AI has a big data problem.
AI implementations require corporate data, yet companies struggle to find, cull, and deliver the most relevant data for AI from across the enterprise, while ensuring it is properly managed for governance, risk, and compliance. The ZL platform delivers several new capabilities that completely change the paradigm.
Curated
Searches and filters data from across the enterprise to deliver a feed of targeted, relevant data for GenAI and analytics.
Virtual
Provides a virtual data management layer to extract content and metadata from documents without creating copies.
Governed
Delivers governance, risk, and compliance functionality to provide defensibility for AI.
From out of sight to insight.
When surfaced, information created and shared by employees every single day has the power to reveal a side of the enterprise never yet seen. ZL Tech delivers a platform for harnessing unstructured data for AI and analytics, trusted by the Fortune 500 and beyond, including 4 of the 5 top US banks.
Unified governance, deep insights.
The ZL Unified Platform empowers visibility and control over information for legal, regulatory, and privacy requirements, while maximizing its value for AI and analytics. ZL Tech is unique in its ability to unify all governance functions under a single centralized platform, while harnessing enterprise data for deep insights.
New Insights
Explore data in near-real time and empower insights at 1000x speeds via in-place search.
Unified Platform
Deliver all governance functions in synergy via singular platform, enabling defensible AI.
Enterprise-Wide
Manage unstructured data at an enterprise scale. Most platforms operate on sandboxes—ZL manages the entire beach.
Frequently Asked Questions
Why is data management critical for enterprise AI projects?
Enterprise AI relies heavily on data quality. Without proper data management, organizations risk inaccurate insights, biased outputs, and compliance issues. Effective data governance ensures AI systems produce trusted and auditable results.
How can enterprises ensure AI compliance with regulations?
Organizations must implement these across all data sources:
- Data retention and deletion policies
- Audit trails and monitoring
- Data classification and access controls
- Privacy compliance frameworks (e.g., GDPR, CCPA)
How can enterprises prepare unstructured data for AI and analytics?
Enterprises can prepare unstructured data for AI and analytics by identifying high-value data sources, classifying sensitive and relevant content, reducing redundant or obsolete data, and establishing governed access. This creates stronger enterprise data foundations for AI and analytics.
What is the difference between a data lake, a data warehouse, and a data lakehouse?
A data warehouse is typically designed for structured, curated data used in reporting and analytics. A data lake is designed to store large volumes of raw structured, semi-structured, and unstructured data. A data lakehouse aims to combine the scale and flexibility of a data lake with the management and query capabilities associated with a warehouse.
How do you manage unstructured data for AI governance?
Organizations govern unstructured data for AI by discovering where high-value content resides, classifying sensitive and relevant information, applying retention, and creating governed access controls for approved AI use cases. The objective is to improve data quality, defensibility, and auditability before content enters AI workflows.
