Overview
What is Anomalo?
To address data quality and other data issues, Anomalo automatically detects data issues as soon as they appear in an organization's data, and before anyone else is impacted. It is used to detect, root-cause, and resolve issues.
Pricing
What is Anomalo?
To address data quality and other data issues, Anomalo automatically detects data issues as soon as they appear in an organization's data, and before anyone else is impacted. It is used to detect, root-cause, and resolve issues.
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Would you like us to let the vendor know that you want pricing?
5 people also want pricing
Alternatives Pricing
What is HubSpot Operations Hub?
HubSpot Operations Hub (based on technology from the former PieSync that was acquired by HubSpot November 2019) is a two-way contacts integration platform. The vendor emphasizes quick and easy setup automated customer and client data matching and updating between apps.
What is Clear Analytics?
Clear Analytics is a business intelligence solution that enables non technical end users to perform analytics by leveraging existing knowledge of Excel coupled with a built in query builder. Some key features include: Dynamic Data Refresh, Data Share and In-Excel Collaboration.
Product Details
- About
- Integrations
- Tech Details
What is Anomalo?
Anomalo is connected to Enterprise Data Warehouse and begins monitoring tables chosen by the user. Its machine learning functionality will automatically learn the historical structure and patterns of the organization's data, allowing Anomalo to alert users to issues without the need to create rules or set thresholds. It can also be fine-tuned and its monitoring directed in a couple of clicks via Anomalo’s No Code UI.
Anomalo’s alerts include visualizations and statistical summaries of what’s happening to help users to more quickly understand the magnitude and implications of the problem displayed. Its Automated Root Cause Analysis will also try to find the root cause of the problem, potentially isolating the issue to a broken partner feed, a missing event type, an issue in a particular geographic region, saving users time.
Anomalo can be run as a managed deployment in the user's own cloud environment (i.e., an In-VPC deployment) for the utmost in privacy and security for data. With Anomalo deployed in a VPC, data will never leave an environment that controlled by the user.
Anomalo Integrations
Anomalo Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Comparisons
Compare with
Reviews
Community Insights
- Business Problems Solved
- Pros
- Cons
Anomalo has emerged as a reliable solution for businesses, addressing critical data quality issues and promoting collaboration among teams. Users have relied on Anomalo to detect and prevent real-time data quality issues, ensuring the accuracy and reliability of financial data. By monitoring data used in machine learning models, Anomalo enables users to maintain high-quality data and take immediate action on any anomalies that arise. This proactive approach not only saves time but also contributes to the overall success of the business by identifying and addressing data pipeline issues before stakeholders even notice them. The software's flawless alert system ensures that integrated teams are immediately notified when thresholds are exceeded, allowing for quick resolution of potential problems. Anomalo's transparency, with its charts in the Data Freshness and Data Volume sections, has proven invaluable in detecting historical data gaps as well as current ones. Additionally, the platform's Pulse dashboard provides a sense of control and security to the Data Engineers team, enabling unsupervised detection of anomalies in customer datasets. Users have found that Anomalo simplifies monitoring and debugging processes, saving valuable time and enabling creative use cases for businesses. With its advanced data quality tests, Anomalo brings attention to previously unnoticed issues that may have had significant consequences if left unaddressed. Furthermore, by automating data quality monitoring checks, Anomalo reduces manual effort and allows for scaling up effectively as the company acquires more data. Overall, users appreciate how Anomalo enhances the trustworthiness and reliability of their data products, making it an indispensable tool for businesses across various industries.
In addition to its main functionality of ensuring high data quality standards, Anomalo offers a range of features and capabilities that address specific business problems faced by users. For instance, the software helps tackle suspicious activity by detecting it promptly and providing confidence for future use in areas where fraud detection is critical. By alerting users to failures in data pipelines, Anomalo enhances analytics coverage without requiring manual monitoring, saving time and resources. Anomalo acts as a second layer of defense against data quality issues, complementing the first layer implemented within data pipelines. This comprehensive approach significantly reduces the time users spend locating and solving data issues while also improving their efficiency by enabling proactive issue detection. Anomalo's user-friendly interface simplifies data pattern exploration and identifies data drift, making it an excellent data visualization tool for end-users. Data scientists and analysts benefit from its combination of trend analysis, data segmentation, scheduling, and data visualization capabilities in their daily tasks
Flexibility: Users highly value the flexibility of Anomalo, with multiple reviewers stating that they appreciate the ability to customize their monitoring and anomaly detection. This flexibility allows users to build custom tests and select exactly what they want to monitor and how they want to do it.
Ease of use: The platform is praised for its useful features that make it easy for stakeholders to set up new tables with automatic checks and dive into the data. Multiple users find it easy to set up Anomalo on their existing infrastructure, and the compatibility with k8s infrastructure is mentioned as a positive aspect.
Proactive issue detection: Users consistently mention that Anomalo has a proactive approach to detecting issues in datasets. Several reviewers have found that Anomalo consistently detects small issues even before vendors detect them, enabling users to push issues upstream multiple times. This proactive approach is seen as a significant advantage compared to other solutions.
Lack of Cost Transparency: Some users have expressed frustration with Anomalo's lack of clarity regarding the costs associated with running validations on the Data Warehouse. This opacity can be problematic, particularly in large projects with extensive tables.
User Interface Challenges: Several users have mentioned that they find Anomalo's user interface to be non-intuitive and difficult to navigate. They have experienced challenges when trying to perform tasks efficiently and quickly on the platform.
Limited Customization Options: Users have reported a lack of flexibility in Anomalo, stating that the tool does not offer enough customization options beyond its original scope. This inflexibility makes it impractical for certain use cases outside of its intended purpose.