Data Engineering and Analytics

A reliable base for reporting and AI.

Pipelines, data-quality checks, dashboards and forecasting, built so the numbers agree and the AI on top has something solid underneath it.

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See the approach

Signal

Agents and assistants are only as good as the data they read. Fix the base first, and every layer above it gets cheaper.

Client problem

Two reports, two different numbers.

Data sits in spreadsheets, ERPs and cloud apps that do not agree.
Reports are rebuilt by hand each month, and errors are found after decisions are made.
Forecasts are requested, but the history needed to support them is incomplete.

Approach

One dependable path from source to decision.

01 / CONNECT

Ingest and check

Automated pipelines with data-quality checks and alerts at the point data arrives.

02 / DEFINE

Agree the measures

Each metric defined once, in one model, so every dashboard and agent uses the same numbers.

03 / USE

Dashboards and forecasts

Reporting built around real decisions, and forecasting only where the data supports it, with its limits stated.

Deliverables

What you get.

Documented pipelines with automated quality checks, owners and runbooks.
A metric dictionary and dashboards your team actually uses.
Forecasts evaluated against a real baseline, or a plain statement that the data is not ready yet.

Technology, grouped by purpose

Chosen for the job.

Engineering

Python, SQL, dbt and PySpark, on Databricks and Delta Lake where analytics and machine learning share tables.

Analytics

Power BI, Tableau or Looker Studio on a governed semantic model; scikit-learn and XGBoost for forecasting.

Data cleaning

Joodei Lens, our in-house multi-agent tool (in beta) for profiling and cleaning tabular data, which exports a pipeline that runs without AI.

FAQs

Questions buyers ask.

Do we need a new data platform?

Not usually. We start from the systems you already run and add only what the decisions need. A lakehouse is recommended only when analytics and machine learning must share the same tables.

Can you forecast with the data we have?

We check first. If the history is too short or inconsistent, we say so and fix the data before building a model.

Strategy call

Make the numbers agree.

A 30-minute conversation, no obligation. We will tell you plainly if this is not the right next step.

Book your strategy call