Monday, April 22, 2024

Data Observability vs Data Monitoring: 3 Key Differences

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Data observability and data monitoring share a goal of helping you understand how your data pipelines are working to deliver information to end users and how well they are functioning. Each process takes a different approach to meeting the end goal.

Data monitoring is the first step toward making your data observable and gives a basic awareness of the data system to answer retrospective questions after an incident—what caused an application to crash, for example. Data observability contributes to the overall health and long-term stability of your data system by helping you understand how well everything is working in your pipeline even before an issue occurs.

In short, data monitoring is the first line of defense against errors or misconfigurations, helping you pinpoint anomalies, while data observability goes beyond basic checks to help predict red flags before they become emergencies or cause downtime.

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Data Monitoring: Definition and Operation

Data monitoring is the continuous process of tracking and analyzing the performance of your data systems. Generally the process works by continuously scanning data against a predefined threshold and triggering an alert every time a breach or anomaly happens or a data system malfunctions. Data monitoring usually involves the following processes:

  • Data Ingestion: Understanding where your data comes from or where it was ingested, and transforming into a usable format.
  • Data Cleaning: Making sure your data is accurate, reliable, and up-to-date through validation, profiling, and replacement.
  • Data Security and Governance: Ensures your data is secure and that governance practices are in compliance with industry regulations.
  • Error Monitoring: Identifies and addresses errors in the data pipeline.

Data Monitoring includes a number of other processes.

Data Observability: Definition and Mechanics

Data observability offers 360-degree visibility into the health, processes, and stability of your data pipeline. It contributes to keeping your data quality robust and even enables self-service and advanced analytics for your data systems. A data observability platform has five pillars to analyze system performance and offer actionable insights into your data health:

  • Lineage: Tracking the complete picture of your data infrastructure to help answer such questions as “Where does the data come from?” and “How has it been transformed throughout the pipeline?”
  • Schema: Establishing ownership and monitoring for schema drift every time the data format or structure changes unexpectedly.
  • Freshness: Using metrics like data latency to check how up-to-date datasets are, this pillar focuses on data timeliness, checking for delays and gaps, and ensuring pipelines are delivering fresh information.
  • Data Distribution: Identifying the spread of your dataset to ensure the data is within the threshold range.
  • Volume: Examining the completeness of your data by visualizing unexpected spikes, or downswing in data volume.

Data observability solutions can automate monitoring and tracking across your organization’s entire data estate to alert you to anomalies or other inconsistencies before they become issues, and often include data monitoring features.

Read our picks for the top 7 data observability tools for 2024.

Data Observation is more complex than data monitoring.

Similarities and Differences: Observability vs Monitoring

Beyond a shared goal, observability and monitoring have a range of other similarities—for example, they both contribute to transparency and improve collaboration between stakeholders, including data, development, operations, and security teams. But they differ in many ways. Here’s a look at what they have in common and what they do not.

Similarities

The following are the most common similarities between observability and monitoring:

  • Shared Telemetry Data: Both processes use the same kind of telemetry data to firefight and detect anomalies, including metrics, logs, traces, and application health checks—observability provides more context but the basic debugging workflows remain fundamentally similar.
  • Similar Audience: Both processes cater to data engineers, business analysts, system administrators, and data scientists.
  • Reliance Upon Metrics: Both rely heavily on metrics and system indicators such as API latencies, database query times, and job success rates to gauge data performance stack.

Differences

Here are the most common differences between observability and monitoring:

  • Silos vs. Visibility: Data monitoring usually works in silos and generally only uncovers issues in individual components, while observability offers data-driven visibility into all moving parts of your data lifecycle, from data collection to usage, archiving, and destruction.
  • Metrics vs. Context: While both rely on metrics, data monitoring is metric-oriented while observability is contextual and goes beyond surface-level analytics and uses techniques like anomaly detection, logs, and traces to proactively identify performance bottlenecks.
  • Effects vs. Causes: Monitoring might alert you about a sudden dip in conversion rates or surge in website traffic so you can take remedial action, while observability looks for red flags or patterns to reveal the root cause of a glitch.

Why It Matters: The Impact on Your Data Systems

At its core, data monitoring and data observability both focus on minimizing service disruption and bolstering revenue generation. Here’s a look at how they affect your data systems.

Improved Efficiencies and Lower Costs

Only 8 percent of companies achieve their business goals with their current data pipelines, an inefficiency that often stems from limited resources and unlimited data requirements. Without observability, investments will keep suffering from low return and remain unsustainable, and won’t be able to match the required resource-cost optimization ratio. This also means teams need observability to divert attention from over-the-surface work to more value-laden, qualitative data projects.

Observability also saves money and engineering effort. Globally, data teams spend 40 percent of their time trying to understand whether the data that flows through their data pipelines is accurate and reliable enough to base decisions on. Observability cuts through that noise through proactive monitoring and data-driven debugging.

More Reliable Data

Currently more than 54 percent of data and analytics professionals lack trust in the data they use for critical decisions. Data observability identifies anomalies upstream before they reach your data warehouse or data lake and contaminate all your data, which builds trust in your data sets so you can make decisions with data-driven confidence.

A multidimensional data observation platform allows you to track the journey of data from its origin to its final destination and remove any instance of flawed, falsified, or irrelevant data that might hamper your team’s decision-making capabilities. Observability removes the uncertainty and replaces it with a clear picture of the organization’s health and performance.

Faster Data Transit

Having visibility into your data systems and processes allows for faster root cause analysis using actionable data, which can minimize downtime and disruptions that bring risk. It also means less time scrambling to fix “unknown unknowns” in your data pipeline.

Better End User Experience

Data observability doesn’t just benefit data specialists—by making data health and access more transparent, it lets more employees become informed data consumers. An improved employee experience also translates to reduced talent churn, less time and money spent on training, and a workforce finding time for growth and innovation.

3 Real-World Examples of Observability vs Monitoring & Top Tools

Observability tools help teams resolve data incidents faster by tracking data flow, query detection, and improving data visibility across the pipeline. Here are four real world examples of how companies are using observability tools.

BlaBlaCar

BlaBlaCar, known around the world for its carpool and bus line concept, was struggling with issue resolution times caused by capacity issues stemming from poor data quality. The company’s broken pipeline led to recurring data incidents with more than 200 hours of potential progress—opportunity cost—lost per quarter.

BlaBlaCar used observability tool Monte Carlo to reduce its resolution time by 50 percent with data-driven visibility and an automated alerting system. BlaBlaCar also used Monte Carlo’s data lineage feature to map data dependencies during its transition to a data mesh architecture, ultimately leading to improved data quality.

Puma

Design company Puma’s limited data monitoring capacity was causing it to lose more than $100,000 across 45 websites in a single downtime incident. The team lacked insights into why sites were continuously crashing, and most business critical operations were spiraling beyond its control.

Puma began using observability tool Splunk to unlock missed sales opportunities by pinpointing issues that caused failed orders or declined credit cards. The company identified issues much faster and cut resolution time down from hours to 15 minutes using automated data management, real-time log analysis, and data capture of user touch points before a downtime or crash.

loanDepot 

U.S.-based financial institution loanDepot used Dynatrace’s observability platform to automate fixes in its customer journey, audit third-party integration to look for negative impact on the data pipeline, and enable single pane monitoring to make faster data-informed decisions. Dynatrace also safeguards loanDepot’s cloud migration so that critical applications like loan origination can keep running smoothly, even as the company shifts to the hybrid cloud.

Steps to Implement Observability and Monitoring Strategies

A strong data observability and monitoring culture is at the heart of the modern data stack, offering deep insights into your system’s health, optimizing data performance, and ensuring the overall integrity of the data infrastructure. Here are seven steps to take when implementing such a culture at your organization.

Create an Effective Monitoring and Alert System 

Identify business-critical KPIs with established thresholds to track. Focus on metrics like completeness, accuracy, and consistency to measure and track data integrity.

Use AI Automation Wherever Needed

The idea is to reduce human efforts on tedious, and no-brainer tasks. Start with automated data validation to catch bad data, and move toward creating self-healing pipelines.

Decentralize Your Data Observability Process

Make data insights accessible to everyone in the organization. Break down knowledge silos and dismantle tribal knowledge by training your staff on data management to enable data-driven decision-making at all levels.

Customize Alerts to Reduce Distractions

Alert fatigue is real, and too often, data engineers leave deep work to engage with alerts that might be redundant or unnecessary in the first place. Customize alerts for critical events like downtime or zero-day attacks early on so your team is not constantly context switching. You can also integrate automation tools to trigger specific actions based on alerts. This might involve data pipeline restarts, data quality checks, or automated remediation procedures for common issues.

Limit Your Telemetry Footprint

Nothing beats the ROI of a cost-effective monitoring and observability strategy for your data infrastructure. A reduced telemetry footprint is the fastest way to achieve this goal. Monitor the amount and type of telemetry data (metrics, logs, traces, events) your organization generates to pinpoint areas where cost savings might be possible. Review tuning options regularly to optimize telemetry volume captured and processed in a scheduled period.

Add Context with Metadata

Make your observability platform context laden. Use metadata—user ID, application version, server name, for example—with your telemetry data. Context helps weed out noise and highlight what truly matters. Without it, an unprocessed data stream might do more damage than good to your decision-making process.

Audit Your Data Pipeline

Regular and recurring audits of your data pipeline can pave the way for early detection of potential bottlenecks and help your team embrace a culture of continuous improvement.

Bottom Line: Observability vs. Monitoring

Even the best data pipelines are not foolproof. As they become more intricate, and as they start to handle larger amounts of data, they are prone to inefficiencies that lead to data quality loss and entropy. While monitoring and observability processes aren’t a silver bullet for data problems, they improve your chances of preventing and correcting them when they do occur. Effective observability hinges on collecting the right data, responding in a timely manner to issues, and making iterative improvements—especially during the initial phases of setting up data pipelines—with the ultimate goal of delivering value to end users with qualitative data and limited resources.

Data observability and data monitoring are both components of a larger data management program, which is essential to any enterprise data efforts. Learn more about the types and challenges of data management, read why it’s important, or discover the data management platforms being implemented by top companies.

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