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What is Data Deduplication?
The process of finding and merging duplicate records in your data – the same customer entered twice, the same invoice recorded in two systems, the same product with three different spellings.
What it is
Data deduplication is the cleanup job of finding records that refer to the same thing and merging them into one clean entry. "Acme Corp," "ACME Corporation," and "Acme Corp." are probably the same customer – but your CRM has them as three separate records, each with their own order history and contact details. Deduplication uses matching techniques – exact matching on email addresses, fuzzy matching on company names, address standardisation – to identify these duplicates and create one "golden record" with the best information from each source. Think of it like sorting through a box of business cards and realising you have five cards for the same person with slight variations.
Why it matters for your business
Duplicates are not just untidy – they distort your business metrics. If your CRM shows 20,000 customers but 4,000 are duplicates, your customer acquisition cost looks 25% better than reality, your retention rate is artificially low, and your marketing team is sending the same person multiple emails. Experian found that the average business database contains 25% duplicate records. A $50M wholesale distributor we worked with deduplicated their supplier database and found they were ordering identical components from the "same" supplier listed under four different names – consolidating those records into one enabled volume pricing that saved $160,000 annually.
How we approach it
We run deduplication in stages. First, exact matching on unique identifiers (email, ABN, phone number) catches the obvious duplicates. Second, fuzzy matching on names, addresses, and company details catches the near-matches. Third, a human review queue handles borderline cases – records that might be duplicates but need a person to confirm. We assign confidence scores to every match, and the merge rules are configurable: which record's address to keep, which phone number takes priority, how to combine order histories. Once clean, we set up ongoing validation rules to prevent new duplicates from entering.
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Key Takeaways
- •The average business database contains 25% duplicate records – distorting customer counts, acquisition costs, and retention rates.
- •Deduplication uses exact matching (email, ABN) and fuzzy matching (names, addresses) to find and merge duplicate records.
- •The real value is not tidiness – it is accurate metrics, consolidated purchasing power, and marketing that does not annoy customers.
- •Deduplication is not a one-time project. You need ongoing validation rules to stop new duplicates from creeping back in.