Data observability tools help remediation processes keep quality in check and pipelines healthy as data flows at increasing volumes to end users.
A-Team Group’s most recent webinar, The ROI of Data Trust: Quantifying the Business Value of Data Observability, examined the best practices, pain points and solutions for ensuring organisations derive the greatest value from their technology investments.
Here are the key themes and topics covered in the webinar, which featured a panel of experts comprising Jay Reilly, SVP, sales – Global Centre of Excellence – at Precisely; Christina Schack, head data operations and strategy at Vontobel; and Paul Barker, chief control officer – cross controls enterprise technology at HSBC.The discussion was moderated by Data Management Insight editor Mark McCord and sponsored by Precisely.
Go to the webinar recording here and find the theme of most interest or importance to you by scrubbing forwards to the relevant time split in brackets.
The meaning of data observability? (03:53-08:00)
- Monitoring data pipelines
- Maintaining data quality
How can teams win C-Suite buy-in for observability investment? (08:13)
- Stressing the costs of failures
- Criticality of data controls transparency
Measuring observability effectiveness when the best results are “nothing happened” (13:11)
- Defining KPIs and the pain points
- Reframing observability as insurance
- Importance of trusted data
Feeling the impacts of pipeline failure (20:50)
- Upstream software teams feel it first
- Constant improvement programmes
- Failures may not be detected immediately
Financial risks of failure (23:15)
- Compounding impact of failure in automated systems
- Data lineage
- Actioning error signals
Demystifying AI alerts (35:50)
- Setting mathematical baselines for AI
- Audit trails
- Shift to predictive
Balancing pipeline self-healing and manual intervention (39:26)
- Risk-based approaches
- AI pipeline management
Overcoming the limitations of fragmented infrastructure (42:20)
- Fractured vs fixed infrastructure
- Integration technology
Privacy compliance (46:20)
- Role of metadata
- Influence of BCBR 239 and GDPR
The relationship between data engineering teams and the broader business units (48:10)
- Common language of data quality
- Changing face of data engineering
- Self-service analytics
- Maintaining confidence in data
Key considerations before implementation (57:05)
- Criticality of architecture
- Data/model explainability
- Building trust
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