What is AI Data Management? Discover, Clean, & Secure Data with AI
AI Summary
What is AI Data Management?
- Data Management Life Cycle Stages:
- Collection Stage
- Data Cleaning Stage
- Data Analysis Stage
- Data Governance Stage
- AI technologies enhance automation and streamlining of these stages to ensure data is accurate, accessible, and secure.
Importance of AI Data Management
- 64% of organizations manage over one petabyte of data, often spread across multiple systems.
- Shadow Data: Unmanaged data that organizations may not be aware of affects data utilization (estimated that 68% of data goes unanalyzed).
Four Ways AI Data Management Helps:
- Data Discovery:
- Automates discovery of data across internal databases, cloud services, and IoT sensors, addressing issues of distributed data silos.
- Smart Classification: Machine learning aids in classifying data by content.
- Natural Language Processing (NLP): Processes unstructured text into structured records.
- Data Quality:
- Basic automated data cleansing operations enhance reliability.
- Synthetic Data Generation: Fills in missing values with plausible estimates.
- Anomaly Detection: AI profiles data and detects irregularities to maintain quality control.
- Data Accessibility:
- AI-powered tools streamline data integration across sources, enhancing user access.
- Natural Language Queries: Users can interact with data using plain language instead of coding.
- Adaptive Access Controls: Changes access rules based on user patterns.
- Data Security:
- AI complements traditional data loss prevention tools by detecting sensitive information and potential threats.
- User and Entity Behavior Analytics (UEBA): Monitors typical access patterns to flag irregularities.
- Fraud Detection: Real-time analysis of transactions to spot fraud patterns.
- Conclusion: AI data management improves discoverability, quality, accessibility, and security of data, leading to informed insights and decision-making.