Machine Learning Times
Machine Learning Times

6 months ago
How to Build and Maintain High Quality Data Without Raising Billions

Originally published in Anomalo, Dec 2, 2020.

Airbnb has always been a data driven company.

Back in 2015, they were laying the foundation to ensure that data science was democratized at Airbnb. Meanwhile, they have grown to more than 6,000 people and have raised more than $6b of venture funding.

To stay data driven through this massive change has required making big investments in data quality, as outlined by their recent Data Quality at Airbnb series: Part 1 — Rebuilding at Scale and Part 2 — A New Gold Standard.

Companies aspiring to be as data driven and successful as Airbnb will also need to prioritize data quality.

It does not matter how much data is collected, how fast it can be queried, how insightful analyses are or how intelligent a machine learning model is. If the underlying data is unreliable and of poor quality, then everything that depends upon it will also be suspect.

Fortunately, companies no longer need to reinvent the wheel or make massive investments to improve and maintain high quality data. New startups, such as ours at Anomalo, are building the technology needed to monitor, triage and root cause data quality issues efficiently at scale.

In their first post, Part 1 — Rebuilding at Scale, Airbnb set the following goals for themselves.

To continue reading this article, click here.

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