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Exploring Data Observability Tools? Access the Gartner® Report to learn more.

AI-First Data Quality

No code, highly scalable, with root cause analysis

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Acceldata Data reliability platform
Postgresql data source with Acceldata
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TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE
Acceldata customer guide HCHC
Acceldata customer guide Phonepe
Acceldata customer guide Dun & Bradstreet
Acceldata customer guide Hershey
Acceldata customer guide  Markle
Acceldata customer guide True
Acceldata customer guide Telcom
Acceldata customer guide Circana
Acceldata customer guide RD station
Acceldata customer guide ACT Fibernet
Acceldata customer guide HCHC
Acceldata customer guide Phonepe
Acceldata customer guide Dun & Bradstreet
Acceldata customer guide Hershey
Acceldata customer guide  Markle
Acceldata customer guide True
Acceldata customer guide Telcom
Acceldata customer guide Circana
Acceldata customer guide RD station
Acceldata customer guide ACT Fibernet

Our users love us

Read all reviews on G2

Timothy C.

Senior Expert of Data Governance - Enterprise
Acceldata has opened quite a few doors for us at Nestle. We have started with the Data Reliability features of the product and moved into the Cloud/Cost Optimization aspects.

Chidambararajan M.

IT Consultant - Enterprise
Acceldata is giving the better quality checks for the data sources and the maintenance of the environment is good.

Aman C.

Data Quality Engineer - Enterprise
Acceldata is reducing more of the human effort that the sustain team was putting where we can have the notifications if the DQ jobs and recon jobs fail.

Ankit B.

Senior Technical Lead - Enterprise
We are using power of Acceldata for our data quality issues. Its integration is simple and we are able to identify various data quality issues quite easily.

Jayanth Reddy G.

Senior DevOps Engineer - Enterprise
This combination of usability, seamless integration, and robust features makes Acceldata a powerful tool for enhancing data reliability and performance.

Acceldata User

Mid-Market - (51 - 1000 emp.)
The Tool is able to provide end-end Reconciliation feature as well Data Quality feature

Data Quality Checks on All Your Data

No more sampling, no cutting corners on DQ rules. Acceldata platform dynamically scales to measure the quality of all your critical data assets against all the rules and business policies needed to ensure data trust.

All Critical Data

All data types including structured, unstructured, and streaming data.

All Rules & Policies

Quality, freshness, drift, operational parameters, cost, and others.

All Data Stages

Measure data quality across Landing, Transformation & Consumption Zones

New to Data Observability?

Download this 2-pager to quickly learn what it is, how it can help you, and why it's essential today.
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Enterprise data observability by Acceldata

AI Based Anomaly Detection

Automatically learn your data patterns to detect anomalies that affect data quality
Detect multiple DQ anomalies
Detect anomalies due to schema changes, quality errors, missing data, inconsistent data, and others
Human Reinforced Feedback Loop
One click human feedback to continuously improve the anomaly detection AI model
Select Sensitivity Level to Reduce Alert Fatigue
Adjust sensitivity level for anomaly detection based on business needs
Learn more

Sophisticated Remediation Options

Apply the right data quality remediation strategy based on fine-grained conditions
Identify and prioritize what to remediate
Use business criticality, downstream impact etc. to prioritize which DQ problems to address first
Define quality thresholds
Specify quality thresholds such as acceptable, warning, and critical to either alert or drive action
Conditions based remediation action
Automatically quarantine data, stop pipelines, rerun pipelines and take other actions based on quality thresholds
FAQ

Frequently asked questions about Data Observability

Do you monitor data on-premises?

Yes, we monitor both data in the could and on-premises. For data on premises our data plan resides within your environment, which send only meta data back to the control plane. None of actual data ever leaves your premises.

What other data problems do you monitor?

In addition to traditional Data Quality, we monitor data drift, schema drift, data freshness, reconciliation of data across data hops. These and other monitors provide you a comprehensive health of your data.

How Can I Reduce Alert Fatigue

Acceldata’s Anomaly detection algorithm is extremely sophisticated and built entirely on your data set, hence the accuracy of alerts is extremely high. In addition anomaly detection has sensitivity levels (low, medium and high), which can be used to minimize anomalies.

What approaches do you use for DQ?

We have a three tier approach. Based on AI based detection of asset type and field a basic set of Data Quality policies are automatically applied. These policies can be modified or edited. Second, we apply Anomaly detection to automatically detect drift in quality and other metrics. This is based on AI models that is built solely based on your data. Third, we allow you to write highly specific custom rules based on a no-code interface. For highly complex business specific rules, we also enable low-code rules that can be written in any language such as SQL, Python, Javascript and such.

How can I prioritize remediation?

Critical data assets can be tagged and any issues from those can immediately be prioritized. You can also prioritize highly used assets for faster remediation to minimize impact to your business.

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