The importance of adopting a data observability approach to avoid data quality issues
The importance of adopting a data observability approach to avoid data quality issues.
Organizations across all industries and from all sizes struggle with data problems such as pipeline failure and quality issues. Nowadays, unreliable data is the costliest problem that data stewards and users face. Around 44% of time is wasted every day on maintaining data pipelines. Data downtimes have a harmful impact in the organization because it generates a significant waste of time and resources, revenue loss due to bad decision making, and lack of trust in results due to old or error-prone data.
Observability allows to identify and describe a problem, and also provides the context to resolve it and look at ways to prevent the error from happening again.
A data observability approach helps to manage data challenges such as pipeline failure and quality issues before they create costly impacts to your organization. IBM Observability by Databand is the only proactive data observability platform that isolates data errors as soon as data is integrated and triages issues to alert relevant stakeholders before there’s a crisis.
Data observability as part of a Data Fabric architecture
A data observability capability can be integrated in a Data Fabric architecture to leverage organizations most valuable asset: data. It can be tied to several uses cases such as multicloud data integration, data governance and privacy, customer 360, MLOps and more.
The Data observability strategy can be implemented independently; but in conjunction with a Data Fabric architecture will help automate the data lifecycle. The goal is to establish a worthy cycle where each data fabric use case strengthens the other. It also brings additional value to clients across the data lifecycle from end to end.
Additionally, many companies struggle with data silos, and a data fabric is the perfect way to connect those silos together.
Data Observability by Databand was created to remediate data issues such as broken pipelines, missing data, and schema changes by detecting and resolving these problems before they create costly business impacts.
Main Benefits
• Detect incidents earlier
• Resolve errors faster
• Deliver trusted data
• Scale data engineering
Main capabilities
• Automatic metadata collection
• Custom SLA alerting
• Root cause analysis
• Continuous anomaly detection
• Automated impact analysis
• Track data health
• Prevents issues from happening in the first place
Through its open and extendable qualities, IBM Data Observability by Databand helps your data engineering teams to effectively integrate and gain observability into their data infrastructure.
About Necando Solutions
Necando helps North American organizations optimize their most valuable corporate assets – people and data. We have worked with government agencies, financing, transportation, etc. Our rich and diversified experience and understanding combined with the IBM data solutions portfolio are what we are proud to provide to our clients.
About IBM
IBM brings together all the necessary technology and services, regardless of where those solutions come from, to help clients solve the most pressing business problems.