Organizations struggle to identify and classify data across file shares, cloud storage, and data lakes. Manual processes are slow, error-prone, and lead to wasted storage on redundant or obsolete data.

Automated Classification & Reduced Storage Costs with Zubin

Zubin takes the hassle out of data classification with its combined metadata analytics and content analytics. By automatically classifying your data based on type, sensitivity, and usage patterns, Zubin saves you valuable time and resources. This intelligent system identifies less frequently accessed data, allowing you to archive it to cost-effective cloud storage options like S3 or OneDrive. This frees up precious space on your high-performance storage for data that’s actively in use. Furthermore, Zubin utilizes statistical sampling to analyze representative subsets of your data, ensuring insightful reporting at all levels – from executives to individual users. This comprehensive approach empowers you to pinpoint and eliminate redundant or obsolete data, leading to significant reductions in storage expenses.

Traditional data governance can be complex and inaccessible to non-technical users, hindering data ownership and collaboration. Business users often lack the tools or knowledge to manage their data effectively.

Empower Data Owners To View, Control and Act on their Data with Zubin

Zubin empowers your business users to take control of their data with its self-service data classification tools. These user-friendly tools allow them to classify data based on specific departmental needs and real-world business context. This fosters a sense of data ownership and collaboration, breaking down traditional data silos. Furthermore, Zubin’s role-based access control (RBAC) extends all the way down to the data owner level, enabling granular control. Business users can define who can access their data and at what level (read, write, edit), ensuring their information remains secure. This empowers them to become active participants in data governance, making informed decisions about their data and its usage.

Data privacy regulations like GDPR and CCPA are becoming increasingly complex, and organizations struggle to comply. Identifying and managing sensitive data across hybrid cloud environments is a challenge.

Mitigating Data Privacy Risks and Ensuring Compliance with Zubin

Zubin’s content analytics powered by AI/ML and NLP automatically identifies and classifies sensitive data (like PII and PHI). But that’s not all. Data owners can take control with Zubin’s self-service data classification, allowing them to further categorize sensitive data based on their specific regulations. Streamline your discovery and risk management with Zubin’s seamless integration with cloud integrated risk controls (like Microsoft Information Protection). Additionally, Zubin’s data usage & traceability provides a centralized record of all data movement activities, making audit trails and compliance reporting a breeze. Finally, Zubin ensures data privacy and regulatory adherence by automating data lifecycle management tasks. Based on your retention policies, Zubin can automatically delete or anonymize data, giving you complete peace of mind.

Preparing data for AI and machine learning projects can be time-consuming and complex. Data scientists often struggle to find, access, and clean data from diverse sources across the organization.

Streamline Data Wrangling with Zubin

No more drowning in data from siloed sources. Zubin’s data aggregation and harmonization is your lifeline. Effortlessly consolidate data from file storage, object storage, and data lakes – no more time-consuming manual collection. Data owners and data scientists can work together seamlessly using self-service tools to define granular access permissions, keeping your data secure and compliant. Need to move data for specific projects? Zubin can initiate data migration and even transform it (file to object) for an even smoother workflow. Automated data cleansing workflows ensure pristine data quality by removing inconsistencies and errors that could sabotage your AI/ML models. This translates to streamlined data preparation, faster AI/ML project development, and finally, unlocking the true potential of your data for advanced analytics.