Data + AI Observability: Overview and Evaluation Criteria

Data + AI observability has been one of the hottest emerging data engineering technologies the last several years. And with LLMs taking the world by storm, data + AI observability has become a natural evolution of the category.

Benefits of Data + AI Observability

• Increasing data trust and adoption
• Mitigating the risks of bad data + AI
• Boosting revenue and data product ROI
• Reducing time and resource costs associated with data quality

What are Data + AI Observability Tools?

Data + AI observability refers to an organization’s comprehensive understanding of the health and performance of the core components of their data + AI products.

Data + AI observability combines data quality monitoring with accelerated root cause analysis capabilities. This enables organizations to correlate data + AI reliability issues to their corresponding root cause across data, systems, code, and model. For example:

• DATA: what source sent us bad data?
• SYSTEM: what specific job failed?
• CODE: what code change led to the anomaly?
• MODEL: was the model output fit for use?

The purpose of data + AI observability tools is to reduce data + AI downtime by automating or accelerating the detection, management, and resolution of data quality issues.

Key Features of Data Observability Tools: The Analyst Perspective

Gartner

Gartner has named data observability one of the hottest emerging technologies. They emphasize exploring data observability tools by investigating their features, upfront setup, deployment models, and possible constraints.

Gartner identifies several key workflows that data + AI observability solutions should support:

• Monitor and detect
• Alert and triage
• Investigate
• Recommend
• Resolve and prevent

Forrester

Forrester’s Total Economic Impact of Data + AI Observability report found a ROI of 357% with a payback period of less than 6 months. It states that augmented data quality solutions help organizations take proactive measures and prevent data quality problems at the point of ingestion.

The five quantified value drivers discussed in the report include:

• Avoided losses from data + AI downtime
• Reclaimed hours from data personnel
• Improved efficacy of AI/ML models
• Improved collaboration from data trust
• Savings from data cloud storage and compute costs

Key Features of Data + AI Observability Tools: Our Perspective

%[Various criteria and features discussed in detail below]

Enterprise-grade Scalability & Security

Organizations need a data + AI observability provider that can serve as a strategic advisor. Important areas to evaluate include:

• Security – Do they have SOC II certification? Robust role-based access controls?
• Architecture – Multiple deployment options for the level of control over the connection?
• Usability – Is it user-friendly? Can alerts actually save time?
• Support – Maturity of the vendor’s customer success organization.

Immediate Time to Value

A data + AI observability solution should have quick implementation and deliver near-immediate value. If it requires extensive setup, it may not meet the data quality efficiencies an enterprise needs.

End-to-end Visibility

The integration across the entire data + AI estate is crucial for monitoring all components and ensuring that issues do not cascade.

AI-Powered Workflows

Data + AI observability tools should utilize advanced anomaly detection monitors that can be automatically deployed across an entire data product.

Intelligent, Fast Root-Cause Analysis

Having a solution that correlates issues to their root cause is critical for minimizing bad data reaching consumers.

Operational Reporting

Key requirements include integrations with a data catalog and dashboards for tracking incidents, response times, and data health metrics.

Data + AI Observability RFP Template

Not every data team needs to issue an RFP, but some organizations find it helpful. Below is a sample template:

The Future of Data + AI Observability Tools

Category leadership is crucial when choosing a data + AI observability solution. It’s essential to understand how closely the vendor's vision aligns with your own long-term goals.