Visibility Into Existing Unstructured Data Footprint Across All Data Constituents
In today’s data-driven world, organizations are drowning in a sea of information. Valuable insights often lie hidden within unstructured data formats like emails, documents, and social media interactions. However, the lack of visibility into this data hinders effective management, compliance, and strategic decision-making. Zubin, Data Dynamics’ AI-powered self-service data management software, empowers you to gain comprehensive visibility into your unstructured data footprint. Unlock the power of your data, from ensuring regulatory compliance and optimizing storage costs to driving innovation and AI enablement.
1Data Discovery and Governance
Data Discovery and Governance
Businesses struggle to identify and classify sensitive data (PII, PHI) across vast unstructured data stores, hindering compliance efforts (e.g., GDPR, CCPA). Additionally, redundant and outdated data consumes storage resources.
Compliance & Cost Optimization with Zubin
Zubin’s metadata analytics engine, coupled with AI-powered content analytics, delves deep to uncover hidden sensitive data with high accuracy. automated classification ensures data is properly labeled and secured, streamlining compliance with regulations like GDPR and CCPA. By identifying and eliminating redundant data, Zubin optimizes storage costs and improves overall data management efficiency.
2Risk Identificationand Mitigation
Risk Identification and Mitigation
Unidentified sensitive data in unstructured formats exposes organizations to security risks like breaches and unauthorized access. Traditional security solutions often struggle with unstructured data.
Data Security & Breach Prevention with Zubin
Zubin’s risk exposure insights proactively identifies potential data security risks through advanced content analytics and classification. High-risk data can be quarantined and isolated, reducing the attack surface. Granular access controls (RBAC) down to the data owner level further strengthen security measures.
3Data Lineageand Traceability
Data Lineage and Traceability
Lack of understanding about data origin, transformations, and usage hinders data quality and trust. This can lead to erroneous insights and downstream analytics issues.
Data Quality & Trustworthiness with Zubin
Zubin’s data usage & traceability provides a centralized data index, enabling comprehensive data lineage tracking. data observability and root cause analysis leverage machine learning to monitor data pipelines and identify issues affecting data quality, ensuring data integrity and building trust.
4Data Democratizationand Self-Service
Data Democratization and Self-Service
Data silos and limited access hinder collaboration and data-driven decision-making across departments.
Improved Data Accessibility & Collaboration with Zubin
Zubin’s self-service data classifications and migrations empower data owners to manage their data effectively. Secure data sharing capabilities facilitate collaboration across departments while maintaining robust access controls. This fosters a data-driven culture where informed decisions are made at all levels.
5Data Lifecycle Management
Data Lifecycle Management
retention complianceUnstructured data sprawl leads to difficulties in managing data lifecycle, including retention and disposal. This can result in non-compliance with regulations and unnecessary storage costs.
Retention & Cost Optimization with Zubin
Zubin’s Retention Compliance automates the identification of data nearing expiration, ensuring adherence to retention policies and regulations. data redundancy eliminates unnecessary data through deduplication and deletion, optimizing storage utilization.
6Data Optimizationfor AI/ML Projects
Data Optimization for AI/ML Projects
Poor data quality and lack of data preparation hinder the performance of AI/ML models.
Improved AI Model Performance with Zubin
Zubin’s data wrangling and curation for AI empowers data scientists and analysts to easily prepare data for AI and machine learning projects. It streamlines data preparation, ensuring high-quality data for AI/ML models. This leads to improved model accuracy, reliability, and overall AI-driven insights.
A Closer Look at What Makes Us Stand Out
Features
Enterprise Risk Posture and Infra Usage Business Intelligence and Reporting
Executive dashboards and reports providing risk and data usage analysis across the enterprise, business units, LOBs, and geographies, complemented by action plans and real-time status updates.
Data Risk and Usage Reporting and Actionability by Team and Data Ownership
Metadata Analytics
Content Analytics
Data Classification
Statistical Sampling
Risk Exposure Insights
Data Containment and Isolation
Quarantining of high-risk sensitive data in secure storage, such as file shares on storage resources or object storage buckets, enhancing data security (lock).
Access Control and File Re-permissioning
Data Policy Creation and Workflow
Personalized data workflows
based on corporate governance
and compliance policies.
Data Sharing
Cloud Integrated Risk Controls
Retention Compliance
Data Redundancy
Self-Service Data Classifications and Migrations
Policy-driven automated data movement, including migration, replication, transform & sync, and archiving across various softwares, both on-premises and in the cloud - SMB, NFS, AWS S3 and OneDrive (File to File, File to Object, Object to Object).
Data Archival
File preservation by converting files into object format by archiving while enabling efficient retrieval via software or RESTful API, audit, and legal discovery, and allowing S3/NFS/SMB-compliant access for compatibility and ease of use.
Data Transformation
Facilitates archival, retrieval, and bidirectional conversion (file to object and object to file) with data quarantine capabilities (transform related).
Data Tiering and Placement
Your Data, Your Rules.
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Zubin in Action
Use cases
Visibility Into Existing Unstructured Data Footprint Across All Data Constituents
Risk Identification of Data Exposure With Actionable Resolution
Privacy Management With Intelligent Data Classification for AI Models
Ensuring Data
Sovereignty Across
Global Data Footprint
Data Owner
Empowerment To View
and Act On Their Data
Storage Optimization
and LCM Across Hybrid
Cloud Infrastructure
Self-Service
Data Classifications
and Migrations
Policy-Based Transfer
of Data Ownership To
Avoid ‘Orphan’ Data
Data Dynamics in the Spotlight
News, events, and articles
Your Guide to What’s Next
Insights, Strategies, and Trends to Keep You in the Know
IDC Spotlight Paper – Rethinking Data Security: Improving Privacy and Compliance with a Shared Approach
From the Heart of the CEO: Zubin, Leading the Future of AI-Driven Self-Service Data Management
IDC Spotlight Paper – Rethinking Data Security: Improving Privacy and Compliance with a Shared Approach
From the Heart of the CEO: Zubin, Leading the Future of AI-Driven Self-Service Data Management
Empowering Data Control and Democratization in the Age of AI
Data Quality: A Persistent and Dynamic, Ever-Evolving Challenge, Not Just a One-Time Fix
Navigating the Ethical Landscape of AI in the Modern Age
Empowering Data Control and Democratization in the Age of AI
Data Quality: A Persistent and Dynamic, Ever-Evolving Challenge, Not Just a One-Time Fix
Navigating the Ethical Landscape of AI in the Modern Age
Do More with Zubin
Related Capabilities