AnalyticsCreator Features

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Analyzes data to find and recommend the highest value customer segmentations.

Tools to create and adjust workflows to ensure consistency.

Presents information and analytics in a digestible, intuitive, and visually appealing way.

Software is able to alert the user in case of any errors.

Creates new or streamlines existing data models to ensure business continuity.

Supports compliance with PII, GDPR, HIPPA, PCI, and other regulatory standards.

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Facilitates the disseminating of collected big data throughout parallel computing clusters

Compile data from across all systems so that users can view relevant information easily.

View ETL process data via reports and visualizations like charts and graphs.

Record ETL historical data for auditing and potential data correction needs.

Provides a built-in integrated data catalog that allows users to easily locate data across multiple sources.

Visualize data flows and lineage that demonstrates compliance with reports and dashboards through a single console.

Provides an automated data lineage functionality which provides visibility over the entire data movement journey from data origination to destination.

Extract data from the designated source(s) like relational databases, JSON files, and XML files.

Cleanse and re-format extracted data to the needed target format.

Load reformatted data into target database, data warehouse, or other storage location.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).

Capable of scaling processing power up or down based on ETL volume.

Automatic creation and management of documents.

The software supports a wide variety of data sources and data warehouses that require automation.

Supports a template system, making it easier to set up configuration parameters.

Authenticates and authorizes individuals to access the data they are allowed to see and use.

Helps identify and manage the roles of owners and stewards of data.

Defines, validates, and monitors business rules to safeguard master data readiness.

Automates all parts of the Data Warehouse lifecycle - warehouse design, provision data sources.

Supports real-time data ingestions and updates in the cloud or on-premises.

Software delivers insights into data in order to identify gaps, risk within the data warehouse.

Supports no-code/low code development patterns.

Works across multiple software systems or databases

Anticipates needs and offers suggestions without prompting

Supports deployment across multiple environments or cloud platforms with consistent configuration management

Provides APIs and SDKs for seamless integration with orchestration, governance, and custom data tools

Allows querying and joining data across multiple warehouses and lakes without requiring data replication

Automatically adapts to schema or metadata changes in connected data sources while maintaining consistency

Enables users to create derived or calculated metrics based on governed data definitions

Provides built-in support for time-based calculations such as YoY, MoM, and rolling averages

Provides visibility and control over how AI systems or automated agents interact with the semantic layer

Tracks how governed metrics and datasets are used in AI/ML training to ensure transparency, compliance, and clear data traceability

Provides version control for semantic models and metrics, with change tracking, rollback, and impact analysis to see how updates affect downstream data or reports

Enables AI and machine learning systems to directly consume standardized, governed metrics and logic