Anomalo Features

Monitors database functionality to verify baselines are maintained or exceeded.

Sends alerts via email, text, phone, and more when an incident or issue occcurs.

Captures logs for all database functions to garner greater information around issues or failures.

Monitors database query time for unusual execution times

Manually and/or automatically generates reports covering database performance

Follows database monitoring live information through graphical dashboards

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Clean data as it enters the data source to prevent mixing bad data with cleaned data.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Generate real-time depth analytics utilizing event metrics, logging and metadata.

Use custom or pre-built tests for buisness rules. to ensure data quality.

Involves automation capabilities to identify and track issues, failed operations by looking at historical trends.

Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility.

Provide follow-up information after data cleanings through a visual dashboard or reports.

Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks.

Identify the different type of anomalies and receive alerts.

The data observability environment can be viewed from a single dashboard.

Provides immediate alerts for any anomalies or expected events.

Establishes lineage for the data pipeline - from data warehouse to the data user.

Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts.

Allows users to generate text based on a text prompt.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Connects structured and unstructured data for unified analysis.

Uses AI to clean, join & prep data with little manual input

Allows users set goals & rules for agent behavior.

Improves agents via outcome learning & feedback.

Detects trends, anomalies & patterns without manual queries.

Breaks down complex analysis into logical steps.

Generates forecasts & action suggestions from data signals

Helps users ask questions in natural language & get answers.

Triggers workflows or actions based on agent analysis.

Shows reasoning paths to explain how insights were made