Blog
Webinar: A New, More Effective Approach To Data Quality Assessments
We introduce a new, more effective approach to data quality assessments, enabled by DataKitchen’s free open-source software
Why Data Quality Dimensions Fall Flat: Data Quality Coffee With Uncle Chip #2
In this playful yet pointed talk, Uncle Chip kicks things off by poking fun at the overcomplicated world of data quality dimensions that are rooted in an outdated era of static data, keeping teams locked in abstraction and inaction.
Introducing The Five Pillars Of Data Journeys
Introducing The Five Pillars Of Data Journeys “There are those who discover they can leave behind destructive reactions and become patient as the earth, unmoved by fires of anger or fear, unshaken as a pillar, unperturbed as a clear and quiet pool.” – Gautama Buddha...
Why the Data Journey Manifesto?
Why the Data Journey Manifesto? So why another manifesto in the world? Really? Why should I care? About seven years ago, we wrote the DataOps Manifesto. We wrote the first version because, after talking with hundreds of people at the 2016 Strata Hadoop World...
UPCOMING WEBINAR: Automated Test Generation – Why Data Teams Need It
Webinar Summary: Data Mesh and Data Products
Webinar Summary: DataOps and Data Mesh Chris Bergh, CEO of DataKitchen, delivered a webinar on two themes - Data Products and Data Mesh. Bergh started by discussing the complexity within data and analytics teams, stating that complexity makes everything more...
Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms
Webinar Summary: Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms In the webinar "Driving Data Analytic Team Excellence Through Agility, Efficiency, and Aphorisms," James Royster, Vice President of Commercial Operations, Insights, and...
Two Downs Make Two Ups: The Only Success Metrics That Matter For Your Data & Analytics Team
Introduction. How to measure your data analytics team? So it’s Monday, and you lead a data analytics team of perhaps 30 people. You’ve got a new boss. And she is numbers driven – great! But wait, she asks you for your team metrics. Like most leaders of data...
Only One Problem To Solve for Successful Data and Analytics
The real problem in data analytics is that teams need to deliver insight to their customers without error put new ideas into production rapidly minimize their ‘insight manufacturing’ expenses ... all at the same time As former leaders of data teams, we...
A summary of Gartner’s recent DataOps-driven data engineering best practices article
On 24 January 2023, Gartner released the article “5 Ways to Enhance Your Data Engineering Practices.” By Robert Thanaraj, Ehtisham Zaidi, and 2 more. Gartner suggests in the article that successful Data Engineering teams have two crucial challenges. How to...
Question: What is the difference between Data Quality and DataOps Observability?
Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. Data Observability is dynamic -- it is the testing of data, integrated data, and tools acting upon data...
Why I Chose DataKitchen for DataOps
Why choose DataKitchen? During my nearly seven-year tenure leading an analytics function at Celgene, our partnership with DataKitchen was a critical component of my team’s data and analytics strategy. DataKitchen preaches the message of DataOps, a philosophy they...
A Data Prediction for 2025
We’ve read many predictions for 2023 in the data field: they cover excellent topics like data mesh, observability, governance, lakehouses, LLMs, etc. Here at DataKitchen, we wanted to take a different approach: look at a three-year horizon. What will the world of...
“You Complete Me,” said Data Lineage to Data Journeys.
What is data lineage? Data lineage traces data's origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. Data lineage...
