What Is Data Observability – and What It Isn't – in Enterprise Data Management

When we hear “data” and “observability” mentioned together, we can readily assume that the point is to monitor your data proactively. But what is the purpose of data observability in an enterprise? Should this monitoring happen in real-time or at regular intervals? To what extent can we automate it? Can it feed our analysis with something more than revealing current data quality issues?

To answer all these questions, let’s start with the definition of data observability and dive into how it differs from other pillars of data management.

What Is Data Observability: the Data Observability Definition

Data observability covers both the processes and practices that ensure data health by proactively monitoring the organization's data ecosystem, including cloud data warehouses (CDW). The initial data observability definition first emerged in 2019 by applying the general principles of observability to data, thus applying “data observability” to tackle downtime situations when data stays unactionable.

As enterprise data systems grow bigger by incorporating more data sources and end users with time, the risk of data downtimes increases. The downtime cost estimates vary between $1M per hour and $5M per hour, depending on the industry and the company size, and that’s without possible penalties and fines for breaching the regulatory requirements. Therefore, proactive anomaly detection and data observability are no longer optional for data-driven enterprises; they are a must.

As a result, data observability tools have become the new normal for enterprise data stacks in 2023.

Data Observability’s Role in Enterprise Data Warehouse Management

Cloud data warehouses are frequently costly and difficult to manage at scale, but this is a necessary investment on any enterprise’s part. On the one hand, continuous data monitoring, ensuring data quality, as well as identifying and troubleshooting existing data issues consume in-house time and resources. On the other hand, the data volumes keep growing, and so do the related risks and costs.

How can enterprise data observability help with that? It fosters systematic monitoring and immediate response to the root causes of data downtime. It elevates the enterprise-wide data culture, unburdens data quality teams, and increases the ROI of cloud data warehousing (CDW) use.

Now that we discussed the evolution of “what is data observability,” let’s see how it differs from other related data management practices.

Data Observability vs Data Ops

Data Ops is dedicated to building and managing data pipelines. The similarity with DevOps isn’t accidental, as Data Ops applies the same CI/CD approach (continuous integration and deployment).

So, what is data observability compared to Data Ops? These are data health monitoiring processes and practices that enterprises can layer atop data pipeline development. Developers can glean meaningful insights from their data observability tools and reports on early signs of data anomalies and data issues.

Data Observability vs Data Governance

Data governance sets unified enterprise-wide data quality standards and rules on how to sustain them. So, generally, governance is about setting strict policies that will meet stakeholders' strategic visions on how to ensure data integrity.

Observability platforms, in turn, provide the necessary means to instrumentalize quality monitoring and detecting and correcting issues. Moreover, data teams can take detailed and clear reports from observability tools to board discussions to prove that governance policies are met.

Data Observability vs Data Monitoring

Data monitoring leverages machine learning to recognize typical data behavior patterns and notify users of discrepancies. However, most external services monitor the pipeline performance without monitoring the data quality itself.

The data observability platform extends monitoring, allowing data engineers to check data integrity and correctness end-to-end. Such a comprehensive approach ensures the necessary consistency of data quality. It prompts specialists whether they need to improve data sources or modernize ETL tools, or whether there’s a problem with BI software that cannot process the incoming data.

Data Observability vs Data Quality

Another aspect to consider about "What is data observability and what is it good for?” is its impact on data quality. In our previous article about the most common data issues, we outlined 5 quality metrics:

• Accuracy
• Completeness
• Consistency 
• Timeliness
• Relevance

Data observability tools allow you to add context to these common data quality problems: you get insights on what happened, where, who’s involved, and who suffers from the data downtime. 

Data Observability in Practice

Generally, organizations ensure data observability by replicating an approach similar to DevOps. However, observability in software engineering focuses on preventing software application downtime only. Conversely, Data engineers, on their part, need to monitor and evaluate data health at scale and proactively prevent data issues. Thus, they reduce data downtime risks to a minimum.

Here are the 6 key areas where data observability is applicable:

1. Data Freshness. To kick off, you must evaluate whether your data assets update on time. The data decays unevenly from industry to industry, so you must understand its cadence.
2. Data Quality. For this aspect, data observability helps ensure compliance with data quality standards adopted in an organization. For instance, the percentage of unique/duplicated data and completeness of records. Additionally, it’s worth measuring data relevance by considering the data usage ratio and time to analyze.
3. Data Stack Performance. Enterprise data stack must ensure smooth and fast-flowing ETL performance. Data observability tools allow enterprise data ecosystems to perform at a high level and scale up effectively as request processing intensifies.
4. Data Lineage. As previously deduced from the data observability definition, it helps data teams monitor the entire life cycle of data and detect the exact stage at which something went off. With observability tools, you know exactly where anomalous or erroneous data appear, what activities caused it, and how it impacts downstream users.
5. Schema. It represents how data flows are organized within the organization. You can significantly cut data lake or warehouse expenditure by forbidding excessive data access and usage. Such was the case of ThoughtSpot: they cut their cloud data platform (CDP) spend by 30% thanks to Revefi Data Operations Cloud.
6. Volume. The volume describes the completeness of the data. On the other hand, it tips you on how to balance it without overfilling your records with irrelevant or excessive values.

6 Features Your Data Observability Tools Must Have

Now, let’s move from the question of “What is data observability?” to selecting the best-of-breed data observability tools. Data quality teams broadly agree on the following evaluation criteria:

• A plug-and-play setup. The monitors get ready and running within a day or so, meaning there’s no need to tweak the code or rebuild the existing data pipelines. Such zero-touch installation greatly benefits businesses as they save tons of human resources and work hours.
• No pre-setting of monitoring objectives. The monitoring runs hands-free, and there’s no need to guide it. Modern observability solutions identify core data sources, dependencies, and destinations automatically.
• Human-readable insights on data health issues. What is data observability value in terms of ease-of-use? Well, it delivers rich context on each case of suspicious data behavior in an understandable way. Here’s an example of how Revefi’s Slack notifications look like:

• Prevention-focused monitoring. Proactive prevention stems from the previous feature. AI-powered algorithms prompt business users on how to correct flawed data so it won’t harm operational stability and distort the outputs.
• No data sampling and extraction. Another upside of data observability tools is that they don’t overuse cloud data warehouse computing capacity, meaning you won’t run into overspending. Monitors access only metadata, whereas enterprise data remains at rest.
• Privacy сompliance. The previous point underpins the observability of software compliance with privacy laws as they don’t extract sensitive enterprise data. Moreover, these products are mostly SOC 2 compliant, which implies the proactive monitoring of processing integrity, encrypted channels, and two-factor authentication.

Going Beyond Data Observability: How Revefi’s Data Operations Cloud Transforms Data Observability

So, the early definition answered the question of “what is data observability” in the context of the operational stability of an enterprise data stack. The further development of data observability platforms and their functionality broadened the term into overall data health and data quality practices. Revefi Data Operations Cloud takes data observability even further, beyond the initial definition of “what is data observability”. It converges monitoring of data quality, cost, usage, and performance into an unprecedented form factor that provides value to data teams within minutes. 

Traditional approaches tend to view the different pillars of data quality, cost, performance, etc. in silos. In reality, the hard problems that data teams grapple with are at the intersection of these. As an example, which business team would say no to the operational data that is fresh up to the latest second? This may lead to the data team implementing a beautiful pipeline that is, say, refreshed every 15 minutes. A few months later, the business moves on to other priorities, and this operational data is no longer needed. The pipeline itself, faithfully refreshes every 15 minutes, consuming valuable resources which could have been deployed somewhere else. In every conversation with data teams, we hear different variants of this same movie, with lean data teams struggling to keep up with business velocity on one hand and CDW pricing models on the other. 

Revefi Data Operations Cloud provides the traditional data observability value, and also intentionally extends into adjacent data-related areas, helping data teams quickly connect the dots across data quality, usage, performance, and spend.

Revefi Data Operations Cloud is a must-try if you want to establish full-fledged data observability and get started immediately. It provides:

• A hassle-free zero-touch installation. Revefi connects to your CDW-based data stack to ingest your metadata, and provides insights within minutes. There’s no POC (proof-of-concept) needed!
• Automatically deployed monitors. Zero-touch copilots start monitoring your data quality, spend, performance, and usage with no configuration or manual setup needed. There’s no need for custom coding or poring through documentation.
• Proactive data issue prevention. Predictive algorithms update you on data anomalies and errors before they affect co-dependent data assets or skew future calculations.
• AI-powered root cause analysis. Get to the root cause 5 times faster compared to manual debugging. Automated root cause analysis provides a holistic view of the entire data lineage.
• Data Usage. Ensure the all-time usability of valuable data assets. Consistent monitoring and evaluation prompt the data team to keep data sets lean, accessible, and debris-free.
• Enhanced cost-efficiency of CDW use. Studies estimate that the indirect impact of implementing a data observability solution results in a ~10% decrease in annual cloud data warehouse expenses. With Revefi Data Operations Cloud, you can cut CDW vendor’s bills by 30% with a higher efficiency of cloud data storage use.

Revefi transforms the idea of “what is data observability” by converging the conventional, traditional Data Observability / Data Quality with additional monitoring of Data Usage, Data Costs, and Data Performance, thus elevating it from tactical into the strategic level that impacts the ROI and performance of the whole organization. Try Revefi for free to see how simple and convenient it is to resolve data issues 5x faster, and slash your CDW costs by 30%.