5 Ways Actian Data Observability Supports Data Analysts: Video Demos

#Data Intelligence #Data Observability #Data Quality

#Data Intelligence #Data Observability #Data Quality

Summary

Actian Data Observability helps analysts understand data behavior through full-scan profiling rather than sampling, giving a more complete view of trends, edge cases, and trust signals.
It provides immediate monitoring through built-in rules that detect issues such as schema changes, drift, freshness anomalies, possible PII exposure, and unusual volume changes.
It also lets analysts create custom monitors with a low-code or no-code approach, including AI-assisted rule creation from natural language.
When issues appear, it helps teams understand their scope and priority by placing alerts in historical context through trend analysis and visual exploration.
It further supports troubleshooting with grounded explanations and root-cause analysis through integration with AI assistants and agents.

With sprawling data ecosystems the norm, most data analysts are responsible for more data than they could ever monitor manually. To ensure that your data is clean, trustworthy, and shareable, you need a reliable solution that can help you automate your understanding of what data you have, what it should look like, and how it’s changing over time.

Enter Actian Data Observability. Watch Video

Here’s a short list of 5 ways that it supports data analysts like you in understanding, monitoring, and troubleshooting the data you’re accountable for.

1. Understand your data’s behavior with comprehensive profiling.

Actian Data Observability doesn’t do sampling. Because sampling doesn’t deliver the level of accuracy, precision, and insight into edge cases that you need in order to do your best work.

With full scan profiling, Actian Data Observability delivers the statistical, trend, and trust information that you need to understand the behavior of your dataset as a whole and of the individual fields inside it.

See how you can understand your data’s baseline and build evidence-backed trust with Actian Data Observability: Watch Video

2. Get instant insight with out-of-the-box rules.

As soon as you connect your data asset to Actian Data Observability, 16 out-of-the-box rules begin monitoring your data, delivering built-in protections on the data you’re responsible for.

Some key alerts that immediately begin reducing blind spots in your data include:

Schema changes: Have columns been added, removed, or reordered?
- Example: A column named “Address 2” is missing.
Data drifts: Is the validity rate of the column’s values unusually low or high?
- Example: A “State” column is typically 99% valid two-letter codes, but today 12% of the values are “ZZ.”
Freshness changes: Is the data earlier or later than usual?
- Example: Data that’s typically timestamped between midnight and 6am is instead timestamped between 6am and noon.
Potential PII exposure: Is there personally identifiable information such as names, phone numbers, or SSNs present in the data?
- Example: The data’s pattern is consistent with typical phone number formatting.
Volume anomalies: Has the number of records in the dataset changed by an unusual amount?

These built-in rules use machine learning to train on your complete dataset–not just sampling–to enforce learned patterns and business expectations that are unique to your organization. Over time, that same machine learning ensures that the rules’ parameters stay up-to-date as your data changes.

With those inevitable changes in your data, it’s often difficult to scale custom rulesets. Actian’s out-of-the-box data monitors ensure that you have immediate, useful, and relevant observability for every single one of your data sources.

This includes critical visibility into subtle data changes that can lead to silent failures. Actian’s built-in rules deliver proactive visibility at the moment an issue happens, instead of reactive work after the issue has snowballed into a much bigger problem—or, worse, not been found at all.

See how detailed, context-rich alerts from Actian Data Observability can help you triage data incidents: Watch Video

3. Create custom monitors with no/low code.

As a data analyst, you have irreplaceable insight into your organization’s key data concerns. Which attributes do your critical processes and reports use? Which data assets are most important to your production systems? What types of issues are most common, or most concerning?

Actian Data Observability makes it easy to transform complex business logic into effective monitoring focused on what you care about most with an AI-powered low-code/no-code approach.

Transform natural language to effective data quality monitoring in seconds using Actian’s Ask AI: Watch Video

4. Understand the extent of your data issues and prioritize.

Once you’re alerted to a data quality issue, that’s only the start of the questions. Is this an isolated issue or widespread, a one-off or recurring? How do you pinpoint the affected data so you can prioritize remediation efforts and prevent downtime?

Trends in Actian Data Observability provide easy-to-explore visuals and meaningful alerts to help you understand the scope of your data reliability issues.

Take a look at how Actian’s trends put your data issue alerts into historical context: Watch Video

5. Get actionable explanations and analysis.

Identifying the existence of a data issue is only the first step. Day-to-day, you need explanations: Why is the issue happening? What’s the issue impacting? What’s the overall state of the data asset?

Actian’s Data Observability includes MCP Server integration, which allows you to connect your data observability to your favorite AI assistants and agents. Instead of generic AI speculation, you can get explanations and assessments grounded in your specific data, monitors, and trends.

See how better root cause analysis is just a quick question away when you use the MCP Server to connect your Actian Data Observability to AI: Watch Video