StarTree ThirdEye is an anomaly detection, monitoring, and interactive root-cause analysis "all-in-one" platform. It comes with a self-serve UI experience. It empowers insights producers and consumers to unleash the power of actionable insights from anomalous events in a self-serve interactive way to act on it in real-time.
StarTree ThirdEye has unique advantages to fast-track problem solving with applied Anomaly Detection on time series data at scale.
- Reduced Time for Detection
- StarTree ThirdEye can easily connect to real-time and historical data sources at scale and detect anomalies with a point-and-click experience. Allows monitoring of multiple time series in a given dataset in real time.
- Allows to run aggregated queries such as Sum/Avg/Count and simple/advanced statistical methods to detect outliers at scale in sub-second response time.
- Applied Science
- It runs outlier detection with applied science (out-of-the-box Anomaly Detection models) on time series data. Allows users to fine-tune these detection models for reduced false alarms with easy-to-use configurations based on the business context. Users need not write algorithms and maintain models and data pipelines to detect outliers.
- Reduced Time to Resolution
- An interactive UI performs root cause analysis in case of an incident and unlocks actionable insights such as top contributors and heatmap of dimensions contributing to the spike or drop in the critical metrics in an easy-to-use interface.
- It allows performing dimension drills at scale in sub-second response time helping with faster time to unlock actionable insights.
- Intuitive User Interface, Customization, and Flexibility
- It has a user-friendly interface that makes it easy to set up and use with a point-and-click-low code/no-code experience.
- Users can customize the algorithms and parameters and build custom apps using APIs to solve custom business use cases.
- Support and Documentation
- StarTree ThirdEye comes with comprehensive documentation and support from StarTree in our community Slack.
It has the following key features:
- Self-serve UI (low-code/no-code) UX
- Monitor, detect, and resolve outliers in massive real-time data at ease
- Interactive root-cause analysis helps to fast-track problem-solving
- Statistical detection techniques helps in detecting
- Global outliers (outliers far outside)
- Contextual outliers (ex: seasonal data patterns)
- Collective outliers (significant deviation from other data points)
- Advanced anomaly detection configurations and intelligent alerts
- Metrics and dimension level anomaly monitoring (single/multiple timeseries): Identify anomalies in massive and rapidly changing data by configuring one-time intelligent alerts for critical metrics
- Anomaly filters: More control in the hands of the user to catch true anomalies and minimize false alarms
- Create (no code) aggregated metrics (ex: sum/count) in ThirdEye for outlier identification
- Generate metrics at different granularities
- Generate different metrics (count,sum,distinct count, percentile etc)
- Group metrics by different sets of dimensions
- ThirdEye Genie - Cohort recommender
- Recommends top contributors of cohorts (group of dimensions/metrics in a dataset) having significant deviation on a given KPI/metrics in a massive scale of data which is hard to find manually. Using point and click experience one can easily start monitoring these cohorts in no time.
- Fast track problem solving (using root cause analysis)
- Self-serve interactive UI
- Identify key dimensions contributing to the outlier
- Find the correlation with related metrics contributing to the outlier
- Find the events contributing to the outlier
- Modular architecture
- Flexible architecture enables easy plug-in of time series data stores such as Apache Pinot and allows plug-ins with anomaly detection models/libraries
Automated monitoring and root-cause analysis of User facing KPIs
- Monitor, detect, and resolve outliers in user-facing insights/KPIs such as detecting outliers in product catalogs so that merchants can ensure the product catalog is stocked, priced, and marketed well for their products.
Automated monitoring and root-cause analysis of Business KPIs
- Monitor, detect and resolve outliers in business-critical KPIs and identify key drivers impacting the business KPIs
Example: a new successful marketing campaign that increased leads or a promotional discount that drove up sales, or a price glitch impacting revenue
Automated monitoring and root-cause analysis of Product KPIs
- Monitor, detect, and resolve outliers related to KPIs used for improving Product experience or driving growth for the product Example: A new feature release or launch or software bugs or increase in # of active users that caused a sudden drop/increase in the user engagement KPIs (ex: # of views or # of monthly active users or paid users)
- Monitor, detect, and resolve outliers in KPIs used to monitor overall data health and quality, such as the drop in data completeness, accuracy, freshness, and compliance stats
Automated monitoring and root-cause analysis of Systems KPIs
- Monitor, detect, and resolve outliers related to KPIs used for ensuring system health of a platform or product, such as the spike in Cloud Costs, Cloud Failure, Infra performance
- Monitor, detect, and resolve outliers related to KPIs for ensuring IoT system health, such as the spike in performance, data volumes, and uptime
Unexpected deviations in critical metrics result in a materialistic impact on business, such as exponential growth in user activity on user facing applcications, revenue loss, drop in user engagement, and increased operational cost. In addition, with rapid growth, change of data, and the measurement of millions of metrics, monitoring and problem-solving are becoming extremely challenging and expensive.
StarTree ThirdEye is built on top of the Open-source Thirdeye, developed at LinkedIn in . StarTree ThirdEye is an anomaly detection platform that allows organizations to monitor Business KPIs in a real-time fashion, and detect any anomalies automatically. This allows key stakeholders to take corrective action in a timely manner.
It is uniquely positioned to solve these challenges with "all-in-one" detection, monitoring and root-cause analysis solution for monitoring critical business and user facing metrics and KPIs.
Some of the examples are:
- Monitoring business KPIs such as # of booked orders help to ensure you correctly handle order fulfillment and logistics.
- Monitoring business KPIs such as order sales helps experiment with different food prices to ensure the pricing is correct and well adopted.
- Monitoring user-facing KPIs such as popular food items, delivery time, and delivery distance will help you understand customer behavior so that you can take the necessary measures to drive engagement and retention.
- Monitoring business KPIs such as booked orders and advertising campaigns will help to decide the correct advertising plan to define marketing strategies, suitable social media platforms, and their target audience.
What ThirdEye is not used for?
ThirdEye maintains a dedicated meta-data store to capture data sources, anomalies, and relationships between entities
but does not store raw time series data. It relies on systems such as Pinot to obtain
both real-time and historic time series data. You can use Data Manager to ingest data from various data sources to Pinot (Such as batch, real-time, CSV etc)
ThirdEye does not replace your issue tracker - it integrates with it. ThirdEye supports collaboration but focuses on the
data-integration aspect of anomaly detection and root-cause analysis. After all, your organization probably already has
a well-oiled issue resolution process that we don't want to disrupt.
ThirdEye is not a generic dashboard builder toolkit. ThirdEye attempts to bring overview data from different sources into one single place on-demand. In-depth data about events, such as A/B experiments and deployments, should be kept in their respective systems. ThirdEye can link to these directly.
Tim Berglund explains ThirdEye