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— Written by Mikkel Dengsøe in Articles
How treating issues and incidents differently has driven accountability and a trace of systemic issues
A deep dive into the ratio of data roles across insight, data engineering, and machine learning
Mikkel Dengsøe- 7/26/2024
Mikkel Dengsøe- 6/28/2024
How to drive impact, where to focus, and what skills are required to succeed in the best data governance teams
Mikkel Dengsøe- 5/30/2024
Building reliability by design for classical ML use cases and Gen AI
Mikkel Dengsøe- 4/25/2024
Petr Janda- 3/26/2024
Petr Janda- 3/25/2024
A toolset for determining if a data incident is a $1,000 or $100,000 problem
Mikkel Dengsøe- 3/4/2024
Building new software engineering-like workflows
Petr Janda- 2/23/2024
As column-level lineage becomes the standard, what is next?
Petr Janda- 2/22/2024
A toolkit for defining and activating ownership across the data team, upstream teams, and business stakeholders
Mikkel Dengsøe- 2/12/2024
Practical steps for measuring and actioning data quality
Mikkel Dengsøe- 1/17/2024
Learnings from bringing ownership of data quality across the business
Mikkel Dengsøe- 1/3/2024
A year of growth, learning, and shipping
Mikkel Dengsøe- 12/22/2023
Combining dbt tests and anomaly monitors to proactively detect data quality issues on sources, features, and predictions
Mikkel Dengsøe- 11/20/2023
Synq Data Products create greater clarity, foster a stronger sense of ownership, and enhance reliability for your business-critical data within scaling data ecosystems.
Petr Janda- 11/8/2023
Data reliability challenges fintech companies face with reconciliation, automated decisioning, regulation, and customer experience
Mikkel Dengsøe- 10/19/2023
How to build reliable data pipelines by combining domain knowledge and automated data checks
Mikkel Dengsøe- 8/25/2023
How to improve the ROI of your marketing campaigns by reducing upstream data quality issues
Mikkel Dengsøe- 7/21/2023
How leading companies drive value-driven ad spend allocation based on customer lifetime value and overcome costly data issues
Mikkel Dengsøe- 7/5/2023
Practical steps to identifying business-critical data models and dashboards and drive confidence in your data
Mikkel Dengsøe- 6/11/2023
How to use data contracts, tests and Synq to enforce quality and activate ownership between producers, consumers and data teams
Mikkel Dengsøe- 6/4/2023
Why data lineage workflows break with scale and how to fix it
Mikkel Dengsøe- 5/11/2023
Petr Janda- 5/9/2023
How asset sprawl in large data teams makes onboarding, development, monitoring and self-serve harder
Mikkel Dengsøe- 5/4/2023
What the number of data and product people relative to engineers can tell us about how top tech scaleups operate
Mikkel Dengsøe- 4/13/2023
Practical steps for managing incidents in data teams for faster resolution, better transparency and less hassle
Mikkel Dengsøe- 4/6/2023
The difficulty of building alerting workflows fit for scale
Mikkel Dengsøe- 3/20/2023
Four improvements to your dbt workflows to build reliable data for scaling data teams
Mikkel Dengsøe- 3/1/2023
A pay benchmark for data analysts, data scientists, analytics engineers and data engineers across hundreds of companies
Mikkel Dengsøe- 2/16/2023
10 practical steps from top data teams
Mikkel Dengsøe- 2/7/2023
Things to consider when setting severity levels, how to act based on different severity levels and simple steps to getting started
Mikkel Dengsøe- 1/20/2023
What to consider before defining ownership, how to manage ownership of upstream and downstream teams and a few simple steps to getting started
Mikkel Dengsøe- 1/15/2023
Fintech startups have multiple times the amount of critical metrics and data systems making the stakes higher than anywhere else.
Mikkel Dengsøe- 1/12/2023
What’s the right ratio of data roles in scaleups and why it should probably be higher than you think.
Mikkel Dengsøe- 1/10/2023