Blog posts written by
Chris Bergh

The Illusion of Clean Data: The Pharma Identifiers Generic Tools Never Check

Chris Bergh · August 26, 2026

Webinar: The Seven Deadly Sins of AI-Generated Data Pipelines

Chris Bergh · August 17, 2026

The Seven Deadly Sins of AI-Generated Data Pipelines

Chris Bergh · August 16, 2026

I Wrote Data Quality Tests for 3 Million NYC Taxi Rides in Five Minutes

Chris Bergh · August 7, 2026

Your Best Data Quality Rules Live in Someone Else's Head

Chris Bergh · August 4, 2026

There Is No Microsoft Data Quality Tool in 2026. There Are Nineteen.

Chris Bergh · August 4, 2026

The Blueprint or the Fire Alarm Panel: Why Data Test Coverage Beats Data Lineage

Chris Bergh · July 27, 2026

IQVIA, specialty pharmacy, and your own files: the commercial data most likely to be wrong

Chris Bergh · July 15, 2026

Webinar: Stop Clicking, Start Asking — The AI Playbook For Data Quality

Chris Bergh · July 15, 2026

Why Testing Commercial Pharma Data Is Harder Than Anyone Tells You

Chris Bergh · July 8, 2026

TestGen MCP Cheat Sheet: 96 Tools, One Page, Zero Excuses

Chris Bergh · July 6, 2026

Shopping for Tests: A New Faceted Picker for TestGen's Test Catalog

Chris Bergh · June 30, 2026

Agents to Fix Data Quality Automatically With Control: A Design Pattern

Chris Bergh · June 9, 2026

The Four Points in Your Medallion Architecture Where Data Testing Really Matters

Chris Bergh · June 3, 2026

Webinar: 10x Your Data Engineering With AI

Chris Bergh · May 20, 2026

Hoarding cash before the FDA gate opens? Your data and analytics bill is the easiest line to cut.

Chris Bergh · May 18, 2026

When the cloud goes dark: a note to Great Expectations customers

Chris Bergh · May 7, 2026

There are a lot of freaking data quality and data observability vendors

Chris Bergh · April 30, 2026

The Data Contract Is Your Receipt

Chris Bergh · April 20, 2026

Context As Infrastructure: The Six-Category Framework for AI-Ready Data Analysis

Chris Bergh · April 16, 2026

Webinar: 10x Your AI Data Analysis — The Context Engineering Formula That Works

Chris Bergh · April 14, 2026

Data Production Tripwires in Databricks: Stop Bad Data Before It Reaches Production

Chris Bergh · April 6, 2026

Introducing BuzzOps: A Tool to Translate Vendor BS. You're Welcome.

Chris Bergh · April 1, 2026

Webinar: The Four Points In Your Medallion Architecture Where Data Testing Really Matters

Chris Bergh · March 31, 2026

DataKitchen Enhances Its Support for Data Stewards to Manage Data Quality Tests in TestGen

Chris Bergh · March 30, 2026

TestGen Now Supports Oracle and SAP HANA, with a New Setup Wizard to Get You Running Fast

Chris Bergh · March 30, 2026

$1 Billion in Data Observability VC Investment: This Is Not Going to End Well

Chris Bergh · March 24, 2026

DataOps + FITT + Data Testing = 10x Data Engineering Productivity with AI

Chris Bergh · March 16, 2026

We Just Eyeball Row Counts and Pray

Chris Bergh · March 16, 2026

The Equation For AI Success: DT + DX + CTX = 10x

Chris Bergh · March 10, 2026

Why Your Data Quality Dashboard Isn't Working And What to Do About It

Chris Bergh · March 9, 2026

Webinar: You’re Massively Overpaying For Data Observability

Chris Bergh · February 26, 2026

Be The First To Know: Smart, Continuous Table Monitoring Has Arrived In TestGen

Chris Bergh · February 24, 2026

Data Quality Testing Is At The Core of Four Critical Data Team Processes

Chris Bergh · February 10, 2026

Stop Paying The Data Quality Tax

Chris Bergh · February 5, 2026

On-Demand Webinar: Seven Sins Of Data Quality

Chris Bergh · January 20, 2026

The Five Organizational Sins of Data Teams

Chris Bergh · January 19, 2026

The Data Consultant's Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen's Open Source TestGen

Chris Bergh · January 18, 2026

The Seven Deadly Sins of Data Quality

Chris Bergh · January 7, 2026

Webinar: A Masterclass In The Six Types Of Data Quality Dashboards

Chris Bergh · January 5, 2026

The Data Errors Shame Game: Why Data Engineers Avoid Harsh Truths

Chris Bergh · December 31, 2025

The Ostrich Problem: Your Data Team Thinks Their Job Ends at Deployment.

Chris Bergh · December 19, 2025

The 2026 Open Source Data Profiling Software Landscape

Chris Bergh · December 10, 2025

The 2026 Data Quality and Data Observability Commercial Software Landscape

Chris Bergh · December 1, 2025

Sure, Go Ahead And Feed That Data To The LLM … What Could Possibly Go Wrong?

Chris Bergh · November 19, 2025

Webinar: Data Quality, DataOps, and Large Language Models

Chris Bergh · November 6, 2025

You're Thinking About Data Products All Wrong

Chris Bergh · October 31, 2025

The 2026 Open-Source Data Quality and Data Observability Landscape

Chris Bergh · October 28, 2025

Webinar: The FITT Way To Data Products: A New Data Architecture For A Product-Centric World

Chris Bergh · October 14, 2025

DataOps Data Quality TestGen Expands: Now Supporting BigQuery and Apache Iceberg

Chris Bergh · October 13, 2025

Process Guardianship: The Most Valuable Data Engineering Work You're Probably Not Doing

Chris Bergh · October 8, 2025

Flip the Script on Data Quality: Shift Left, Shift Down, and Take Control

Chris Bergh · September 30, 2025

FITT vs. Fragile: SQL & Orchestration Techniques For FITT Data Architectures

Chris Bergh · August 18, 2025

Data Quality Test Coverage In a Medallion Data Architecture

Chris Bergh · August 11, 2025

Critical Data Elements: Your Shortcut to Data Governance That Actually Works

Chris Bergh · July 31, 2025

Webinar: Test Coverage: The Software Development Idea That Supercharges Data Quality & Data Engineering

Chris Bergh · July 17, 2025

Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers

Chris Bergh · July 8, 2025

The Data Quality Revolution Starts with You

Chris Bergh · June 20, 2025

Webinar: A Guide to the Six Types of Data Quality Dashboards

Chris Bergh · June 18, 2025

Data Quality Testing: A Shared Resource for Modern Data Teams

Chris Bergh · June 6, 2025

The $100 Billion Secret: Why Leading Pharma Companies Bring In a Dedicated Commercial Data Team

Chris Bergh · May 30, 2025

DataKitchen Is One Of The Coolest DataOps & Data Observability Companies of 2025

Chris Bergh · April 24, 2025

White Paper: A New, More Effective Approach To Data Quality Assessments

Chris Bergh · April 24, 2025

Webinar: A New, More Effective Approach To Data Quality Assessments

Chris Bergh · April 16, 2025

“Trusting Your Gut” 78.45% More Effective Than Data-Driven Decisions

Chris Bergh · April 1, 2025

Webinar: Announcing Actionable, Automated, & Agile Data Quality Scorecards – 2024

Chris Bergh · March 26, 2025

Unlocking Data Team Success: Are You Process-Centric or Data-Centric?

Chris Bergh · March 20, 2025

How Data Quality Leaders Can Gain Influence And Avoid The Tragedy of the Commons

Chris Bergh · March 13, 2025

Announcing Open Source DataOps Data Quality TestGen 3.0

Chris Bergh · February 20, 2025

No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically

Chris Bergh · February 17, 2025

Summary of the Gartner Presentation: “How Can You Leverage Technologies to Solve Data Quality Challenges?”

Chris Bergh · December 17, 2024

Webinar: Data Quality in a Medallion Architecture – 2024

Chris Bergh · December 6, 2024

A Guide to the Six Types of Data Quality Dashboards

Chris Bergh · November 27, 2024

The Race For Data Quality in a Medallion Architecture

Chris Bergh · November 5, 2024

Read White Paper: Data Quality The DataOps Way

Chris Bergh · October 10, 2024

Spotify

Podcast: The Data Strategy Show

Chris Bergh · October 8, 2024

Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

Chris Bergh · September 18, 2024

Data Hurdles

Podcast: Data Hurdles Poscast

Chris Bergh · July 8, 2024

How Three Small Pharma Companies Used DataKitchen to Achieve Commercial Launch Success and Skyrocket to $100 Billion in Acquisition Value

Chris Bergh · June 11, 2024

Data Observability and Data Quality Testing Certification Series

Chris Bergh · May 14, 2024

The Five Use Cases in Data Observability: Overview

Chris Bergh · May 10, 2024

The Five Use Cases in Data Observability: (#1) Data Quality in New Data Sources

Chris Bergh · May 10, 2024

The Five Use Cases in Data Observability: (#2) Effective Data Anomaly Monitoring

Chris Bergh · May 10, 2024

The Five Use Cases in Data Observability: (#3) Mastering Data Production

Chris Bergh · May 10, 2024

The Five Use Cases in Data Observability: (#4) Fast, Safe Development and Deployment

Chris Bergh · May 10, 2024

The Five Use Cases in Data Observability: (#5) Ensuring Accuracy in Data Migration

Chris Bergh · May 10, 2024

Webinar Summary: Introducing Open Source Data Observability

Chris Bergh · May 6, 2024

Why We Open-Sourced Our Data Observability Products

Chris Bergh · April 18, 2024

Webinar Summary: Agile, DataOps, and Data Team Excellence

Chris Bergh · April 11, 2024

Congratulations to the Karuna Team for their acquisition!

Chris Bergh · March 18, 2024

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software

Chris Bergh · March 12, 2024

ngx-toolkit, a new open-source project from DataKitchen

Chris Bergh · March 5, 2024

Why Not Hearing About Data Errors Should Worry Your Data Team

Chris Bergh · March 5, 2024

Your LLM Needs a Data Journey: A Comprehensive Generative AI Guide for Data Engineers

Chris Bergh · February 27, 2024

DataKitchen Resource Guide To Data Observability & DataOps

Chris Bergh · February 23, 2024

The Art of Data Buck-Passing 101: Mastering the Blame Game in Data and Analytic Teams

Chris Bergh · February 22, 2024

On Demand Webinar: Beyond Data Observability

Chris Bergh · December 7, 2023

Navigating the Chaos of Unruly Data: Solutions for Data Teams

Chris Bergh · November 10, 2023

On Demand Webinar: Data Observability Demo Day

Chris Bergh · November 6, 2023

The Need For Personalized Data Journeys for Your Data Consumers

Chris Bergh · October 20, 2023

Data Teams and Their Types of Data Journeys

Chris Bergh · October 2, 2023

Bridging the Gap: How 'Data in Place' and 'Data in Use' Define Complete Data Observability

Chris Bergh · September 21, 2023

On Demand Webinar: Automated Test Generation – Why Data Teams Need It

Chris Bergh · September 20, 2023

A Summary Of Gartner's Recent Innovation Insight Into Data Observability

Chris Bergh · August 8, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

Chris Bergh · July 27, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validation Testing

Chris Bergh · July 12, 2023

Introducing The Five Pillars Of Data Journeys

Chris Bergh · June 19, 2023

Why the Data Journey Manifesto?

Chris Bergh · June 12, 2023

Webinar Summary: Data Mesh and Data Products

Chris Bergh · May 4, 2023

ON-DEMAND WEBINAR: Data Products and Data Mesh

Chris Bergh · May 4, 2023

Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms

Chris Bergh · April 11, 2023

ON-DEMAND WEBINAR: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms

Chris Bergh · April 7, 2023

Two Downs Make Two Ups: The Only Success Metrics That Matter For Your Data & Analytics Team

Chris Bergh · March 16, 2023

Only One Problem To Solve for Successful Data and Analytics

Chris Bergh · March 8, 2023

A summary of Gartner's recent DataOps-driven data engineering best practices article

Chris Bergh · February 21, 2023

Question: What is the difference between Data Quality and DataOps Observability?

Chris Bergh · February 19, 2023

A Data Prediction for 2025

Chris Bergh · February 2, 2023

Discovering Data

Podcast: Discovering Data: Build a little, test a little, learn a lot

Chris Bergh · January 26, 2023

"You Complete Me," said Data Lineage to Data Journeys.

Chris Bergh · January 23, 2023

The Terms and Conditions of a Data Contract are Data Tests

Chris Bergh · December 29, 2022

An AI Chat Bot Wrote This Blog Post …

Chris Bergh · December 9, 2022

Gartner Market Guide to DataOps Software

Chris Bergh · December 6, 2022

DataOps Observability and Automation to the Rescue!

Chris Bergh · December 1, 2022

DataOps For Beginners

Chris Bergh · February 18, 2022

What Is ‘Equity As Code,’ And How Can It Eliminate AI Bias?

Chris Bergh · October 28, 2021

Forbes

What Is ‘Equity As Code,’ And How Can It Eliminate AI Bias?

Chris Bergh · June 7, 2021

Forbes

Managing Data Analytics Is More Like Running A Restaurant Than You Think

Chris Bergh · April 5, 2021

Why DevOps Tools Fail at DataOps

Chris Bergh · October 26, 2020

Why Are There So Many *Ops Terms?

Chris Bergh · May 5, 2020

A Note to Our Customers on Covid-19

Chris Bergh · March 23, 2020

Gartner: “Select a DataOps Platform for 2020”

Chris Bergh · March 3, 2020

DataOps Aphorisms

Chris Bergh · February 14, 2020

Iowa Caucuses, do your DataOps

Chris Bergh · February 4, 2020

How to Get Started with DataOps

Chris Bergh · December 17, 2019

A summary of Gartner’s Recent DataOps Report and recommendations

Chris Bergh · November 1, 2019

“The Unicorn Project” Review & DataOps Excerpt

Chris Bergh · October 30, 2019

Gartner Research Names DataKitchen “Cool Vendor”

Chris Bergh · June 30, 2019

451 Group On DataKitchen

Chris Bergh · June 24, 2019

Gartner Puts DataOps on the Rise in 2018

Chris Bergh · July 24, 2018

Welcome to the DataKitchen Company Blog

Chris Bergh · June 18, 2018