Data Observability Solution - Enhance Data Reliability

Unleash the true potential of your data.

Gain deep insights about its quality and visibility to resolve real-time data issues and reduce downtime with our Data Observability solution.
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What is Data Observability?

Data Observability ensures the quality, reliability, and performance of data within a system by continuously monitoring, measuring, and analyzing it. This proactive approach detects and resolves data anomalies in near real-time, enhancing the accuracy and trustworthiness of your data for analysis, decision-making, and downstream processes.

Implementing Data Observability practices and Data Governance strategies helps organizations improve operational efficiency and mitigate associated data risks, promoting overall productivity and quality.
Reduce Cost
Reduction in Data
Infrastructure Costs
Minimize Risk
Reduction in
Data Downtime
Save Time
Data Engineering hours
saved per quarter

5 Pillars of Data Observability


Tracking how regularly your data is updated to eliminate stale data


Monitoring changes to data tables and data organization to identify broken data


Tracking the number of expected values to confirm when your data is incomplete


Recording what to expect of your data values to determine when data is unreliable


Collecting metadata plus mapping upstream data sources and downstream ingestors to troubleshoot where breaks occur and which teams have access to that data.

Top Use Cases of Data Observability in solving real-time data issues

Data Downtime Management
Don’t just observe data incidents. Resolve them fast. Now you can alert, respond, and resolve all your data incidents in one location.
Data Pipeline Monitoring
Manage the health of 100s to 1000s of data pipelines. Detect missing operations, failed jobs, and run durations so you can handle pipeline growth.
Data Quality Monitoring
Ensure better data quality by monitoring data SLAs, unexpected column changes, and null records before they get to your consumers.
Root Cause Analysis 
Break siloes and get the whole data story with end-to-end data lineage. Understand the impact of data incidents on upstream and downstream data flow.
Data Anomaly Detection
The worst data incidents are unknown. Anomaly detection removes bad data surprises by automatically detecting deviant behaviour in your data pipelines.

Why Data Observability

Data observability goes beyond monitoring and alerting. Data observability allows organizations to understand their data systems fully and enables them to fix data problems in increasingly complex data scenarios or even prevent them in the first place.

"Mostly eliminates the data downtime by appealing best practices".
How Data Observability differs from Monitoring
Known unknowns
Monitoring tells you when something is wrong
Assumes you know what questions to ask
Periodic routine checks are supported only by established standards
3 step approach—data ingestion, issue identification, and data cleaning
Unknown unknowns
Doesn't assume that something is wrong
Assumes we don't know what all the questions are to ask
The set of practices are advocated by data teams to uncover data health
Helps resolve real-time data issues without any delay

Our Solution

Agilisium’ s Data Observability is a customized solution designed to monitor and enhance the reliability of data.

Custom frameworks and sophisticated ML models will help to detect anomalies, errors, and inconsistencies based on historical data and provide actionable insights.
Uniqueness of implementing Data Observability as a solution
No-code onboarding
Code-free implementation for full out-of-the-box coverage with your existing data stack and seamless collaboration with your teammates.
Security-first architecture
Data never leaves your environment. Our solution is 100% customizable as per your Data engineering stack.
Scales with your data
We monitor your data at rest and do not extract it from your data store, facilitating end-to-end coverage no matter how your stack evolves.
End-to-end observability
Use your favourite stack. Get a single view into data health across your data lakes, warehouses, ETL, business intelligence tools, and catalogues.

Why choose Data Observability as a plug and play solution?

100% Customizable for your Data Engineering stack
Data Security
100% Data never leaves your environment
Minimize Risk
80% Reduction in Data Downtime
Save Time
30% Data Engineering Hours saved
Request Guidance from a Data Observability Expert
Connect with our experts to harness the  power of Data Observability.

Our most recent work


What is the difference between Data Observability and Data Monitoring?

Data Monitoring primarily involves systematically tracking and examining data pipelines and systems in real-time to detect and resolve anomalies, errors, or deviations from expected behavior.   

On the other hand, Data Observability solutions take a broader approach, along with the technical facets. Data Observability software also understands and facilitates data interpretation.

How does Data Observability eliminate data downtime?

The pillars of Data Observability enable organizations to gain real-time visibility and understand their data pipelines and processes. 

Organizations can implement Data Observability frameworks to proactively identify and address issues that could lead to data downtime. It aids in keeping track of data flow, transformation, and quality throughout the entire data ecosystem.

What are the consequences of poor data quality?

Poor data quality can have significant consequences for organizations across various sectors. 

Here are some of the major consequences of poor data quality:

  • The precision and dependability of decision-making procedures may be impaired, resulting in erroneous tactics and unsuccessful results. 
  • It reduces the capacity for meaningful insight and data-driven decision-making.
  • Poor data quality can lead to challenges with compliance, government charges, and reputational damage.
How can Data Observability improve data usefulness?

An effective data observability framework can help obtain in-depth insights into the data assets' reliability, quality, and integrity. Enabling the detection and mitigation of problems with the data, such as inaccuracies, inconsistencies, and incompleteness, could ensure data reliability. 

Additionally, Data Observability supports proactive monitoring and warning systems, allowing quick identification and correction of any data issues.

What are the early signs of data downtime in Data Observability that you need to be aware of?

Look out for these indicators of data downtime in data observability, as they can arise unexpectedly and may go unnoticed unless their impact on your organization is substantial:

1. Your data team spends less than 12% of their time on urgent fire drills due to support on incomplete or partial data.
2. Your company experiences recurring financial losses attributable to erroneous data.
3. Inability to deliver crucial analysis or insights due to reliance on compromised or corrupted data.
4. Troubleshooting issues becomes increasingly challenging, requiring intricate and time-consuming debugging processes.

Talk to Us
Got a question? Don’t hesitate to give us a call today or shoot us an email. 
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