What is data observability? Discover the 5 pillars of healthy data

Data observability is the practice of monitoring and diagnosing the health and performance of your data systems. Unlike other data practices, observability focuses on the bigger picture: the whole ecosystem, not just a single tool or software.

By creating a framework based on the five pillars of data observability, you can visualize the flow and quality of data across your business. The aim is simple: to ensure data is always complete, reliable, and fit for purpose.

Data is everything in modern business. By ensuring data observability, you can address data issues and quality challenges and lay the groundwork for strategic decision-making, operational efficiency, and compliance.

The 5 pillars of data observability 

Central to data observability are 5 key pillars. These define what high-quality data means for your business and, more importantly, can be linked to metrics and KPIs to ensure data is always working in your best interests. 

1. Data freshness

The first pillar is data freshness: a measure of how recently data has been updated. Access to up-to-date, reliable data is critical for strategic decision-making. The amount of latency that’s acceptable for your business depends on how you use data. Do you do data stacks to check for data reliability, and are these updated hourly or daily? 

Refreshing data systems regularly and identifying and acting on outdated content will ensure the accuracy and integrity of your data for operations and compliance.

Solutions to ensure data freshness

• Pipeline management to streamline and monitor data pipeline schedules
• Monitoring tools to track when data is ingested and uploaded
• Automation tools that send alerts when data is stale

2. Data distribution

Distribution relates to data health. It might be fresh, but are its attributes within the range you expect? Examining the shape and structure of your data is key for anomaly detection. Acting on these discrepancies will maintain data integrity (and quality), ensuring the people who need it can trust its veracity.

Solutions to observe data distribution

• Data governance solutions to ensure data distribution patterns are correct and align with regulatory standards
• Data quality monitoring tools with visualized charts and graphs to help interpret distributions

3. Data volume

Data volumes need to be consistent. Monitoring the data flow in pipelines will flag any sudden spikes or slumps in the amount of data ingested. This allows you to quickly act on bottlenecks and overflows that can cause major issues for the data system, like reducing efficiency, increasing costs, delaying updates, etc.

Solutions to observe data volume

• A cloud storage platform that monitors volume changes and sends alerts for sudden spikes and drops.

4. Database schema

An observability framework must account for schema changes. Data needs to be collected in a logical structure with the correct table names, fields, etc. This must be consistent across sources and applications, as anomalies can cause disruptions. Monitoring schema is critical to detecting and addressing sudden changes to data values. 

Solutions to observe data schema

• Schema validation software for consistency checks
• Automated cloud-based monitoring to detect changes and notify teams
• Developer platforms to log and manage the schema

5. Lineage

Data lineage tracks the flow of data, providing a full view of its origins, transformations, and dependencies so you know what data you have and where it resides. With a clear overview of pipelines, you can conduct root cause analysis to troubleshoot and solve data problems and undertake impact analysis to evaluate how updates, migrations, and other events will affect data systems.

Solutions to observe data lineage

• Lineage tracking software to map data flow and transformations
• Data catalogs to create end-to-end lineage visualizations

What are the benefits of data observability? 

Data can make or break a business. Bad data negatively affects everything it comes into contact with. It erodes trust, creates inefficiencies, and leads to poor decision-making, which can result in operational failures and financial losses.

Unity Software lost $110m in revenue after discovering large-scale data quality issues ingested from an important customer.

Data observability is the best method for guarding against these outcomes. But it’s not just a defense mechanism – it also maximizes the value you get from all the essential data you possess.

Data observability allows you to:

1. Transform data quality

Feedback loops from errors and deviations help data engineers create stronger validation rules and transformation logic, improving data quality and reliability. Observability practices deliver the clean and accurate data your business needs to succeed. 

2. Enhances data operational efficiency

Data observability practices streamline pipelines and reduce inefficiencies, speeding up workflows. The increased transparency of data operations minimizes delays and supports faster deployment of data model updates and ETL changes.

3. Achieve compliance

Active mapping and monitoring ensures data meets regulatory requirements for GLBA, GDPR, APA, HIPAA, and CPPA. Data lineage also creates automated audit trails for evidence that your data lakes and data landscapes are compliant.

4. Make strategic decisions for managing data

Data observability strengthens analytics and provides complete, accurate, and relevant data for forecasting and identifying trends, allowing you to seize opportunities and gain a competitive edge. With reliable and robust data, you can rely on trusted insights to make critical decisions.

5. Mitigate risks

Automated alert systems flag unusual activity and unauthorized access before they escalate into major issues, improving governance and security.

How to implement data observability 

Now that we’ve established that data observability preserves and enhances the health and performance of your systems. Let’s look at how to draft an observability strategy and implement it at scale. 

Step 1: Categorize data for business operations

First, categorize your data into three use cases: analytics, operational, and customer-facing. 

• Analytical: Data used for decision-making
• Operational: Data used to support business operations
• Customer-facing: Data used for customer experiences

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The value of data is contextual. Certain processes need to be weighted more heavily towards one of the five pillars. For example, distribution and accuracy are critical for financial reporting and compliance, while freshness will take precedence for machine learning applications. 

Step 2: Identify and address key healthy data metrics

Next, go through the data observability pillars for each of the 3 categories and define what “healthy” data is. Use metrics here for the pillars and ensure they reflect the needs of each department: marketing, sales, operations, and your stakeholders.

Observability metrics you could use include table uptime, latency, error rates, status update rates, and quality scores.

Now, you’ll have clear standards for freshness, volume, distribution, schema, and lineage for analytical, operational, and customer-facing data. And a complete understanding of how they support your key processes. Everything needs to work in tandem.

Step 3: Choose the right tools

Now, you can select tools to observe your data. Choosing a cloud data solution with built-in observability software is an option. Just make sure everything integrates seamlessly with your current data stacks and infrastructure.

Leveraging tools that automate repetitive tasks is recommended and a quick win for improved data. This will free up your teams so they can focus on higher levels of analysis rather than validating data or detecting inconsistencies. 

Step 4: Set up monitoring and alerting

You can now use the dashboards within your selected tools to set up monitoring and altering systems. Define the thresholds for alerts based on the metrics you defined for each pillar earlier. The alerts will then be triggered when anomalies are detected, and relevant teams will receive an automated notification to act on them. 

Step 5: Create a data observability culture

It’s not just about the right tools. You also need to oversee a cultural shift towards cross-team data observability to be proactive rather than just reactive.

Start the process by training employees. This will address skill gaps and ensure they fully understand observability tooling. You should also promote transparency in data processes across departments so everyone knows what data assets must look like throughout the pipeline.

Data observability best practices 

Now that you have a data observability framework, how do you keep it in place and effective in the long term? These observability practices can help:

1. Start with high-impact pipelines

Which data pipelines have the biggest impact on your business? Start with those, make sure they are healthy and accurate, and then build out your observations. 

2. Run checks

Data observability isn’t a one-and-done task. You need to establish a routine to check the health and performance of data pipelines periodically. For critical business data, we recommend running real-time or hourly monitoring. On the other hand, archival data might only need a quarterly check.

Tailor your quality assurance to the criticality of the data. And use automation to schedule and run checks, reducing the need for manual interventions.

3. Regularly audit

Monitoring is essential, but sometimes you need to intervene. Regularly auditing data will identify unused dashboards or logging entries that need to be deleted. Getting rid of clutter will keep your observability solution streamlined and efficient. 

4. Build better pipelines

You can also use a data observability platform to create more stable and effective pipelines over time. This is critical as the volume of data grows and your data infrastructure expands. 

Future trends in data observability 

The concept of data observability was first created in 2019. It is a young discipline, one that is naturally evolving at a rapid pace. What developments can we expect during the next 5 to 10 years?

1. AI and ML-powered observability

Artificial intelligence and cloud computing will shape the immediate future of data observability. Manual processes have already become largely redundant due to automation. The next wave of AI-driven machine learning capabilities promises to take things a step further by not only detecting anomalies but also taking corrective action based on predictive analytics to keep systems running smoothly. 

2. Cloud-native observability

According to Gartner, the cloud will host 95% of new digital workloads by the end of 2025. Data observability must now adapt to the demands of cloud environments. In the future, tools will need to scale dynamically to adapt to changes in workloads while covering serverless architectures, microservices, and other complex systems for true, full visibility. 

3. Data observability for IoT

The scale of tracking data will also explode. With Internet of Things (IoT) devices collecting a mass of data, edge processing will be required to monitor and catalog data before it overloads central data infrastructures. Observability tools must all ensure the accuracy and integrity of IoT data across varying network conditions while combatting vulnerabilities and security threats.

Summing up

Think of data observability as a strategic asset. By running the rule over all of your data systems, you can enhance the resilience of your business and lay the groundwork for innovation and growth. A world where you maximize the value of your data, where no opportunities are lost, and regulatory or market changes don’t threaten your core processes.

Are you ready to transform your data? Recordpoint’s cloud-native cloud platform will give you all the tools you need to identify, manage, and use your data effectively to drive operational gains. Our powerful platform excels in data discovery, data categorization, data governance, and more.

Take action and contact us today to start observing your data. It could transform your business.

FAQs

What is observational data?

Observational data is data obtained through the process of observing an event or behavior. It’s often collected during research, studies, and other forms of real-world analyses.

What is the difference between data observability and data monitoring?

Data monitoring is on a much smaller scale, focusing on tracking metrics to inform campaigns. Data observability provides deeper insights into the health of broader systems, not just data.

Is data observability part of data governance?

Yes, a data observability solution supports data governance by ensuring the security and integrity of data so it meets compliance. 

What is the difference between data quality and data observability?

Data quality complements data observability. Data quality focuses on specific qualities of data: accuracy, completeness, consistency, etc. This informs data observability, which monitors and investigates large-scale data pipelines and systems to determine their health and performance.

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