Advanced Data Analytics

Reliable data pipelines and reporting your team can trust

Pipelines, warehouses and reports that replace manual spreadsheet work with numbers that are checked automatically before anyone relies on them.

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Indicative starting price: $24,999 (USD). Final price depends on scope.

How data moves from your sources to reportsData from spreadsheets, databases and other systems is ingested on a schedule. A validation gate checks row counts, types and ranges, duplicates and totals. If a check fails, the load is held and an alert is sent, and the last good data stays in place. Data that passes is stored in a SQL warehouse as raw, clean and metrics layers, and feeds reports, dashboards and forecasts. Runs are scheduled and monitored. Your data Spreadsheets Databases Other systems and APIs Ingest scheduled runs Validation gate Checked before publishing Row counts Types and ranges Duplicates Totals match Held and alerted last good data stays if a check fails passes SQL warehouse Raw exactly as received Clean typed, de-duplicated Metrics one definition each What your team uses Reports on a schedule Dashboards live views Forecasts when data allows Runs are scheduled and monitored; failures raise an alert to a named person How data moves from your sources to reportsData from spreadsheets, databases and other systems is ingested on a schedule. A validation gate checks row counts, types and ranges, duplicates and totals. If a check fails, the load is held and an alert is sent, and the last good data stays in place. Data that passes is stored in a SQL warehouse as raw, clean and metrics layers, and feeds reports, dashboards and forecasts. Runs are scheduled and monitored. Your data Spreadsheets Databases Other systems and APIs Ingest scheduled runs Validation gate Checked before publishing Row counts Types and ranges Duplicates Totals match passes if a check fails Held and alerted last good data stays SQL warehouse Raw exactly as received Clean typed, de-duplicated Metrics one definition each What your team uses Reports on a schedule Dashboards live views Forecasts when data allows Runs are scheduled and monitored
Data moves left to right. Nothing reaches the metrics layer without passing the validation gate, and a failed check stops the load instead of publishing wrong numbers.
Technical notes
  • Core tools are Python and SQL; Spark is used only where data volume requires it.
  • Each run is logged with its input row counts so a number can be traced back to its source.
  • Metric definitions live in one place in the metrics layer; dashboards read from it and do not recalculate.
  • Checks, schedules and alert recipients are configuration, so your team can change them without a rebuild.

How we check the numbers are right

Every pipeline gets automatic checks before data is published. These are the standard ones; we add rules specific to your data.

Standard validation checks and what happens on failure.
CheckWhat it catchesOn failure
Row countsA file or extract that arrives truncated or empty.Load is held; alert sent.
Types and rangesText in a number column, impossible dates, negative quantities.Offending rows quarantined for review.
DuplicatesThe same record loaded twice, for example a re-sent file.Duplicates removed by a documented rule, and counted.
ReconciliationTotals that no longer match a trusted figure from the source system.Load is held; difference reported.
FreshnessA source that stopped updating.Alert sent before anyone reads a stale report.

One definition per metric

Different teams quoting different numbers is usually a definition problem, not a data problem. Each metric is written down once, with an owner, and reports read from it.

Illustrative example

Net revenue

Grain
One row per order line
Sources
Orders, refunds
Rule
Order value minus refunds, excluding cancelled orders
Owner
Finance
Refresh
Daily, after validation passes

Problems this solves

  • Reporting depends on someone copying and reshaping Excel files each week.
  • Different teams quote different numbers for the same metric.
  • Data arrives late or broken and nobody notices until a report is wrong.
  • You want forecasting but are not sure your data is ready for it.

What you receive

  • ETL / ELT pipelines (Python, SQL, Spark where volume requires it)
  • A structured data store with agreed metric definitions
  • Automated data-quality checks and failure alerts
  • Reports and dashboards for the people who use them
  • Forecasting or predictive models where the data supports them

How the work runs

Each phase has an input from you and a written output from us. Ongoing pipeline monitoring is agreed separately.

PhaseWhat happensYou provideYou receive
Step 1Discover sources and metrics List the sources, the reports that matter and how each metric is calculated today. Sample files or read access to source systems; the people who own each metric. A source inventory and a metric glossary draft.
Step 2Model and design Design the raw, clean and metrics layers and the validation checks for each source. Sign-off on metric definitions. A data model and a validation plan.
Step 3Build the pipelines Implement scheduled ingestion, transformations, checks and failure alerts. Test access and an agreed schedule. Working pipelines running on your data.
Step 4Validate and reconcile Reconcile outputs against known totals and previous reports; fix differences. Known-good reports to compare with. A reconciliation note listing every difference found.
Step 5Release and hand over Switch reporting to the new pipeline and document how to run and change it. Named owners. Documentation and a handover session.

Related work

Anonymised client projects. Client names are withheld because of confidentiality; the project page explains each one.

Client project, anonymisedRetail

Sales Forecasting Dashboard

A multi-panel dashboard on three years of retail data: monthly revenue trends, product-category performance, regional heatmaps and a six-month machine-learning forecast with confidence intervals.

Client project, anonymisedHealthcare

Healthcare Data Analytics Platform

An analytics platform that processes patient data to surface treatment patterns for review.

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From the blog

Practical articles from the engineers on this topic.

Mar 18, 2026

Data Analytics Revolution: From Reactive to Predictive

Forecasting does not need a complicated model to be useful. A practical walk through setting a baseline, testing a forecast honestly, and telling decision-makers how uncertain it is.

FAQ

Frequently asked questions

Can you automate our weekly Excel reports?

Yes. We replace manual copy-and-paste with a scheduled pipeline that loads the files, checks them and produces the same report automatically. Most projects start with one report.

Can you build a sales forecasting dashboard?

Yes. We have built forecasting dashboards using Prophet and ARIMA, and we compare against a simple baseline so you can see whether the model adds value.

We only have Excel files. Is that enough to start?

Yes, it is a common starting point. The first step is usually to make those files flow into one place reliably, then add reporting on top.

How do you check that the numbers are right?

Each pipeline gets validation checks (row counts, ranges, duplicates, reconciliation to a known total) that run automatically and raise an alert when they fail.

How is the work priced?

The 'starting at' figure is indicative. After a short discovery conversation we share a written scope and an estimate based on what the project actually involves.

Who owns the code and the data?

This is agreed in writing before work starts. Our default is that you own what we build for you, and that your data stays in systems you control.

Replace weekly spreadsheet rework with a pipeline?

Tell us which reports take the most effort and where the data comes from. We will outline how we would approach it.

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