Adjacent Data Concepts
14 articles
- May 21, 2026
What Is Data Integration? A Plain-English Guide for Non-Engineers
Data integration connects data from multiple systems into a unified view. Here's what it means, how it works, and why data quality is the hardest part of doing it well.
- May 21, 2026
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.
- May 21, 2026
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.
- May 21, 2026
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.
- May 21, 2026
What Is a Data Warehouse? And When Does Your Business Actually Need One?
A data warehouse centralizes your business data for analytics and reporting. Here's what it is, how it works, and — more importantly — when you actually need one.
- May 21, 2026
What Is a Data Pipeline? How Data Flows Work (And Where They Break)
A data pipeline moves data from where it's created to where it's used. Understanding how pipelines work — and where they fail — is essential for maintaining data quality.
- May 21, 2026
What Is dbt (Data Build Tool) and How Does It Relate to Data Quality?
dbt has become one of the most widely adopted tools in the modern data stack. Here's what it does, how it works, and why it matters for data quality — even if you're not an engineer.
- May 21, 2026
What Is a Data Lake? And How Does Data Quality Work Inside One?
A data lake stores raw data at any scale and in any format. That flexibility is powerful — and it's exactly why data quality is so hard to maintain inside one.
- May 21, 2026
What Is DAMA? Why Data Professionals Use This Framework
DAMA is the most widely referenced framework for data management and data quality. Here's what it is, what the DMBOK covers, and why its 6 data quality dimensions became the standard.
- May 21, 2026
What Is Change Data Capture (CDC)? Why It Matters for Data Quality
Change Data Capture tracks what changes in your databases and when — making it one of the most powerful techniques for maintaining data quality in real-time systems.
- May 21, 2026
How Data Quality Fits Into the Modern Data Stack (Even If You're a Small Team)
The modern data stack has changed how companies collect, move, and analyze data. Here's where data quality fits in — and what it means for teams of any size.
- May 21, 2026
What Is Data Fabric? A Plain-English Guide for Non-Data-Engineers
Data fabric is one of the most hyped concepts in enterprise data architecture. Here's what it actually means, stripped of vendor marketing, and whether it matters for your team.
- May 21, 2026
What Is Data Observability? How It's Different From Data Quality (And Why You Need Both)
Data observability and data quality are often confused, but they solve different problems. Here's how to tell them apart — and why you need both.
- May 21, 2026
What Is Metadata? Why It's the Hidden Key to Better Data Quality
Metadata is data about your data — and it's often the reason data quality problems are hard to find and fix. Here's what metadata is and how to use it to your advantage.