Data quality

Latest Articles

What is AI-Ready Data?

Before we define AI-ready data, it’s important to understand the key requirements it needs to meet: 1. Be easy for a data scientist (or an LLM) to interpret and reason about. 2. Be easy to query and feature engineer with. (Data should not require lots of prep) 3. To be accurate (so that predictions on […]

Observability vs monitoring for data pipelines

Data teams don’t have it easy. The Modern Data Stack is evolving at pace, but with each addition comes spiraling complexity and responsibility. Staying on top of the number of failure points as well as maintaining organizational trust in data is the Mount Everest of jobs. Trudging on, like the badly equipped sherpas of the digital […]

A guide to data governance: What it is, and why your organization needs it

Delivering well-understood, accurate, and complete data is a challenging task for most organizations, and its difficulty increases as organizations grow. To combat this challenge, companies are investing an abundance of resources into data governance. A recent study from McKinsey & Company found that companies, on average, are investing between 2.5% and 7.5% of their IT […]

How to protect your data pipeline against the next cloud outage

Every year, businesses grow increasingly dependent on cloud infrastructure to power their data stack. This year, cloud services are already set to process an incredible 94% of workloads. This massive growth is not surprising. Cloud computing offers many advantages such as greater flexibility, increased speed and agility, lower latency, cost optimization and more. Yet although […]

Monitor and manage data quality in real time

Many customers and community members come to Snowplow specifically because they want accurate and complete data collection. One of our core features is being a loss-less data pipeline, which means all data can be accounted for, even if it doesn’t make it all the way through to a data storage environment. More specifically, events coming […]

How tracking issues undermine your data quality

Many factors determine how successfully you’re able to power real-time applications, improve the user experience and inform critical product decisions. However, the first step is to set the right foundation for these use cases, and others, by collecting the highest quality data. Getting this right depends on two critical elements; creating a robust tracking design, […]

The road to better data quality - your questions answered

Snowplow recently hosted a webinar where Yali Sassoon, Co-founder and CPO at Snowplow, and Christophe Bogaert, Co-founder at Tasman Analytics, addressed some of the challenges companies are facing around data quality with regards to behavioral data specifically, and what we can do to address these challenges. We recorded the webinar and have made it available […]

Avoid turning your data lakes into data swamps: Focus on data quality, not capture

Putting data to work and gathering meaningful insights from it is growing increasingly complex, in large part because there is a near-unfathomable amount of data flowing in from all kinds of sources. This will come as no surprise to anyone, regardless of the industry in which they work, because data has come to be seen […]

Monitoring Bad Rows on GCP Using BigQuery and Data Studio

One of the key features to Snowplow pipeline is that it’s architected to ensure data quality up front – rather than spending a lot of time cleaning and making sense of the data before using it, schemas are defined up front and used to validate all data types as they come through the pipeline. Another […]

How a clear data taxonomy drives insight and action

This is guest blog post by João Correia, Solutions Director at Igloo Analytics and an experienced analytics professional, helping organizations embed analytics for growth and innovation. In this post, João explains how to define an analytics strategy with Snowplow Analytics that considers your business context and drives insights and action. Many thanks to João for […]

Debugging bad data in Elasticsearch and Kibana - a guide

One of the features that makes Snowplow unique is that we actually report bad data: any data that hits the Snowplow pipeline and fails to be processed successfully. This is incredibly valuable, because it means you can: Spot data tracking issues that emerge, quickly, and address them at source Have a corresponding high degree of […]

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