Data Observability Solution For Data Quality and Reliability

What is data observability?

Data observability is about ensuring the quality, reliability, and performance of your data in the system by monitoring, measuring, and analyzing it. This helps eliminate all arising data anomalies by enabling teams to collect, profile, alert, and resolve data issues in near real time.
‍
Stay assured that your data is accurate and trustworthy for analysis, decision-making, and downstream processes. Implementing data observability practices and data governance strategies helps improve your overall operational efficiency, mitigate associated data risks with quality and productivity.

Reduce Cost

Reduction in Data
infrastructure Costs

Minimize Risk

80%

Reduction in
Data Downtime

Save Time

30%

Data engineering Hours
Saved per Quarter

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

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

Observability

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.

Our most recent works

Ebook

How Log Management and Analytics with Amazon OpenSearch Serverless is revolutionizing Operational Analytics?

Amazon OpenSearch Serverless is transforming operational analytics by simplifying and accelerating log management and analytics, enabling businesses to gain real-time insights into their operations.

CASE STUDY

How a F500 Pharmaceutical Commercial Team achieved Data Democratization and reduced Drug Commercialization Cost by 30%

Agilisium used AWS services to rearchitect a commercial data lake and a data warehouse with end-to-end process automation

Blog

Vital role of talent in digital transformation journey

In 2022, digital transformation is the key driving factor behind the success of any organisation.

View All Resources

FAQs

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 Data Observability Can 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.

‍

Data Observability Solution - Enhance Data Reliability

What is data observability?

5 Pillars of data observability

Freshness

Schema

Volume

Distribution

Lineage

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

Why Data Observability

Our Solution

Why choose data observability as plug and play solution?

Our most recent works

FAQs