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What is Mage Sensitive Data Discovery?

The Mage Sensitive Data Discovery module is designed to reveal concealed data locations within your organization. It enables the detection of hidden information across various data stores, including structured, unstructured, and Big Data environments. Utilizing Natural Language Processing and Artificial Intelligence, this tool is capable of locating data in even the most challenging scenarios. Its patented discovery method guarantees effective identification of sensitive data while keeping false positives to a minimum. You can enhance your data classifications with over 70 existing categories that encompass all widely recognized PII and PHI data types. Furthermore, the module streamlines the discovery process, allowing you to schedule sample scans, complete scans, and incremental scans at your convenience. This versatility ensures that your organization can maintain robust data security measures while efficiently managing data discovery tasks.

What is AutoDiscovery?

AutoDiscovery is a sophisticated software solution crafted for automated exploratory data analysis, enabling biomedical researchers to uncover complex relationships hidden within their experimental and clinical trial data. This innovative tool autonomously identifies the appropriate statistical tests required to assess the connections between different variable combinations in each data subset. AutoDiscovery effectively tackles prevalent challenges in biomedical research, such as discerning cause-effect relationships, managing false discovery rates, addressing small sample sizes, organizing treatment groups, and ensuring result traceability. Specifically designed for Principal Investigators, who often face time constraints and may lack extensive statistical knowledge, it allows them to focus on conducting significant and productive research endeavors. Additionally, this software streamlines research workflows, promoting faster insights and breakthroughs in the biomedical domain while empowering researchers to make data-driven decisions with confidence. By leveraging AutoDiscovery, researchers can enhance the overall quality of their findings and contribute more effectively to advancements in health science.

Media

Media

Integrations Supported

Amazon DynamoDB
Amazon S3
Apache HBase
Azure Databricks
Cloudera
Google Cloud BigQuery
Google Cloud Build
Google Drive
Hemi HL7
Hive
Magento
Microsoft Dynamics 365
Microsoft SharePoint
Oracle Cloud Infrastructure
PostgreSQL
SAP HANA
Salesforce
ServiceNow
Teradata QueryGrid
Workday Extend

Integrations Supported

Amazon DynamoDB
Amazon S3
Apache HBase
Azure Databricks
Cloudera
Google Cloud BigQuery
Google Cloud Build
Google Drive
Hemi HL7
Hive
Magento
Microsoft Dynamics 365
Microsoft SharePoint
Oracle Cloud Infrastructure
PostgreSQL
SAP HANA
Salesforce
ServiceNow
Teradata QueryGrid
Workday Extend

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

€1.795 per year
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Mage Data

Date Founded

2014

Company Location

United States

Company Website

magedata.ai/sensitive-data-discovery-catalog/

Company Facts

Organization Name

Butler Scientifics

Date Founded

2014

Company Location

Spain

Company Website

www.butlerscientifics.com

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

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