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 Additionally, redundant and outdated data consumes storage resources.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.

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