Protocols
Good data, always
Take control of your data quality with intuitive data cleaning features and real-time validation built for enterprise scale.
Create a shared data dictionary
Align all the teams in your company around a single data dictionary.
Diagnose data quality issues
Automate the QA process
Manually testing your tracking code is time consuming and doesn’t always catch every incorrect property or data type. With automatic Data Validation, you can audit your implementation in minutes.
Have confidence making decisions with clean data
Most companies detect issues after their team has used bad data to make decisions or trigger campaigns. Quickly take action on every invalid event with in-app reporting and daily email digests.
Trusted by startups and the
world's largest companies
Frequently asked questions
Data cleansing is the process of ensuring data is complete, accurate, and reliable so that it can be used for analysis, decision-making, and reporting. Data cleansing includes correcting outdated or missing information, applying standardized naming conventions to data entries for consistency, deduplicating events, recording data transformations for transparency, and more.
Without properly cleaning data, organizations run the risk of basing strategies and campaigns off inaccurate or misleading information. Duplicate data entries, incomplete fields, and inconsistent naming conventions are common culprits behind “bad data,” which can cost businesses millions of dollars each year in misguided insights and poor decision-making.
Getting started is easy
Start connecting your data with Twilio Segment.