Data Quality Insights

Latest articles

• May 21, 2026

  Data Deduplication

  What Is Data Deduplication and Why It Matters

  Data deduplication is the process of identifying and removing duplicate records from a dataset — and it's one of the highest-impact data quality operations for most businesses.

  6 min read

• May 21, 2026

  Data Quality Glossary

  What Is a Data Catalog? (And Does Your Business Actually Need One?)

  A data catalog is an organized inventory of your data assets — helping teams find, understand, and trust the data they work with. Here's what it is and when you actually need one.

  5 min read

• May 21, 2026

  Data Quality Fundamentals

  Data Completeness: What It Is and Why It Matters

  Data completeness measures what percentage of required fields have values. Low completeness silently breaks filters, segments, and reports.

  3 min read

• May 21, 2026

  Data Profiling

  How to Detect Missing Values and Null Patterns in Your Dataset

  Missing values silently break analysis, filters, and campaigns. Here's how to detect them systematically and understand why they're appearing.

  3 min read

• May 21, 2026

  Data Standardization

  How to Handle International Date Formats in Global Datasets

  03/05/2024 means March 5th in the US and May 3rd in Europe. When your dataset contains dates from multiple countries, format ambiguity creates silent errors that corrupt your analytics.

  6 min read

• May 21, 2026

  Data Standardization

  Currency Normalization: Handling Multiple Currencies in One Dataset

  When your dataset contains transactions in USD, EUR, GBP, and JPY, every financial calculation that aggregates across currencies is wrong unless you normalize first. Here's how.

  6 min read

• May 21, 2026

  Data Standardization

  How to Standardize Categorical Data (Enums, Picklists, Dropdowns)

  Status fields with 47 variants. Industry fields with 200+ unique values. Categorical data chaos is one of the most common and most fixable data quality problems. Here's how to fix it.

  5 min read

• May 21, 2026

  Data Standardization

  How to Standardize Product Names and SKUs Across Systems

  When your product catalog, inventory system, and e-commerce platform use different names and IDs for the same product, every cross-system report is unreliable. Here's how to fix it.

  6 min read

• May 21, 2026

  Data Standardization

  Handling Special Characters and Encoding Issues in Data

  é, ’, and □□□ in your data aren't corrupted — they're encoding errors. Here's what causes them and how to fix them before they break your imports and reports.

  6 min read

• May 21, 2026

  Comparisons

  Data Quality vs. Data Cleansing: What's the Difference?

  You're trying to fix problems in your dataset, and every article you read uses "data quality" and "data cleansing" as if they mean the same thing. They don't — and the distinction matters when you're deciding what to do next.

  6 min read

• May 21, 2026

  Data Validation

  Pattern Matching for Data Quality: A Practical Guide

  Pattern matching validates that field values follow a specific structural pattern — catching postal codes with letters, order IDs in the wrong format, and phone numbers that are clearly wrong.

  4 min read

• May 21, 2026

  Data Standardization

  Why Inconsistent Data Formats Are Killing Your Reports (And How to Fix It)

  Your reports are wrong. Not because the calculations are incorrect — but because the data they're built on has inconsistent formats that fragment your metrics into incomparable groups. Here's how to find and fix it.

  7 min read

• May 21, 2026

  Data Deduplication

  How to Find Exact Duplicate Records in a Database or Spreadsheet

  Exact duplicates — records where every field matches — are the easiest duplicates to find. Here's exactly how to do it in SQL, Excel, Google Sheets, and with a data quality tool.

  6 min read

• May 21, 2026

  Data Deduplication

  Fuzzy Matching: How to Find Near-Duplicate Records

  Exact duplicates are easy. Near-duplicates — 'John Smith' and 'Jon Smith,' or 'IBM Corp' and 'IBM Corporation' — are the harder problem. Here's how fuzzy matching catches them.

  6 min read

• May 21, 2026

  Workflows & Migrations

  How to Handle Data Quality Failures in an Automated Workflow

  When automated workflows encounter data quality failures, most systems either crash or silently skip the bad record. Neither is good. Here's how to build workflows that handle failures gracefully.

  6 min read

• May 21, 2026

  Comparisons

  Excel vs. a Data Quality Tool: When Do You Need to Upgrade?

  You've been using Excel formulas, conditional formatting, and manual spot-checks to manage data quality for years — and it mostly works. Now something is making you wonder whether a dedicated data quality tool is worth considering.

  7 min read

• May 21, 2026

  Data Quality Fundamentals

  Data Consistency: The Silent Killer of Business Insights

  Data consistency means the same fact is represented the same way across all systems. Inconsistency silently breaks every cross-system report you build.

  3 min read

• May 21, 2026

  Data Quality Glossary

  What Is a Data Quality Rule?

  A data quality rule is a defined condition that data must satisfy to be considered valid for its intended use. Here's what rules look like, how they work, and how to write effective ones.

  5 min read

• May 21, 2026

  Adjacent Data Concepts

  What Is Data Governance? A Beginner's Guide for Small Business Owners

  Data governance sounds like a big-company problem. It isn't. Here's what it actually means and why even small teams need at least a basic version of it.

  4 min read

• May 21, 2026

  Data Quality FAQs

  What Is the First Step to Improving Data Quality?

  The first step to improving data quality is to measure it. You cannot prioritize what to fix, or know whether improvements are working, without baseline quality metrics.

  7 min read

• May 21, 2026

  CSV & Spreadsheet Data Quality

  How to Find Duplicate Records in a CSV File

  Duplicate records inflate counts, cause double-sends, and split customer history. Here's how to find them in a CSV file — with and without specialized tools.

  2 min read

• May 21, 2026

  Adjacent Data Concepts

  What Is a Data Mesh? A Plain-English Guide for Growing Teams

  Data mesh is one of the most talked-about concepts in modern data architecture. Here's what it actually means — without the jargon — and whether it applies to your team.

  4 min read

• May 21, 2026

  Adjacent Data Concepts

  What Is a Data Lakehouse? A Plain-English Guide for Non-Technical Teams

  A data lakehouse combines the flexibility of a data lake with the structure of a data warehouse. Here's what that means in plain English — and what it has to do with data quality.

  4 min read