There is a lot of discussion about Data Observability and why it’s important; this is a short summary of what it is, why the resolution is important and how IBM’s Databand can help clients should they be considering steps in that direction.
In today’s data-driven world, ensuring the quality, reliability, and availability of data is paramount, particularly when you have multiple applications and systems in operation, many of which are running 24/7 or mission-critical.
Data observability helps maintain data visibility. Data observability refers to the comprehensive monitoring and management of data across various processes, systems, and pipelines within an organisation. It goes beyond traditional data monitoring by providing insights into the health and state of data, enabling organisations to proactively identify, troubleshoot, and resolve data issues in near real-time.
So why is Data Observability Important?
- Data Quality and Reliability: Poor data quality can lead to incorrect business decisions, financial losses, and damaged reputations. Data observability helps maintain high data quality by continuously monitoring data pipelines for anomalies and inconsistencies.
- Operational Efficiency: By providing a clear view of data flows and dependencies, data observability tools help data teams quickly identify and address issues, reducing downtime and improving overall operational efficiency.
- Compliance and Governance: Maintaining data integrity and compliance is crucial with increasing regulatory requirements. Data observability ensures that data governance policies are adhered to, providing transparency and traceability.
- Innovation and Insights: Reliable data is the backbone of advanced analytics and machine learning models. Data observability ensures that data remains a valuable asset, driving innovation and insights.
IBM have a solution which is available ‘as a service’ and easily deployed, called ‘Databand’. This is how IBM’s Databand can help
IBM® Data Observability by Databand is an observability solution designed for data engineers and DataOps teams.
Currently, most data engineers use various tools to run their pipelines (for example, Airflow, Python, Spark, Snowflake, and BigQuery). When you work across all these systems, you need deep visibility across Directed Acyclic Graphs (DAGs), data flows, and levels of infrastructure to:
- Make sure that pipelines are reliable.
- Detect issues that lead to Service Level Agreement (SLA) breaches.
- Identify problems in data quality.
IBM Data Observability by Databand can track, alert, and help you investigate problems in data quality, integrity, and access. Databand provides visibility into this information by:
- Automated Metadata Collection: Databand automatically collects metadata to gain immediate visibility into data pipelines, enabling customised data quality validations.
- Historical Baselines and Anomaly Detection: By building historical baselines, Databand can detect anomalies and alert data teams to potential issues before they impact business operations.
- Smart Communication Workflows: Databand allows for the creation of smart communication workflows to remediate data quality issues, ensuring data deliveries stay on track.
- End-to-End Data Lineage: Databand provides end-to-end data lineage, helping organisations understand the impact of data incidents across all data flows.
In summary, data observability is essential for maintaining the integrity and reliability of data in modern organisations. IBM’s Data and offers a comprehensive solution to monitor, manage, and maintain data quality, ensuring that data remains a valuable asset for driving business success and is available as a Software-as-a-service solution.
For more detailed information, please reach out to SmallNet Consulting here – https://www.smallnetconsulting.co.uk/contact-us/
