<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Match Data Studio Blog</title><description>Guides, tutorials, and insights on data matching, entity resolution, and CSV processing.</description><link>https://match-data.studio/</link><language>en-us</language><item><title>From AI scraping to AI matching — building the data pipeline for competitive analysis</title><link>https://match-data.studio/blog/ai-scraping-to-matching-data-pipeline/</link><guid isPermaLink="true">https://match-data.studio/blog/ai-scraping-to-matching-data-pipeline/</guid><description>AI scraping collects cleaner data than rule-based crawlers. AI matching processes it beyond what string comparisons allow. Here is how the full stack works.</description><pubDate>Sun, 08 Mar 2026 00:00:00 GMT</pubDate><category>web-scraping</category><category>ai</category><category>competitive-intelligence</category><category>product-matching</category><category>multimodal</category><category>e-commerce</category><category>real-estate</category></item><item><title>AI talent matching as a service: the infrastructure gap holding consultants back</title><link>https://match-data.studio/blog/ai-talent-matching-service-infrastructure/</link><guid isPermaLink="true">https://match-data.studio/blog/ai-talent-matching-service-infrastructure/</guid><description>Talent matching consultants spend more time building pipelines than matching candidates. Configurable matching infrastructure changes the math.</description><pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate><category>hr-recruiting</category><category>ai</category><category>matching-algorithms</category><category>entity-resolution</category></item><item><title>Building a candidate-to-job matching workflow that actually scales</title><link>https://match-data.studio/blog/candidate-job-matching-workflow-at-scale/</link><guid isPermaLink="true">https://match-data.studio/blog/candidate-job-matching-workflow-at-scale/</guid><description>Matching resumes to job descriptions requires more than keyword overlap. Here&apos;s how to build a multi-signal matching workflow that handles thousands of candidates and hundreds of roles.</description><pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate><category>hr-recruiting</category><category>ai</category><category>matching-algorithms</category><category>best-practices</category></item><item><title>Scaling your AI recruiting practice beyond spreadsheets and scripts</title><link>https://match-data.studio/blog/scaling-ai-recruiting-practice-beyond-spreadsheets/</link><guid isPermaLink="true">https://match-data.studio/blog/scaling-ai-recruiting-practice-beyond-spreadsheets/</guid><description>Most AI recruiting consultants match candidates with a mix of spreadsheets, Python scripts, and API calls. Here&apos;s how to move from fragile one-off workflows to repeatable matching operations.</description><pubDate>Sat, 07 Mar 2026 00:00:00 GMT</pubDate><category>hr-recruiting</category><category>ai</category><category>getting-started</category><category>best-practices</category></item><item><title>AI extraction vs AI enrichment: how structured data gets pulled from files</title><link>https://match-data.studio/blog/ai-extraction-vs-enrichment-how-it-works/</link><guid isPermaLink="true">https://match-data.studio/blog/ai-extraction-vs-enrichment-how-it-works/</guid><description>Extraction produces one column from a file. Enrichment produces many. Understanding the difference — and when to use each — determines whether your matching pipeline gets the right signals.</description><pubDate>Fri, 06 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>deep-dive</category><category>file-matching</category><category>configuration</category></item><item><title>Extracting structured data from home inspection reports and insurance documents</title><link>https://match-data.studio/blog/home-inspection-insurance-document-extraction/</link><guid isPermaLink="true">https://match-data.studio/blog/home-inspection-insurance-document-extraction/</guid><description>Inspection reports and insurance documents are PDFs full of structured data — room-by-room condition ratings, damage photos, repair estimates, coverage details. AI extraction turns them into matchable records.</description><pubDate>Fri, 06 Mar 2026 00:00:00 GMT</pubDate><category>pdf-extraction</category><category>real-estate</category><category>ai</category><category>property-management</category></item><item><title>Matching real estate listings with photos: how AI reads property images across platforms</title><link>https://match-data.studio/blog/real-estate-listing-photo-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-listing-photo-matching/</guid><description>Addresses differ, MLS numbers don&apos;t transfer, and square footage disagrees. Listing photos show the same kitchen in both datasets. AI extraction turns property images into matchable attributes.</description><pubDate>Fri, 06 Mar 2026 00:00:00 GMT</pubDate><category>real-estate</category><category>image-matching</category><category>ai</category><category>proptech</category><category>mls</category></item><item><title>From string comparisons to contextual reasoning: how AI transformed data matching</title><link>https://match-data.studio/blog/ai-data-matching-from-rules-to-llms/</link><guid isPermaLink="true">https://match-data.studio/blog/ai-data-matching-from-rules-to-llms/</guid><description>Data matching evolved from rigid rules to machine learning to neural embeddings to LLMs. Each generation solved problems the previous one couldn&apos;t. Here&apos;s how the technology progressed, what each approach actually does, and why modern systems layer all of them.</description><pubDate>Tue, 03 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>matching-algorithms</category><category>entity-resolution</category><category>data-quality</category></item><item><title>Best data matching tools in 2026: a feature-by-feature comparison</title><link>https://match-data.studio/blog/best-data-matching-tools-compared/</link><guid isPermaLink="true">https://match-data.studio/blog/best-data-matching-tools-compared/</guid><description>An in-depth comparison of 12 data matching tools — from AI-powered platforms to open-source libraries — covering features, matching approaches, deployment models, and what actually matters when choosing one.</description><pubDate>Tue, 03 Mar 2026 00:00:00 GMT</pubDate><category>data-matching</category><category>comparison</category><category>tools</category><category>entity-resolution</category><category>fuzzy-matching</category></item><item><title>Can ChatGPT do fuzzy matching? Yes — but here&apos;s where it breaks down</title><link>https://match-data.studio/blog/can-chatgpt-do-fuzzy-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/can-chatgpt-do-fuzzy-matching/</guid><description>ChatGPT, Gemini, Claude, and other LLMs can absolutely do fuzzy matching. They&apos;re just not built for it. Here&apos;s what works, what doesn&apos;t, and when you need a dedicated matching tool.</description><pubDate>Mon, 02 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>fuzzy-matching</category><category>data-quality</category><category>best-practices</category></item><item><title>Deterministic vs probabilistic matching: what they are, when to use each, and why the best systems use both</title><link>https://match-data.studio/blog/deterministic-vs-probabilistic-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/deterministic-vs-probabilistic-matching/</guid><description>Deterministic matching compares exact values. Probabilistic matching uses statistics, embeddings, and LLMs to find likely matches. Here&apos;s how each works, where each fails, and how combining them produces faster, cheaper, more accurate results.</description><pubDate>Mon, 02 Mar 2026 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>ai</category><category>data-quality</category><category>best-practices</category></item><item><title>Can Gemini read PDFs? Can ChatGPT understand documents? Yes — here&apos;s how AI classifies PDFs</title><link>https://match-data.studio/blog/pdf-document-classification-with-ai/</link><guid isPermaLink="true">https://match-data.studio/blog/pdf-document-classification-with-ai/</guid><description>LLMs like Gemini, ChatGPT, and Claude can read PDFs, understand tables, extract text from images, and interpret graphs. Here&apos;s how multimodal AI enables granular PDF document classification — and where it still needs help.</description><pubDate>Mon, 02 Mar 2026 00:00:00 GMT</pubDate><category>ai</category><category>pdf</category><category>document-classification</category><category>data-quality</category><category>multimodal</category></item><item><title>Why SQL and pandas can&apos;t accurately match retail products — and what can</title><link>https://match-data.studio/blog/sql-pandas-limitations-retail-product-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/sql-pandas-limitations-retail-product-matching/</guid><description>SQL JOINs and pandas merges fail on color variants, promotional naming, translated descriptions, and spec formatting differences. AI embeddings and LLMs understand that &apos;Midnight&apos; means black and &apos;Violet&apos; means purple. Here&apos;s why traditional tools hit a ceiling and how hybrid pipelines break through it.</description><pubDate>Mon, 02 Mar 2026 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>ai</category><category>e-commerce</category><category>data-quality</category></item><item><title>Extracting matchable attributes from product images: beyond basic categorization</title><link>https://match-data.studio/blog/attribute-extraction-product-images/</link><guid isPermaLink="true">https://match-data.studio/blog/attribute-extraction-product-images/</guid><description>Product images contain brand names, model numbers, colors, and condition details that aren&apos;t in your spreadsheet. AI attribute extraction turns visual information into structured fields ready for matching.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>image-matching</category><category>product-matching</category><category>ai</category><category>e-commerce</category></item><item><title>The CPG translation tax: why demand planning breaks at the data seams</title><link>https://match-data.studio/blog/cpg-translation-tax-demand-planning/</link><guid isPermaLink="true">https://match-data.studio/blog/cpg-translation-tax-demand-planning/</guid><description>Most CPG forecasting failures aren&apos;t analytics problems — they&apos;re harmonization problems. Internal SKUs, syndicated codes, and retailer GTINs speak different languages, and the cost of translating between them is quietly destroying forecast accuracy.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>supply-chain</category><category>master-data</category><category>entity-resolution</category><category>product-matching</category></item><item><title>Image categorization at scale: from folders of photos to structured data</title><link>https://match-data.studio/blog/image-categorization-ai-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/image-categorization-ai-matching/</guid><description>Thousands of images sitting in folders with meaningless filenames. AI image categorization extracts structured labels, categories, and descriptions — turning visual assets into matchable data.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>image-matching</category><category>ai</category><category>data-quality</category></item><item><title>Master data management: what it actually takes to keep one version of the truth</title><link>https://match-data.studio/blog/master-data-management-what-it-is/</link><guid isPermaLink="true">https://match-data.studio/blog/master-data-management-what-it-is/</guid><description>Master data management (MDM) is the practice of creating and maintaining a single, accurate view of your core business entities. Here&apos;s what it involves, why it fails, and what modern tools change.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>master-data</category><category>data-quality</category><category>getting-started</category></item><item><title>Matching with images and attributes: the complete file-based matching workflow</title><link>https://match-data.studio/blog/matching-images-attributes-ai/</link><guid isPermaLink="true">https://match-data.studio/blog/matching-images-attributes-ai/</guid><description>Text matching misses products that look identical but are described differently. File-based matching adds images, PDFs, and documents to the comparison — combining visual and textual signals for accurate results.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>image-matching</category><category>product-matching</category><category>ai</category><category>file-matching</category></item><item><title>Extracting structured data from PDFs: categorization, attributes, and matching</title><link>https://match-data.studio/blog/pdf-categorization-data-extraction/</link><guid isPermaLink="true">https://match-data.studio/blog/pdf-categorization-data-extraction/</guid><description>PDFs contain structured information trapped in unstructured format. AI extraction turns invoices, contracts, reports, and spec sheets into matchable data rows — no manual data entry required.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>pdf-extraction</category><category>ai</category><category>data-quality</category></item><item><title>Product data annotation: why your catalog needs more attributes before matching works</title><link>https://match-data.studio/blog/product-data-annotation-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/product-data-annotation-matching/</guid><description>Product matching accuracy depends on attribute richness. Sparse product data produces weak matches. Here&apos;s how to annotate product catalogs — manually and with AI — to make matching reliable.</description><pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate><category>data-quality</category><category>e-commerce</category><category>product-matching</category></item><item><title>Marketplace data deduplication: cleaning scraped listings at scale</title><link>https://match-data.studio/blog/marketplace-deduplication-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/marketplace-deduplication-matching/</guid><description>Scraped marketplace data is full of duplicate listings — different sellers, different titles, same underlying product. AI-powered deduplication collapses these into canonical records for reliable analytics and catalog management.</description><pubDate>Tue, 17 Feb 2026 00:00:00 GMT</pubDate><category>web-scraping</category><category>e-commerce</category><category>data-quality</category></item><item><title>Matching at scale: strategies for millions of records</title><link>https://match-data.studio/blog/matching-at-scale-performance-strategies/</link><guid isPermaLink="true">https://match-data.studio/blog/matching-at-scale-performance-strategies/</guid><description>How to handle the N x M explosion in record matching — blocking strategies, pre-filter cascades, batch processing, and fault tolerance for large datasets.</description><pubDate>Wed, 28 Jan 2026 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>best-practices</category><category>data-quality</category></item><item><title>How to measure matching quality: precision, recall, and F1 for data teams</title><link>https://match-data.studio/blog/measuring-matching-quality-precision-recall/</link><guid isPermaLink="true">https://match-data.studio/blog/measuring-matching-quality-precision-recall/</guid><description>Number of matches is not a quality metric. Learn how to measure precision, recall, and F1 score for data matching — including practical sampling methods when you don&apos;t have labeled data.</description><pubDate>Sun, 25 Jan 2026 00:00:00 GMT</pubDate><category>best-practices</category><category>data-quality</category><category>getting-started</category></item><item><title>Lead enrichment through data matching: combining scraped and CRM data</title><link>https://match-data.studio/blog/lead-enrichment-matching-scraped-data/</link><guid isPermaLink="true">https://match-data.studio/blog/lead-enrichment-matching-scraped-data/</guid><description>Your CRM has 50K contacts with gaps. Scraped conference lists, directories, and profiles have the missing data. AI-powered matching connects records across sources — even when names, titles, and company names don&apos;t match exactly.</description><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><category>web-scraping</category><category>crm</category><category>lead-management</category></item><item><title>How to set up blocking keys to speed up large matching jobs</title><link>https://match-data.studio/blog/blocking-keys-large-matching-jobs/</link><guid isPermaLink="true">https://match-data.studio/blog/blocking-keys-large-matching-jobs/</guid><description>Matching slows down fast at scale. Learn how blocking keys reduce comparisons by orders of magnitude, how to choose effective keys, and how multi-pass blocking recovers missed pairs.</description><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>best-practices</category><category>data-quality</category></item><item><title>How to match, deduplicate, and enrich mailing lists for direct mail</title><link>https://match-data.studio/blog/mailing-list-matching-direct-mail/</link><guid isPermaLink="true">https://match-data.studio/blog/mailing-list-matching-direct-mail/</guid><description>Duplicate mailers waste money and annoy recipients. Learn how to match across mailing lists, standardize addresses for deliverability, and build a clean send file.</description><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><category>data-quality</category><category>crm</category><category>best-practices</category></item><item><title>Building competitive intelligence from scraped data with record matching</title><link>https://match-data.studio/blog/competitive-intelligence-data-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/competitive-intelligence-data-matching/</guid><description>Scraped data from multiple platforms contains the same entities represented differently. AI-powered record matching connects job postings, product listings, reviews, and properties across sources to build a unified competitive intelligence picture.</description><pubDate>Tue, 02 Dec 2025 00:00:00 GMT</pubDate><category>web-scraping</category><category>competitive-intelligence</category><category>best-practices</category></item><item><title>How to match product catalogs from different suppliers</title><link>https://match-data.studio/blog/product-catalog-matching-suppliers/</link><guid isPermaLink="true">https://match-data.studio/blog/product-catalog-matching-suppliers/</guid><description>Different suppliers describe the same products differently. Learn how to match catalogs by name, SKU, specs, and AI embeddings to build a unified product taxonomy.</description><pubDate>Fri, 28 Nov 2025 00:00:00 GMT</pubDate><category>supply-chain</category><category>product-matching</category><category>e-commerce</category></item><item><title>How to consolidate customer records from two companies after a merger</title><link>https://match-data.studio/blog/customer-record-consolidation-merger/</link><guid isPermaLink="true">https://match-data.studio/blog/customer-record-consolidation-merger/</guid><description>After an M&amp;A deal, two CRMs become one. Learn how to match, deduplicate, and merge customer records while handling subsidiaries, DBAs, and conflicting data.</description><pubDate>Wed, 12 Nov 2025 00:00:00 GMT</pubDate><category>data-quality</category><category>crm</category><category>entity-resolution</category></item><item><title>From web scraping to price intelligence: matching products across competing sites</title><link>https://match-data.studio/blog/web-scraping-price-comparison-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/web-scraping-price-comparison-matching/</guid><description>Scraped product data from competitor sites uses different naming conventions, SKU systems, and category structures. AI-powered matching connects equivalent products across sources to build real-time competitive pricing intelligence.</description><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><category>web-scraping</category><category>e-commerce</category><category>price-intelligence</category></item><item><title>How to validate a data migration by matching source and target records</title><link>https://match-data.studio/blog/data-migration-validation-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/data-migration-validation-matching/</guid><description>Catch migration errors before go-live. Learn how to match source and target records, check for data loss, detect transformation errors, and build a migration validation report.</description><pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate><category>data-quality</category><category>best-practices</category></item><item><title>Deduplicating real estate CRM contacts against acquired lead lists</title><link>https://match-data.studio/blog/real-estate-crm-lead-deduplication/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-crm-lead-deduplication/</guid><description>Brokerages spend thousands importing lead lists only to re-contact existing clients and miss routing accuracy. AI matching identifies duplicates that name-and-email matching misses — including changed contact info and nickname variations.</description><pubDate>Thu, 23 Oct 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>crm</category><category>lead-management</category><category>brokerages</category></item><item><title>How to match transaction records across accounting systems for reconciliation</title><link>https://match-data.studio/blog/transaction-matching-financial-reconciliation/</link><guid isPermaLink="true">https://match-data.studio/blog/transaction-matching-financial-reconciliation/</guid><description>Transaction reconciliation involves matching records across systems with amount differences, date offsets, and description mismatches. A step-by-step guide for finance teams.</description><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><category>finance</category><category>data-quality</category><category>best-practices</category></item><item><title>How to match candidates across job boards, ATS systems, and referral lists</title><link>https://match-data.studio/blog/candidate-matching-recruiting-ats/</link><guid isPermaLink="true">https://match-data.studio/blog/candidate-matching-recruiting-ats/</guid><description>Duplicate candidate records waste recruiter time and create poor candidate experiences. Learn how to build a unified talent pool by matching across sourcing channels.</description><pubDate>Mon, 22 Sep 2025 00:00:00 GMT</pubDate><category>hr-recruiting</category><category>data-quality</category><category>crm</category></item><item><title>Recovering lost skip trace matches with AI: a guide for wholesalers</title><link>https://match-data.studio/blog/real-estate-skip-trace-contact-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-skip-trace-contact-matching/</guid><description>When you merge skip-traced contact data back to your property owner list, a 30% non-match rate is common. Here&apos;s why it happens and how AI-powered matching recovers contacts you&apos;re currently leaving behind.</description><pubDate>Thu, 11 Sep 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>skip-tracing</category><category>wholesaling</category></item><item><title>How to deduplicate your vendor list and stop paying the same supplier twice</title><link>https://match-data.studio/blog/vendor-supplier-deduplication-procurement/</link><guid isPermaLink="true">https://match-data.studio/blog/vendor-supplier-deduplication-procurement/</guid><description>Duplicate vendor records cause overpayments, missed discounts, and audit findings. Learn how to match vendor records across ERPs and maintain a clean vendor master.</description><pubDate>Fri, 05 Sep 2025 00:00:00 GMT</pubDate><category>supply-chain</category><category>data-quality</category><category>best-practices</category></item><item><title>How to match patient records across hospital systems without a universal ID</title><link>https://match-data.studio/blog/patient-record-matching-healthcare/</link><guid isPermaLink="true">https://match-data.studio/blog/patient-record-matching-healthcare/</guid><description>A practical guide to patient record matching — handling nicknames, maiden names, transposed digits, and HIPAA constraints to build a reliable Master Patient Index.</description><pubDate>Wed, 20 Aug 2025 00:00:00 GMT</pubDate><category>healthcare</category><category>data-quality</category><category>entity-resolution</category></item><item><title>How to compare two datasets to find differences and matches</title><link>https://match-data.studio/blog/how-to-compare-two-datasets/</link><guid isPermaLink="true">https://match-data.studio/blog/how-to-compare-two-datasets/</guid><description>A systematic approach to comparing two datasets — finding overlapping records, identifying gaps, detecting conflicting values, and building a comparison report.</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>getting-started</category><category>data-quality</category><category>best-practices</category></item><item><title>Deduplicating MLS listings across multiple feed sources</title><link>https://match-data.studio/blog/real-estate-mls-listing-deduplication/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-mls-listing-deduplication/</guid><description>Real estate portals and brokerages ingesting data from multiple MLS feeds routinely encounter the same property listed twice with conflicting details. AI-powered deduplication keeps inventory counts accurate and analytics trustworthy.</description><pubDate>Mon, 04 Aug 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>mls</category><category>data-quality</category><category>proptech</category></item><item><title>How to match two CSV files in Python (and when to use a tool instead)</title><link>https://match-data.studio/blog/how-to-match-two-csv-files-python/</link><guid isPermaLink="true">https://match-data.studio/blog/how-to-match-two-csv-files-python/</guid><description>From pandas merge to fuzzywuzzy to recordlinkage — a practical guide to CSV matching in Python, plus a framework for deciding when custom code stops making sense.</description><pubDate>Sun, 20 Jul 2025 00:00:00 GMT</pubDate><category>getting-started</category><category>matching-algorithms</category><category>best-practices</category></item><item><title>Matching rental applicants against tenant history records</title><link>https://match-data.studio/blog/real-estate-tenant-applicant-history-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-tenant-applicant-history-matching/</guid><description>Property managers lose thousands to missed eviction flags and fair housing violations caused by poor record matching. Here&apos;s how AI changes the accuracy equation for tenant screening.</description><pubDate>Tue, 15 Jul 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>property-management</category><category>tenant-screening</category></item><item><title>How to find and remove duplicates in a CSV file</title><link>https://match-data.studio/blog/how-to-find-duplicates-csv/</link><guid isPermaLink="true">https://match-data.studio/blog/how-to-find-duplicates-csv/</guid><description>Excel&apos;s Remove Duplicates misses most real-world duplicates. Learn field-by-field fuzzy deduplication, threshold tuning, and strategies for CSV files with 100K+ rows.</description><pubDate>Wed, 02 Jul 2025 00:00:00 GMT</pubDate><category>data-quality</category><category>getting-started</category><category>best-practices</category></item><item><title>How to merge two spreadsheets when the data doesn&apos;t match exactly</title><link>https://match-data.studio/blog/how-to-merge-two-spreadsheets/</link><guid isPermaLink="true">https://match-data.studio/blog/how-to-merge-two-spreadsheets/</guid><description>VLOOKUP and INDEX-MATCH break when data is messy. Learn how to merge two spreadsheets with fuzzy matching, AI-powered joins, and practical validation steps.</description><pubDate>Sun, 15 Jun 2025 00:00:00 GMT</pubDate><category>getting-started</category><category>best-practices</category><category>data-quality</category></item><item><title>Reconciling property owner names for HOA and tax billing</title><link>https://match-data.studio/blog/real-estate-owner-billing-reconciliation/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-owner-billing-reconciliation/</guid><description>Trust transfers, LLC ownership, and inherited properties silently break HOA and municipal billing rosters. Traditional fuzzy matching can&apos;t bridge the gap — but AI can reason across it.</description><pubDate>Fri, 06 Jun 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>hoa</category><category>property-management</category><category>billing</category></item><item><title>List stacking for real estate investors: finding motivated sellers with AI</title><link>https://match-data.studio/blog/real-estate-list-stacking-motivated-sellers/</link><guid isPermaLink="true">https://match-data.studio/blog/real-estate-list-stacking-motivated-sellers/</guid><description>How real estate wholesalers and investors use AI-powered record matching to identify property owners who appear on multiple distress signal lists — and why those owners convert at dramatically higher rates.</description><pubDate>Mon, 19 May 2025 00:00:00 GMT</pubDate><category>real-estate</category><category>list-stacking</category><category>investors</category></item><item><title>AI embeddings vs rule-based matching: when to use each</title><link>https://match-data.studio/blog/ai-embeddings-vs-rule-based-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/ai-embeddings-vs-rule-based-matching/</guid><description>A comparison of rule-based and AI embedding approaches to record matching — strengths, weaknesses, costs, and why the best systems use both.</description><pubDate>Tue, 08 Apr 2025 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>ai</category><category>best-practices</category></item><item><title>How to choose the right matching algorithm for your data</title><link>https://match-data.studio/blog/how-to-choose-matching-algorithm/</link><guid isPermaLink="true">https://match-data.studio/blog/how-to-choose-matching-algorithm/</guid><description>A practical decision guide for selecting matching algorithms based on data type, quality, and scale — from simple name matching to multi-field entity resolution.</description><pubDate>Thu, 27 Feb 2025 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>best-practices</category><category>getting-started</category></item><item><title>Understanding similarity thresholds</title><link>https://match-data.studio/blog/understanding-similarity-thresholds/</link><guid isPermaLink="true">https://match-data.studio/blog/understanding-similarity-thresholds/</guid><description>What cosine similarity scores mean in practice, and how to tune thresholds to get the right balance of precision and recall.</description><pubDate>Tue, 14 Jan 2025 00:00:00 GMT</pubDate><category>deep-dive</category><category>configuration</category></item><item><title>Address matching and standardization: a practical guide</title><link>https://match-data.studio/blog/address-matching-standardization-guide/</link><guid isPermaLink="true">https://match-data.studio/blog/address-matching-standardization-guide/</guid><description>Addresses are the hardest field to match. Abbreviations, unit numbers, directionals, and international formats make exact matching useless. Here&apos;s how to handle them.</description><pubDate>Mon, 16 Dec 2024 00:00:00 GMT</pubDate><category>data-quality</category><category>best-practices</category><category>matching-algorithms</category></item><item><title>Five matching mistakes that silently ruin your results</title><link>https://match-data.studio/blog/common-matching-mistakes/</link><guid isPermaLink="true">https://match-data.studio/blog/common-matching-mistakes/</guid><description>These five common record matching errors don&apos;t throw exceptions or show warnings. They just quietly produce bad results. Here&apos;s how to identify and fix each one.</description><pubDate>Mon, 28 Oct 2024 00:00:00 GMT</pubDate><category>best-practices</category><category>data-quality</category></item><item><title>Fuzzy matching algorithms explained: Levenshtein, Jaro-Winkler, and beyond</title><link>https://match-data.studio/blog/fuzzy-matching-algorithms-explained/</link><guid isPermaLink="true">https://match-data.studio/blog/fuzzy-matching-algorithms-explained/</guid><description>A practical breakdown of six fuzzy matching algorithms — how they work, where they excel, and when to combine them for record matching across messy datasets.</description><pubDate>Mon, 09 Sep 2024 00:00:00 GMT</pubDate><category>matching-algorithms</category><category>fuzzy-matching</category><category>data-quality</category></item><item><title>Data cleaning before matching: the steps most people skip</title><link>https://match-data.studio/blog/data-cleaning-before-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/data-cleaning-before-matching/</guid><description>80% of matching improvement comes from basic data cleaning. Here are the specific steps — with before and after examples — that transform garbage-in matches into reliable results.</description><pubDate>Mon, 22 Jul 2024 00:00:00 GMT</pubDate><category>data-quality</category><category>best-practices</category><category>getting-started</category></item><item><title>Entity resolution explained: turning messy records into clean data</title><link>https://match-data.studio/blog/entity-resolution-what-it-is/</link><guid isPermaLink="true">https://match-data.studio/blog/entity-resolution-what-it-is/</guid><description>Entity resolution is the process of determining when two records refer to the same real-world entity. Here&apos;s what it is, why it&apos;s hard, and what happens when you get it wrong.</description><pubDate>Mon, 03 Jun 2024 00:00:00 GMT</pubDate><category>entity-resolution</category><category>data-quality</category><category>getting-started</category></item><item><title>Getting started with CSV matching</title><link>https://match-data.studio/blog/getting-started-with-csv-matching/</link><guid isPermaLink="true">https://match-data.studio/blog/getting-started-with-csv-matching/</guid><description>A step-by-step walkthrough of your first matching job in Match Data Studio — from upload to download.</description><pubDate>Fri, 12 Apr 2024 00:00:00 GMT</pubDate><category>tutorial</category><category>getting-started</category></item></channel></rss>