Detect Anomalies to Improve Data Quality

Quick Start, Out of the Box

Connect your data to Telmai and detect patterns and values that stand out. These outliers could be related to data quality issues such as unexpected string values in a numeric field or could be outliers in business metrics such as transaction totals that fall outside the expected distribution of your data.

Time Series Drift Detection

Telmai uses time series analysis to learn about the trend of values in your data over time and predict what can be reasonably expected. With Telmai’s observations, you are able to identify data points that drift from expected thresholds. You can decide how much variability is standard and set rules and expectations to get notified when outliers and anomalies occur.

Monitor and Detect Changes in Real Time

Telmai uses the historical analysis of your data to create a baseline for normal behavior. With that baseline established, Telmai automates your anomaly detection. When your data crosses a certain threshold or falls outside historical patterns, alerts and notifications help you uncover changes in your data and key KPIs.

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Trusted by

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The Leader in Data Quality

Telmai has been named a High Performer in the G2 Data Quality  Report. Checkout our 5 star reviews.

Trusted by

Quick time to value

Telmai's out of the box data quality metrics, ML-based automation, and a low-code, no-code user interface provides you and your team engineering productivity and faster time to value.

Faster detection and resolution of data quality issues by using ML
Cost reduction vs. maintaining data quality rules over time

The Leader in Data Quality

Telmai has been named a "High Performer" in the G2 Data Quality Grid Report of Spring 2023.

Checkout our 5 star reviews.

Customer testimonials

“Our customers deserve the best possible data to make their business grow. Telmai's Data Observability enables us to show the quality of the data that we are delivering to our customers. With Telmai we are able to build data quality KPIs that reflect our data's freshness, completeness, and accuracy to keep and extend the trust that our customers have put in us.”

Harlow Ward

CTO, Clearbit

"When dealing with data sets comprising million of records, Telmai quickly becomes an incredibly useful tool, saving a lot of time and ultimately increasing data quality."

Maria Grineva

SVP Engineering, Dun & Bradstreet

“We chose Telmai because of its flexibility and architecture. Telmai will remain our data observability platform as our data landscape changes. Telmai has lots of adapters in place to quickly integrate with various data sources across cloud providers. In addition, it supports real time API integration for automated decisions in complex workflows.”

Kishore Damodara

AVP of Cloud and Data Engineering, Merkle

"Data anomaly investigations that used to take hours now take minutes and give us more time to focus on the business needs."

Darius Kemeklis

EVP, Myers-Holum

“In searching for data quality and observability, we looked at a few solutions. They were limited to tracking just the data volumes, row counts, completeness, and uniqueness. That was not enough for us. We needed to look inside each attribute and each record to see if our data is drifting or its patterns are changing. Telmai was the only solution that empowered us to do so.”

Alejandra Cabrera

Data Product Manager, Clearbit

“To prepare data for product usage analysis we needed data quality metrics beyond monitoring operational data pipelines and job status checks. While we continue to monitor the quality of the pipeline, we chose Telmai to detect the quality of the data that moves through the pipeline.”

Raghu Nadiger

Data & Analytics Leader, DataStax

Turnkey integrations with your stack

Snowflake Data Quality
Databricks data quality
BigQuery Data Quality
Cloud Pub/Sub Data Quality
Amazon Kinesis Data Quality
Kafka Data Quality
POstGreSQL Data Quality
MySQL Data Quality
MySQL Data Quality
Firebolt Data Quality
SQL Server Data Quality
Azure Blog Storage Data Quality

Telmai is a platform for the Data Teams to proactively detect and investigate anomalies in real-time.
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