Atlas accelerates your AI roadmap by helping you identify, prioritize and address data issues so that you can start training your AI systems faster. Atlas also helps you uncover 60+ different types of PII data.Request a demo
Data scientists spend between 40-60% of their time on data wrangling and cleaning 1, 2 and less than 20% of their time selecting and tuning AI models 1. Yet, most data scientists find data cleaning the least enjoyable part of their job.
1. 2022 State of Data Science. Anaconda. https://www.anaconda.com/resources/whitepapers/state-of-data-science-report-2022
2. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task. Forbes. https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=7ac1f2c66f63
Validate your data for accuracy, cleanliness, completeness and more to help identify potential issues and blockers for AI training.
Locate and automatically classify the PII (Personal Identifiable Information) in your data stores. Assess the collective sensitivity of the PII data within your organization.
Understand how your data stacks up in terms of data complexity to better plan for talent and other resources working with your data.
Pinpoint exact data issues and identify their impact. Actionable recommendations and instructions to address issues at your fingertips.
Automatically identify links and relationships between wide ranging datasets to assess data consistency issues between your different systems.
Make data, system and organizational consolidation and migration a breeze with a complete ontology of all your data.
Atlas can automatically recognize 60+ different types of PII (Personal Identifiable Information) including medical data.
We eliminate the work of having to manually set column types so your data science team can focus on higher value tasks like scaling your AI operations.
Identify 50+ different data integrity issues instantly without having to set manual rules.
Atlas assesses the validity and accuracy of your data within the context of its data type.