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BigID

by BigID · Since 2016
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ActiveAvailable globallyCloud
Quick facts
VendorBigID
Year launched2016
StatusActive
LocationNew York, New York 10011, US
Countries servedGlobal
Languages5
Integrations
Free tier
Free trial
Contact salesYES

About BigID

BigID is a data intelligence software from BigID that focuses on data discovery and privacy management. It combines advanced data mapping, risk assessment, and automated compliance reporting so organizations can better manage sensitive data and adhere to regulatory requirements. BigID helps businesses identify where personal data resides, classify it, and monitor it for potential risks. The platform also supports GDPR, CCPA, and other data protection laws, ensuring organizations remain compliant. With its scalable architecture, BigID can handle large volumes of data across various environments. Key capabilities: data discovery risk assessment compliance reporting data classification regulatory monitoring Best for: organizations that need to manage sensitive data and ensure compliance with data protection regulations.

BigID by BigID is a sophisticated data intelligence platform designed to help organizations manage their sensitive data efficiently. It enables automated data discovery, classification, and governance, making it a critical tool for organizations dealing with large amounts of data. The platform provides robust capabilities for mapping sensitive data across multiple environments, including cloud, on-premise, and SaaS applications. With compliance regulations such as GDPR and CCPA becoming increasingly stringent, BigID’s privacy compliance tools, remediation workflows, and data lineage mapping offer businesses a reliable solution for maintaining regulatory adherence and mitigating data risks. The platform’s user interface is modern and functional, but its extensive feature set may be overwhelming for new users. While the dashboard presents valuable insights and the navigation is logical, the sheer number of options can pose a learning curve. BigID could enhance its usability by incorporating more contextual help and guided workflows, especially for users without deep technical expertise. The search functionality is a standout, allowing users to locate specific data elements and reports efficiently, which is crucial for organizations managing vast data ecosystems.

Pros & Cons

What users like
  • +• Comprehensive Data Discovery and Classification: BigID excels at finding and categorizing sensitive data across diverse environments, a crucial foundation for data security and privacy.
  • +• AI-Powered Automation: Leverages machine learning to automate key processes like data discovery, classification, and privacy compliance tasks, saving time and resources.
  • +• Unified Platform: Offers a single platform for data security, privacy, and governance, simplifying management and reducing the need for multiple tools.
  • +• Scalability: Designed to handle massive data volumes (petabyte scale), making it suitable for large enterprises.
  • +• Cloud-Native and Flexible Deployment: Can be deployed in the cloud, on-premises, or in hybrid environments, offering flexibility and adaptability.
  • +• Strong Security Features: Includes enterprise-grade security features like role-based access control (RBAC), two-factor authentication (2FA), and secure cloud deployment.
  • +• Open Ecosystem and Integrations: Integrates with existing tech stacks and other tools through APIs and partnerships, enhancing interoperability.
  • +• Modular Approach: Allows users to select and deploy only the modules they need, optimizing costs and resource utilization.
  • +• Focus on Key Use Cases: Addresses critical use cases like data security posture management (DSPM), data privacy management (DPM), compliance, and cloud migration.
  • +• Addresses Emerging Needs: Provides solutions for AI security and data governance in the age of generative AI.
  • +• Positive Industry Recognition: Garnered positive reviews and recognition from industry analysts and users.
What users flag
  • • Complexity: A comprehensive platform like BigID can be complex to implement and manage, requiring specialized expertise.
  • • Integration Challenges: While BigID aims for seamless integration, there might still be challenges integrating with certain legacy systems or niche tools.
  • • Learning Curve: Users may require training and time to become proficient with the platform's features and functionalities.
  • • Over-Reliance on Automation: While automation is a strength, it's essential to ensure human oversight and validation, especially for critical data-related decisions.
  • • Vendor Lock-in: Adopting a platform like BigID could lead to vendor lock-in, making it difficult to switch to alternative solutions in the future.
  • • Performance at Scale: While designed for scale, real-world performance with extremely large datasets and complex environments needs to be validated. This is a general con for many enterprise systems.
  • • Limited Public Pricing Information: The website doesn't provide detailed pricing, requiring potential customers to contact sales, which can be a barrier for initial evaluation.
  • • Dependence on Machine Learning Accuracy: The effectiveness of some features relies on the accuracy of the machine learning models. While they claim advanced ML, real-world performance and fine-tuning are always factors.

Features

Key features

• Data Discovery & Classification
Automatically finds and categorizes sensitive data across various sources.
• AI Security
Secures data used in AI initiatives, addressing risks and ensuring compliance.
• Data Security Posture Management (DSPM)
Identifies and remediates data security risks, including dark data.
• Data Privacy Management (DPM)
Automates privacy compliance tasks and supports privacy-by-design.
• Data Management
Provides comprehensive data lifecycle management, from collection to deletion.
• Compliance
Helps organizations meet data regulations and security frameworks.
• Zero Trust
Enhances security by implementing least privilege access and reducing insider risk.
• Cloud DLP
Identifies and labels sensitive data in cloud environments, enforcing policies.
• Cloud Native Data Visibility & Control
Offers visibility and control over data in hybrid and multi-cloud environments.
• Data Minimization
Reduces data footprint by eliminating redundant, obsolete, and trivial (ROT) data.

Additional features

• Data Rights Automation
Automates the fulfillment of data subject requests (e.g., access, deletion).
• Insider Risk Management
Monitors and mitigates internal and external access risks.
• Access Intelligence
Provides insights into data access patterns and potential risks.
• Automated Labeling
Automatically assigns labels to data based on sensitivity and other criteria.
• Data Deletion
Securely deletes data according to policies and regulations.
• Data Remediation
Corrects data quality issues and enforces data policies.
• Data Retention
Manages data retention policies and schedules.
• Metadata Exchange & Enrichment
Enhances data metadata for better understanding and governance.
• Privacy Portal
Provides a centralized platform for managing privacy-related activities.
• PIA Automation
Automates Privacy Impact Assessments.
• Scalability
Designed to handle petabyte-scale data volumes.
• Machine Learning
Uses advanced ML for accurate data discovery and classification.
• Cloud Native
Can be deployed in the cloud, on-premises, or in hybrid environments.
• Enterprise Security
Includes robust security features like RBAC and 2FA.
• Modular Extensibility
Allows users to choose and add modules as needed.
• Open Ecosystem
Integrates with existing tech stacks through APIs and partner apps.

Pricing

Free trial
Free version
Request a quote
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Countries & Languages

Global
Countries served
5
Interface languages
8
Billing currencies

Interface languages

EnglishPortugueseGermanFrenchSpanish

Billing currencies

🇺🇸USD🇪🇺EUR🇬🇧GBP🇦🇺AUD🇨🇦CAD🇯🇵JPY🇨🇳CNY🇮🇳INR

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