DataKitchen

APRIL 14, 2025

Why Data Quality Dimensions Fall Flat : Data Quality Coffee With Uncle Chip #2 In this playful yet pointed talk, Data Quality Coffee With Uncle Chip’ kicks things off by poking fun at the overcomplicated world of data quality dimensions. With so many dimensions and no consensus on definitions, vague terms like “accuracy” and “validity” just blur together.

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DataKitchen

APRIL 9, 2025

Why Data Quality Isnt Worth The Effort : Data Quality Coffee With Uncle Chip Data quality has become one of the most discussed challenges in modern data teams, yet it remains one of the most thankless and frustrating responsibilities. In the first of the Data Quality Coffee With Uncle Chip series, he highlights the persistent tension between the need for clean, reliable data and its overwhelming complexity.

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DataKitchen

APRIL 3, 2025

Improving data quality can feel overwhelming. There are so many things to fix and so many processes to improve. Where do you even start? Surprisingly, the answer may come from an unusual placeflossing your teeth. Start Small to Build Better Habits In the book Tiny Habits by B.J. Fogg, the author suggests a simple way to build habits. If you want to start flossing your teeth, dont aim for a perfect routine right away.

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DataKitchen

APRIL 1, 2025

Groundbreaking Study: Trusting Your Gut 78.45% More Effective Than Data-Driven Decisions, Say Top Execs April 1, 2025: CAMBRIDGE, MA In a shocking reversal of modern business orthodoxy, a joint study from Harvard Business School and Gartner has concluded that trusting your gut and doing what sounds good are 78.45% more effective than traditional data analytics, integration, or any attempt to be data-driven.

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DataKitchen

MARCH 26, 2025

Announcing Actionable, Automated, & Agile Data Quality Scorecards Are you ready to unlock the power of influence to transform your organizations data qualityand become the hero your data deserves? Watch the previously recorded webinar unveiling our latest innovation: Data Quality Scorecards, powered by our AI-driven DataOps Data Quality TestGen software.

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DataKitchen

MARCH 20, 2025

Unlocking Data Team Success: Are You Process-Centric or Data-Centric? Over the years of working with data analytics teams in large and small companies, we have been fortunate enough to observe hundreds of companies. We want to share our observations about data teams, how they work and think, and their challenges. We’ve identified two distinct types of data teams: process-centric and data-centric.

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DataKitchen

MARCH 13, 2025

How Data Quality Leaders Can Gain Influence And Avoid The Tragedy of the Commons Data quality has long been essential for organizations striving for data-driven decision-making. Despite the best efforts of data teams, poor data quality remains a persistent challenge, leading to distrust in analytics, inefficiencies in operations, and costly errors. Many organizations struggle with incomplete, inconsistent, or outdated data, making it difficult to derive reliable insights.

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DataKitchen

FEBRUARY 20, 2025

Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball.

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DataKitchen

FEBRUARY 17, 2025

No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically As a data engineer, ensuring data quality is both essential and overwhelming. The sheer volume of tables, the complexity of the data usage, and the volume of work make manual test writing an impossible task to get done.

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DataKitchen

DECEMBER 17, 2024

The Gartner presentation, How Can You Leverage Technologies to Solve Data Quality Challenges? by Melody Chien, underscores the critical role of data quality in modern business operations. High-quality data is the blood that sustains the organizational value chainimpacting everything from logistics to services, sales, and marketing. Poor data quality, on average, costs organizations $12.9 million annually , or 7% of their total revenue.

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DataKitchen

DECEMBER 9, 2024

A Drug Launch Case Study in the Amazing Efficiency of a Data Team Using DataOps How a Small Team Powered the Multi-Billion Dollar Acquisition of a Pharma Startup When launching a groundbreaking pharmaceutical product, the stakes and the rewards couldnt be higher. This blog dives into the remarkable journey of a data team that achieved unparalleled efficiency using DataOps principles and software that transformed their analytics and data teams into a hyper-efficient powerhouse.

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DataKitchen

DECEMBER 6, 2024

Would you like help maintaining high-quality data across every layer of your Medallion Architecture? Like an Olympic athlete training for the gold, your data needs a continuous, iterative process to maintain peak performance. We covered how Data Quality Testing, Observability, and Scorecards turn data quality into a dynamic process, helping you build accuracy, consistency, and trust at each layerBronze, Silver, and Gold.

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DataKitchen

NOVEMBER 27, 2024

A Guide to the Six Types of Data Quality Dashboards Poor-quality data can derail operations, misguide strategies, and erode the trust of both customers and stakeholders. Data quality dashboards have emerged as indispensable tools, offering a clear window into the health of their data and enabling targeted actionable improvements. However, not all data quality dashboards are created equal.

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DataKitchen

NOVEMBER 5, 2024

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. The Medallion architecture is a design pattern that helps data teams organize data processing and storage into three distinct layers, often called Bronze, Silver, and Gold.

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DataKitchen

OCTOBER 30, 2024

“That should take two hours, not two months. Can’t your Data & Analytics Team go any faster?” “The executives’ dashboard broke! The data’s wrong! Can I ever trust our data?” If you’ve ever heard (or had) these complaints about speed-to-insight or data reliability, you should watch our webinar, DataOps for Beginners, on demand. DataKitchen’s VP Gil Benghiat breaks down what DataOps is (spoiler: it’s not just DevOps for data) and how DataOps can take your Data & Analytics factory fro

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DataKitchen

OCTOBER 10, 2024

Read Our New White Paper: Data Quality The DataOps Way Data quality isn’t just a technical hurdle—it’s a strategic necessity in the data-driven world. Traditional methods fall short, but the DataOps approach to data quality offers a transformative path forward. It empowers individuals to act swiftly, enables continuous improvement, and fosters collaboration across organizational silos.

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DataKitchen

OCTOBER 8, 2024

Christopher Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 30 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of engineering. Enjoy the chat.

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DataKitchen

SEPTEMBER 18, 2024

A DataOps Approach to Data Quality The Growing Complexity of Data Quality Data quality issues are widespread, affecting organizations across industries, from manufacturing to healthcare and financial services. According to DataKitchen’s 2024 market research, conducted with over three dozen data quality leaders, the complexity of data quality problems stems from the diverse nature of data sources, the increasing scale of data, and the fragmented nature of data systems.

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DataKitchen

SEPTEMBER 9, 2024

From Cattle to Clarity: Visualizing Thousands of Data Pipelines with Violin Charts Most data teams work with a dozen or a hundred pipelines in production. What do you do when you have thousands of data pipelines in production? How do you understand what is happening to those pipelines? Is there a way that you can visualize what is happening in production quickly and easily?

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DataKitchen

AUGUST 22, 2024

Data Quality Circles: The Key to Elevating Data and Analytics Team Performance Introduction: The Pursuit of Quality in Data and Analytic Teams. According to a study by HFS Research, 75 percent of business executives do not have a high level of trust in their data. High-quality data underpins the reliability of insights, models’ accuracy, and decision-making processes’ efficacy.

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DataKitchen

AUGUST 20, 2024

DataOps, the promising future that nobody seems to be able to make reality. But not for lack of trying: meet Chris Bergh, "Head Chef" at DataKitchen, joining us again to tell us how te filed evolved over the last few years. To get in.

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DataKitchen

AUGUST 16, 2024

2024 Gartner Market Guide To DataOps We at DataKitchen are thrilled to see the publication of the Gartner Market Guide to DataOps, a milestone in the evolution of this critical software category. As the pioneer in the DataOps category, we are proud to have laid the groundwork for what has become an essential approach to managing data operations in today’s fast-paced business environment.

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DataKitchen

AUGUST 8, 2024

Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

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DataKitchen

AUGUST 6, 2024

Chris Bergh joins me to chat about all things DataOps. We also discuss lean, removing waste from data processes and teams, and much more.

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DataKitchen

AUGUST 6, 2024

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Chris Berg, CEO of DataKitchen, to discuss his ongoing mission to…

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DataKitchen

AUGUST 6, 2024

DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time) on data.boston.gov building permit data! Imagine a free tool that you can point at any dataset and find actionable data quality issues immediately! It sure beats having your data consumers tell you about problems they find when you are trying to enjoy your weekend.

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DataKitchen

JULY 8, 2024

Christopher Bergh, CEO of DataKitchen, is transforming data analytics with his DataOps approach. By applying principles from agile and lean manufacturing, Bergh aims to eliminate the 70-80% waste in data processes. DataKitchen's suite of open-source tools offers solutions for observability, testing, and automation, addresses challenges in rapid change management, error detection team productivity.

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DataKitchen

JUNE 21, 2024

Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You know you’re in trouble when the finance team uses your reports as modern art installations rather than decision-making tools.

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DataKitchen

MAY 14, 2024

Data Observability and Data Quality Testing Certification Series We are excited to invite you to a free four-part webinar series that will elevate your understanding and skills in Data Observation and Data Quality Testing. This series is crafted for professionals eager to deepen their knowledge and enhance their data management practices, whether you are a seasoned data engineer, a data quality manager, or just passionate about data.

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DataKitchen

MAY 10, 2024

Harnessing Data Observability Across Five Key Use Cases The ability to monitor, validate, and ensure data accuracy across its lifecycle is not just a luxury—it’s a necessity. Data observability extends beyond simple anomaly checking, offering deep insights into data health, dependencies, and the performance of data-intensive applications. This blog post introduces five critical use cases for data observability, each pivotal in maintaining the integrity and usability of data throughout its journe

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Sources (#1) Introduction to Data Evaluation in Data Observability Ensuring their quality and integrity before incorporating new data sources into production is paramount. Data evaluation serves as a safeguard, ensuring that only cleansed and reliable data makes its way into your systems, thus maintaining the overall health of your data ecosystem.

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. Data ingestion monitoring, a critical aspect of Data Observability, plays a pivotal role by providing continuous updates and ensuring high-quality data feeds into your systems.

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. This blog explores the third of five critical use cases for Data Observability and Quality Validation—data Production—highlighting how DataKitchen’s Open-Source Data Observability solutions empower organizations to manage this critical s

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DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Fast, Safe Development and Deployment (#4) Introduction The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This blog post delves into the third critical use case for Data Observation and Data Quality Validation: development and Deployment.

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DataKitchen

MAY 10, 2024

The post The Five Use Cases in Data Observability: Ensuring Accuracy in Data Migration first appeared on DataKitchen.

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DataKitchen

MAY 7, 2024

DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certificatio n (3 hours, online, free, signup online): DataOps Manifesto (over 30,000 signatures) One Day DataOps training (paid) Data Observability (the first step in DataOps) I deas and Principles of Data Observability Four-part Da

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DataKitchen

APRIL 18, 2024

Introducing DataKitchen’s Open Source Data Observability Software Today, we announce that we have open-sourced two complete, feature-rich products that solve the data observability problem: DataOps Observervability and DataOps TestGen. With these two products, you will know if your pipelines are running without error and on time and can finally trust your data.

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