Atlan Features

Data Governance (3)

Search: Offers simple search capabilities to discover specific data sets
Data Quality and Cleansing: Allows users and administrators to easily clean data to maintain quality and integrity
Data Transformation: Converts data formats of source data into the format required for the reporting system without mistakes
Data Modeling: Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Commenting: Allows users to comment on data sets to help future users better interact and interpret the data
Profiling and Classification: Permits profiling of data sets for increased organization, both by users and machine learning
Business and Data Glossary: Creates a business glossary for faster understanding by the average business user
Metadata Management: Indexes metadata descriptions for easier searching and enhanced insights

Data Modelling: Tools to (re)structure data in a manner that allows extracting insights quickly and accurately
Recommendations: Analyzes data to find and recommend the highest value customer segmentations.
Workflow Management: Tools to create and adjust workflows to ensure consistency.
Dashboards and Visualizations: Presents information and analytics in a digestible, intuitive, and visually appealing way.

Identification: Correctly identify inaccurate, incomplete, or duplicated data from a data source.
Correction: Utilize deletion, modification, appending, merging, or other methods to correct bad data.
Normalization: Standardize data formatting for uniformity and easier data usage.
Preventative Cleaning: Clean data as it enters the data source to prevent mixing bad data with cleaned data.
Data Matching: Finds duplicates using the fuzzy logic technology or an advance search feature.

Reporting: Provide follow-up information after data cleanings through a visual dashboard or reports.
Automation: Automatically run data identification, correction, and normalization on data sources.
Quality Audits: Schedule automated audits to identify data anomalies over time based on set business rules.
Dashboard: Gives a view of the entire data quality management ecosystem.
Governance: Allows user role-based access and actions to authorization for specific tasks.
Business Glossary: Lets users build a glossary of business terms, vocabulary and definitions across multiple tools.
Data Discovery: Provides a built-in integrated data catalog that allows users to easily locate data across multiple sources.
Data Profililng: Monitors and cleanses data with the help of business rules and analytical algorithms.
Reporting and Visualization: Visualize data flows and lineage that demonstrates compliance with reports and dashboards through a single console.
Data Lineage: Provides an automated data lineage functionality which provides visibility over the entire data movement journey from data origination to destination.

Data Integration: Consolidate data from various disparate sources in a single unified view
Data Discovery: Understand the state of data, applications, systems and services
Multi - Platform: Manage data across environments (on-premises cloud, hybrid, and multi-cloud)
Metadata: Provides metadata management and lineage capabilities
Data Integration: Integrates data and data-related technologies into a single environment.
Metadata: Provides metadata management capabilities.
Self-service: Empowers the user via a self-service capability to manage data workflows.
Automated workflows: Completely automates end-to-end data workflows across the data integration lifecycle.

Compliance: Rules and regulations inherited from source systems or defined to secure sensitive data
Governance: Grant or restrict data access and control
Data Protection: Built - In Backup and Disaster Recovery
Access Control: Authenticates and authorizes individuals to access the data they are allowed to see and use.
Roles Management: Helps identify and manage the roles of owners and stewards of data.
Compliance Management: Helps adhere to data privacy regulations and norms.

Metadata Management: Indexes metadata descriptions for enhanced insights.
Automation Features: Ensures automation of metadata across the organization. Dynamically improves data management processes.
Collaboration: Embedded collaboration using APIs and in-app integrations.
Data Lineage: Provides insights into original data sources and transformations made to data sets from source to consumption of data.
Data Discovery: Provides an interface to collect and evaluate data to identfy patterns and outliers.

AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.
AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.
AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.
AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.
AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.
AI Text Generation: Allows users to generate text based on a text prompt.
AI Text Summarization: Condenses long documents or text into a brief summary.

Autonomous Task Execution: Capability to perform complex tasks without constant human input
Multi-step Planning: Ability to break down and plan multi-step processes
Cross-system Integration: Works across multiple software systems or databases
Adaptive Learning: Improves performance based on feedback and experience
Natural Language Interaction: Engages in human-like conversation for task delegation
Decision Making: Makes informed choices based on available data and objectives