Data Preprocessing

Raw data is rarely trainable as collected. We clean, normalise and deduplicate it, and tell you honestly what had to be discarded.

What is included

Data Preprocessing, in detail

Cleaning and normalisation

Encoding, formatting, units and date handling made consistent across the whole set.

Deduplication

Exact and near-duplicate detection, which matters more than most teams expect.

PII handling

Personal data detected and redacted or pseudonymised according to your policy.

Splits and stratification

Train, validation and test splits built to preserve distribution.

Data documentation

A datasheet describing what is in the set, what was removed, and why.

How we work

Four steps, no surprises

  1. ConsultationWe learn the business and what success looks like.
  2. Audit & scopeA written plan: what we build, in what order, at what cost.
  3. BuildDelivered in stages you review as we go.
  4. SupportMonitoring and iteration once it is live.

Questions

Frequently asked

How much data typically gets discarded?

It varies enormously by source. Scraped data often loses a large share; curated data much less. We report the number rather than quietly dropping rows.

Can you work with our existing pipeline?

Yes — we can deliver as files or run the steps inside your own tooling.

Let us look at what you are trying to build

Tell us the problem and we will tell you honestly whether we are the right people to solve it — and what it would take.

Book appointment