Discussiing the nasty, brutsh and short tenure of a CDO, and how to make DataOps happen in your organization via bottoms up innovation.
Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact
Webinar: Unlocking the Power of Data Observability and Quality Testing
Podcast: Data Hurdles Poscast
In the fast-paced world of data analytics, where mountains of information are processed daily to derive valuable insights, one man is on a mission to revolutionize how teams work with data. Christopher Bergh, the CEO, Founder, and self-styled “Head Chef” of DataKitchen, is not your typical tech executive. With a background in software development and a passion for efficiency, Bergh has set out to tackle what he sees as the biggest problem in the data industry: waste.
How Three Small Pharma Companies Used DataKitchen to Achieve Commercial Launch Success and Skyrocket to $100 Billion in Acquisition Value
Three pharma companies recently made headlines by securing successful exits totaling $100 billion. What’s their common denominator? They all chose DataKitchen to power their pre-launch data operations. Here’s why DataKitchen stood out as their partner of choice.
Data Observability and Data Quality Testing Certification Series
Join Our Free Webinar Series: Unlocking the Power of Data Observability and Quality Testing
The Five Use Cases in Data Observability: Ensuring Accuracy in Data Migration
 The Five Use Cases in Data Observability: Accuracy in Data Migration  (#5) Data migration projects, such as moving from on-premises infrastructure to the cloud, are critical and complex projects that involve transferring data across different systems while...
The Five Use Cases in Data Observability: Fast, Safe Development and Deployment
The Five Use Cases in Data Observability: Fast, Safe Development & Deployment (#4) 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...
The Five Use Cases in Data Observability: Mastering Data Production
 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...
The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring
The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) 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...
The Five Use Cases in Data Observability: Data Quality in New Data Sources
The Five Use Cases in Data Observability: Data Quality in New Data Sources (#1) 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...
