpler scheduled jobs when that is enough, and deliver results into databases, spreadsheets or dashboard tools such as Metabase. We scale up to warehouses when data volumes justify it.

Focus areas

  • ETL and ELT pipelines
  • Data cleaning and validation
  • Automated reporting
  • Interactive dashboards

Technologies

  • Python and pandas
  • SQL and PostgreSQL
  • Apache Airflow
  • BigQuery and Snowflake
  • Metabase and Looker
  • Jupyter

02 · Capabilities

What we deliver

The full path from raw exports to reliable reporting.

Data processing pipelines

Scheduled extraction from databases, APIs, files and SaaS tools into a consistent, queryable store.

Data cleaning and validation

Deduplication, type fixes, missing-value handling and validation rules that catch bad data early.

Transformation

Business logic expressed as tested, documented transformations and reusable data models.

Automation

Manual spreadsheet work replaced with repeatable jobs that run on schedule and alert on failure.

Reporting and dashboards

Reports and dashboards built on agreed metric definitions, with access for the people who need them.

Analysis workflows

Reproducible notebooks and documented methods for one-off questions and deeper analysis.

03 · Approach

An iterative path to trusted data

We deliver a working data flow early, then extend coverage and quality with each iteration.

  1. 01

    Discovery and ingestion

    We map sources and questions, then connect the first data flow.

    • Source mapping
    • Sample data review
    • Schema design
    • Connector setup
  2. 02

    Transform and model

    Cleaning and business logic implemented as tested transformations.

    • Cleaning scripts
    • Transformation models
    • Incremental loads
    • Quality checks
  3. 03

    Analyse and visualise

    Views, reports and dashboards built on agreed definitions.

    • SQL views
    • Dashboard design
    • Self-serve access
    • Alert rules
  4. 04

    Deploy and govern

    Scheduled, monitored pipelines with clear ownership and documentation.

    • Scheduled pipelines
    • Access control
    • Cost monitoring
    • Documentation

04 · Technical considerations

The details that decide success

Trustworthy data comes from deliberate design choices, not tools alone.

One definition per metric
Revenue, active users and other key measures are defined once and reused, so reports never disagree.
Quality gates
Schema checks, null and duplicate detection and range rules stop bad data before it reaches a report.
Incremental processing
Only new or changed records are processed where possible, keeping pipelines fast and costs low.
Privacy and access
Personal data is minimised, masked where appropriate and access-controlled, with data protection laws such as Nigeria’s NDPA and the GDPR in mind.
Reproducibility
Pipelines and analyses are version-controlled and repeatable, so any number can be traced back to its source.
Right-sized tooling
Not every problem needs a data warehouse. We match the tooling to your data volume and team.

05 · Outcomes

What you can expect

  • Trusted numbers with documented definitions
  • Hours of manual work automated
  • Reports that update themselves
  • A data setup that grows with you

06 · FAQ

Data Analysis & Processing: common questions

Ask us something else
What is the difference between ETL and ELT?

ETL transforms data before loading it into its destination, which suits smaller datasets and strict compliance needs. ELT loads raw data first and transforms it inside the warehouse, which scales well and keeps raw history. We choose based on volume, latency and governance requirements.

How long does it take to build a data pipeline?

A simple scheduled pipeline often takes one to two weeks. Pipelines with many sources, streaming requirements or a full warehouse model usually take several weeks. We aim to deliver a working first flow early.

Can you handle real-time data?

Yes, when the use case needs it. Many reporting needs are well served by hourly or daily batches, which are simpler and cheaper. For live dashboards and alerts we can use streaming tools such as Kafka.

Do you build predictive models?

We can build and evaluate forecasting and classification models on clean data. Our Machine Learning & AI Prototyping service covers this in more depth.

How do you ensure data quality?

Automated validation at each stage: schema checks, null and duplicate detection, range and consistency rules, with alerts when something fails and clear ownership for fixing it.

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Backend Development

Application architecture, business logic, background jobs and integrations that hold up under load.

Put your data to work

Show us the data you have and the questions you want answered. We will propose a practical first pipeline.

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