Thought leadership
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January 9, 2025

Data Leaders’ 2025 Toolkit: Must-Have Observability Resources

With the complexity of enterprise data pipelines, ensuring trust in data is crucial. But where do you start?

Adrianna Vidal
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Data leaders know: the stakes for reliable, high-quality data have never been higher. As we head into 2025, ensuring your data observability strategy is dialed in can make or break your team’s success.

To help you cut through the noise and get right to the essentials, we’ve pulled together a list of resources that cover all the bases—from understanding the basics to proving ROI. Here’s pretty much everything you need to know:

1. The Data Observability Dictionary

Data observability comes with its own set of buzzwords and technical jargon. To get everyone on the same page—from your data engineers to your business stakeholders—you need a solid understanding of the terminology.

What’s inside?

  • Definitions of key terms in data observability
  • Simplified explanations for complex concepts
  • A resource that’s great for onboarding new team members or brushing up on the latest lingo

Read the dictionary

2. Standalone vs. Embedded Tools for Data Observability

When it comes to selecting the right approach for your organization, the decision often boils down to standalone tools vs. embedded solutions. Both have their pros and cons, and understanding them is critical to making the right choice.

What you’ll learn:

  • The differences between standalone and embedded tools
  • How to assess which approach aligns with your organization’s goals
  • Key factors to consider before making a decision

Explore the guide

3. RFI Template for Data Observability Platforms

Evaluating data observability platforms can feel overwhelming. What features should you prioritize? How do you compare platforms effectively? This RFI template takes the guesswork out of the process.

What’s included?

  • A customizable Request for Information template
  • Key questions to ask vendors
  • Guidance for aligning platform capabilities with your enterprise needs

Download the template

4. Data Observability Team Deployment Guide

So you’ve chosen your data observability platform—what’s next? Successful deployment requires thoughtful planning and alignment with your team’s workflows. This guide shows you how.

What you’ll discover:

  • Step-by-step instructions for deploying a data observability platform
  • Tips for ensuring cross-team alignment
  • Best practices for scaling your implementation

Check out the guide

5. How to Measure the ROI of Your Data Observability Platform

You’ve made the investment, but how do you prove its value? Measuring ROI is essential for gaining buy-in and demonstrating the impact of data observability on your organization.

What’s covered?

  • Five key metrics to track
  • Examples of how data observability drives tangible results
  • Tips for communicating ROI to stakeholders

Learn how to measure ROI

6. The Ultimate Guide to Data Observability

Looking for a comprehensive, all-in-one resource? The Ultimate Guide to Data Observability is your go-to for everything from foundational principles to advanced strategies.

What you’ll find:

  • An overview of data observability and its importance
  • Best practices for implementation
  • Insights into the future of data observability

Download the full guide

Ready to Level Up Your Data Observability Strategy?

With these resources in hand, you’ll be well-equipped to implement data observability in 2025. Whether you’re building a business case, deploying a platform, or measuring success, these guides are here to support you every step of the way.

Curious about how Bigeye can help you? Book a demo and see how our platform makes reliable, trustworthy data a reality.

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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