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Use our expert insights to find out how you can gain total trust in your data

How to Identify Your First Data Products

Taking the first steps to getting started with Data Products

Mikkel Dengsøe

Benchmark Your Data Team

What data from hundreds of top data teams tells us about team size, role distribution, data-to-engineer ratios, and salaries.

Mikkel Dengsøe

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2/13/2025

Building our SQLMesh Integration

Bridging model and table-level concepts to make the best-in-class data observability and SQLMesh integration

Mikkel Dengsøe

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2/12/2025

5 real-world data product use cases

Examples of how data products are used to solve real-world problems and drive meaningful business impact in B2B SaaS, eCommerce, logistics, and fintech

Mikkel Dengsøe

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1/24/2025

SQLMesh and SYNQ integration

Combine SQLMesh DataOps workflows with SYNQ’s Data Product Observability layer to deliver reliable data products

Mikkel Dengsøe

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1/7/2025

SYNQ’s 2024 Highlights

Reflecting on 2024, SYNQ introduced the Data Product Observability Workflow, enhancing data reliability through strategic testing, ownership activation, and quality metrics. Key developments include the launch of Data Products for streamlined monitoring and SLA tracking, advanced incident management features, and comprehensive data quality analytics. These innovations have empowered enterprises like IAG and Aiven to optimize their data operations effectively.

Mikkel Dengsøe

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12/20/2024

Building Incident Management at SYNQ

Discover how Synq developed its Incident Management system tailored for data teams, integrating error detection, ownership, and lifecycle management to enhance data reliability.

Karan Gupta

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10/28/2024

Supercharging data quality: Using a data reliability platform alongside dbt

How and when to extend dbt with a data reliability platform, and key considerations for vendor selection

Mikkel Dengsøe

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10/21/2024

Learnings from running hundreds of data incidents at SYNQ

How treating issues and incidents differently has driven accountability and a trace of systemic issues

Mikkel Dengsøe

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8/26/2024

How top data teams are structured

A deep dive into the ratio of data roles across insight, data engineering, and machine learning

Mikkel Dengsøe

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7/26/2024

How data observability fits into the different stages in the data pipeline

Explore how data observability integrates into each stage of the data pipeline, from source systems to analytics, enhancing data quality and reliability.

Mikkel Dengsøe

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6/28/2024

High-impact data governance teams

How to drive impact, where to focus, and what skills are required to succeed in the best data governance teams

Mikkel Dengsøe

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5/30/2024

ML & Gen AI for data teams

Building reliability by design for classical ML use cases and Gen AI

Mikkel Dengsøe

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4/25/2024

Unpacking Synq’s approach to incident management for data teams

Explore Synq’s tailored approach to incident management for data teams, highlighting key stages like issue detection, triage, handling, and post-incident analysis.

Petr Janda

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3/26/2024

Why incidents – not issues – must be the basis of data reliability SLAs

Learn why tracking data incidents, not just issues, is key to reliable SLAs. Explore best practices for detection, triage, and postmortems.

Petr Janda

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3/25/2024

The cost of data incidents

A toolset for determining if a data incident is a $1,000 or $100,000 problem

Mikkel Dengsøe

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3/4/2024

How we built code-column lineage to reimagine the data practitioner’s experience

Building new software engineering-like workflows

Petr Janda

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2/23/2024

Why we’re taking column-level lineage to the next level

As column-level lineage becomes the standard, what is next?

Petr Janda

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2/22/2024

Data ownership: A practical guide

A toolkit for defining and activating ownership across the data team, upstream teams, and business stakeholders

Mikkel Dengsøe

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2/12/2024

Measuring data quality: bringing theory into practice

Practical steps for measuring and actioning data quality

Mikkel Dengsøe

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1/17/2024

LendInvest webinar

Learnings from bringing ownership of data quality across the business

Mikkel Dengsøe

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1/3/2024

2023 recap

A year of growth, learning, and shipping

Mikkel Dengsøe

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12/22/2023

Building reliable machine learning models in the data warehouse

Combining dbt tests and anomaly monitors to proactively detect data quality issues on sources, features, and predictions

Mikkel Dengsøe

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11/20/2023

Helping data teams improve reliability of their critical data

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

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11/8/2023

Building reliable data in fintech

Data reliability challenges fintech companies face with reconciliation, automated decisioning, regulation, and customer experience

Mikkel Dengsøe

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10/19/2023

Anomaly monitors and dbt tests to ensure the quality of business-critical pipelines

How to build reliable data pipelines by combining domain knowledge and automated data checks

Mikkel Dengsøe

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8/25/2023

The importance of data quality for automated customer engagement

How to improve the ROI of your marketing campaigns by reducing upstream data quality issues

Mikkel Dengsøe

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7/21/2023

The hidden cost of data quality issues on the return of ad spend

How leading companies drive value-driven ad spend allocation based on customer lifetime value and overcome costly data issues

Mikkel Dengsøe

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7/5/2023

How to identify your business-critical data

Practical steps to identifying business-critical data models and dashboards and drive confidence in your data

Mikkel Dengsøe

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6/11/2023

Activating ownership with data contracts in dbt

How to use data contracts, tests and Synq to enforce quality and activate ownership between producers, consumers and data teams

Mikkel Dengsøe

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6/4/2023

Untaming the spaghetti lineage

Why data lineage workflows break with scale and how to fix it

Mikkel Dengsøe

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5/11/2023

New capabilities to manage data reliability at scale

Discover how Synq’s self-learning testing engine enhances data reliability by dynamically detecting anomalies, reducing false positives, and integrating seamlessly with existing dbt tests.

Petr Janda

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5/9/2023

The struggles scaling data teams face

How asset sprawl in large data teams makes onboarding, development, monitoring and self-serve harder

Mikkel Dengsøe

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5/4/2023

Data and product to engineer ratio at 50 tech scaleups

What the number of data and product people relative to engineers can tell us about how top tech scaleups operate

Mikkel Dengsøe

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4/13/2023

Incident management for data teams

Practical steps for managing incidents in data teams for faster resolution, better transparency and less hassle

Mikkel Dengsøe

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4/6/2023

Data alerts are hard

The difficulty of building alerting workflows fit for scale

Mikkel Dengsøe

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3/20/2023

Analytics engineering workflows with dbt and Synq

Four improvements to your dbt workflows to build reliable data for scaling data teams

Mikkel Dengsøe

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3/1/2023

Europe data salary benchmark 2023

A pay benchmark for data analysts, data scientists, analytics engineers and data engineers across hundreds of companies

Mikkel Dengsøe

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2/16/2023

The complete guide to building reliable data with dbt tests

10 practical steps from top data teams

Mikkel Dengsøe

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2/7/2023

Designing severity levels for data issues

Things to consider when setting severity levels, how to act based on different severity levels and simple steps to getting started

Mikkel Dengsøe

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1/20/2023

Defining ownership and making it actionable

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

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1/15/2023

The unique challenges faced by fintech data teams

Fintech startups have multiple times the amount of critical metrics and data systems making the stakes higher than anywhere else.

Mikkel Dengsøe

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1/12/2023

Data team as % of workforce: A deep dive into 100 tech scaleups

What’s the right ratio of data roles in scaleups and why it should probably be higher than you think.

Mikkel Dengsøe

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1/10/2023

Build with data you can depend on

Join the data teams delivering business-critical impact with SYNQ.

Go deeper on your data

How to Identify Your First Data Products

Benchmark Your Data Team

Building our SQLMesh Integration

5 real-world data product use cases

SQLMesh and SYNQ integration

SYNQ’s 2024 Highlights

Building Incident Management at SYNQ

Supercharging data quality: Using a data reliability platform alongside dbt

Learnings from running hundreds of data incidents at SYNQ

How top data teams are structured

How data observability fits into the different stages in the data pipeline

High-impact data governance teams

ML & Gen AI for data teams

Unpacking Synq’s approach to incident management for data teams

Why incidents – not issues – must be the basis of data reliability SLAs

The cost of data incidents

How we built code-column lineage to reimagine the data practitioner’s experience

Why we’re taking column-level lineage to the next level

Data ownership: A practical guide

Measuring data quality: bringing theory into practice

LendInvest webinar

2023 recap

Building reliable machine learning models in the data warehouse

Helping data teams improve reliability of their critical data

Building reliable data in fintech

Anomaly monitors and dbt tests to ensure the quality of business-critical pipelines

The importance of data quality for automated customer engagement

The hidden cost of data quality issues on the return of ad spend

How to identify your business-critical data

Activating ownership with data contracts in dbt

Untaming the spaghetti lineage

New capabilities to manage data reliability at scale

The struggles scaling data teams face

Data and product to engineer ratio at 50 tech scaleups

Incident management for data teams

Data alerts are hard

Analytics engineering workflows with dbt and Synq

Europe data salary benchmark 2023

The complete guide to building reliable data with dbt tests

Designing severity levels for data issues

Defining ownership and making it actionable

The unique challenges faced by fintech data teams

Data team as % of workforce: A deep dive into 100 tech scaleups

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