Data observability: the key to trustworthy revenue data

Your sales forecasts look great on paper until the quarter ends, and the numbers don’t match reality. Deals slipped through the cracks, lead scores were off, and your AI-driven recommendations felt more like guesses than insights. The culprit? Bad data.

In AI-powered revenue operations, even small data errors compound quickly. A missing field in your CRM can throw off pipeline health, outdated contact data can waste weeks of outreach, and unreliable reporting erodes trust across sales, marketing, and customer success. Without strong visibility into your data’s health, AI models end up making decisions based on incomplete or inaccurate information.

That’s where data observability comes in. Think of it as a real-time health check for your data pipelines, ensuring clean, trustworthy information flows across your systems so your forecasts, automations, and AI strategies actually deliver.

What is data observability? 

Data observability is the ability to fully understand, monitor, and trust an organization’s data. It’s like having a real-time health check for your data pipelines, ensuring that clean, high-quality information flows through your business systems without a hitch. 

In sales environments, where real-time insights fuel decision-making, having strong data observability means your AI models and analytics tools can function at their best, driving smarter strategies and better results. 

Why is data observability important? 

When it comes to AI-powered sales and marketing, data is the fuel that keeps everything running smoothly. Data privacy and compliance practices can shape the data health of a company. But what happens when that data is inaccurate, outdated, or incomplete? 

According to the 2024 Observability Forecast by New Relic, organizations that prioritize observability experience a 4x median ROI on their investments, 79% less downtime per year, and 48% lower outage costs compared to those without full-stack observability.

Flawed data leads to flawed predictions, which means unreliable sales forecasts and missed opportunities. That’s where data observability comes in—it acts as a safeguard, ensuring your data is always clean, accurate, and trustworthy. 

With strong data observability, businesses can: 

• Avoid AI failures: AI models rely on high-quality data. Observability ensures your models aren’t making decisions based on bad information. 

• Improve sales forecasting: Clean, real-time data leads to more accurate revenue predictions and better decision-making. 

• Prevent costly errors: Catching and fixing data inconsistencies early helps avoid billing mistakes, misaligned sales goals, and poor customer experiences. 

• Enhance automation & lead scoring: Reliable data strengthens AI-driven sales automation, making prospecting and lead qualification more precise. 

AI-powered sales tools rely on clean, real-time data. They help optimize prospecting, improve forecasting, and automate key sales processes. But if your data is outdated or inaccurate, your AI models and sales strategies can take a serious hit. That’s where data observability steps in—it ensures your AI systems get the clean data they need to perform at their best. 

How data observability differs from other data management practices 

While often confused with other data management practices, data observability is distinct in its proactive approach to monitoring and improving data quality. In this section, we’ll walk through how data observability differs from other data practices in four key areas: monitoring, testing, quality, and governance. 

Data observability vs. data monitoring 

Data monitoring involves tracking predefined metrics and thresholds, while data observability provides deeper insights into the causes of anomalies and data inconsistencies.  

Observability tools are much more proactive, while monitoring is reactive. The truth is that sales teams need both to detect and resolve data quality issues before they impact decision-making. 

Data observability vs. data testing 

Data testing and data observability both help maintain data integrity, but in different ways. Data testing runs predefined tests at specific stages to catch known issues, like ensuring customer data is formatted correctly before entering a CRM. While effective for spotting expected problems, it doesn't catch surprises.  

Data observability, however, continuously monitors data pipelines to detect anomalies and unexpected issues in real time. It's like comparing a checklist to an intelligent system that identifies problems before they cause major disruptions. Using both ensures a reliable data infrastructure that supports AI-powered decision-making. 

Data observability vs. data quality & reliability 

While data quality focuses on ensuring that data is accurate, complete, and fit for use, data observability takes it a step further by providing continuous monitoring of data health at scale. It ensures that data quality is maintained in real-time, even as it moves through complex pipelines. With observability in place, teams can detect and address issues before they disrupt AI-powered sales workflows.  

Data observability vs. data governance 

Data governance lays the groundwork for how data should be managed, ensuring that policies, security measures, and compliance requirements are in place. However, governance alone doesn’t guarantee clean, reliable data.

Data observability acts as the enforcement mechanism, providing transparency into how data flows through an organization and ensuring that it adheres to governance policies. Together, they create a comprehensive framework for data integrity and compliance. 

The 5 core pillars of data observability

To fully understand data observability, it's important to break it down into its five core pillars. Each of these plays a crucial role in ensuring data integrity and reliability. 

1. Freshness – Ensuring data is up-to-date 

Freshness refers to how recently data has been updated. In AI-powered sales tools, real-time data ingestion is key for accurate lead scoring, personalization, and forecasting. For example, if a sales team relies on AI to recommend leads but the data is a week old, the AI might suggest contacts who have already made a purchase or moved on, leading to missed opportunities and incorrect sales projections.  

2. Volume – Monitoring data flow & consistency 

Volume tracking ensures that the expected amount of data is flowing through systems without sudden spikes or drops. For sales teams, missing or duplicated data can cause reporting errors, impacting key decisions around revenue projections and customer engagement strategies. 

3. Schema – Detecting changes in data structures 

A schema defines how data is structured, and even minor changes, like a missing column or format shift, can break AI models and analytics tools. Data observability tools detect these changes before they disrupt workflows, preventing costly downtime and operational inefficiencies. 

4. Lineage – Tracking data movement from source to use 

Data lineage provides visibility into where data comes from, how it's transformed, and where it’s used. For example, a sales AI platform might pull customer interactions from multiple sources, like emails, CRM logs, and chat transcripts, before analyzing them to suggest the next best action.  

5. Data quality & distribution – Ensuring accuracy & completeness 

AI models perform best when trained on high-quality, diverse datasets. However, a 2024 survey by Grafana Labs found that most teams are using multiple observability tools—on average, 62 tools, with 70% relying on four or more. 

This fragmentation can make it harder to maintain consistent data quality. Tools like Outreach’s AI-driven data insights help address this by assessing data quality and distribution, identifying gaps, inconsistencies, or biases that could lead to misleading insights or inaccurate predictions. 

The business benefits of data observability 

Data observability is a business enabler. By ensuring data integrity and reliability, organizations can optimize operations, improve decision-making, and drive revenue growth.

Enhances AI-driven sales & forecasting 

AI-driven sales predictions are only as accurate as the data that powers them. Without strong data observability, models may rely on incomplete, inconsistent, or outdated information—leading to missed revenue opportunities, incorrect demand forecasts, and ineffective sales strategies. 

With data observability, organizations can: 

• Improve forecast accuracy by detecting and resolving data inconsistencies before they skew predictions. 

• Enhance AI trustworthiness by maintaining clean, reliable datasets for machine learning models. 

• Optimize sales performance by ensuring that AI-driven recommendations are based on real-time, high-quality data. 

By integrating observability tools, sales teams gain confidence in AI-driven insights, leading to smarter prospecting, better quota planning, and higher conversion rates.

Streamlines data operations & reduces firefighting 

Let’s be honest, no one likes putting out data fires all day. When data breaks, teams waste hours (or even days) tracking down the root cause, fixing errors, and dealing with frustrated stakeholders. It’s stressful, inefficient, and pulls focus from high-value work. 

With automated data observability, those late-night troubleshooting sessions become a thing of the past. Instead of manually sifting through logs, teams get real-time alerts when something looks off—so they can fix issues before they snowball. 

With automated data observability: 

• Engineers and analysts spend less time firefighting and more time on strategic initiatives. 

• Data pipeline issues are detected in real-time, preventing errors from compounding downstream. 

• Business leaders receive timely, accurate insights without operational bottlenecks 

By implementing automated anomaly detection, data lineage tracking, and alerting systems, organizations reduce inefficiencies and empower teams to focus on growth instead of fixing broken data pipelines. 

Enables proactive data issue detection & faster resolutions 

Data errors can have significant business consequences—from lost revenue to AI decision-making failures. Whether it’s inaccurate sales reports, misleading AI predictions, or compliance risks, data issues can quietly cost your business money and credibility. 

That’s why catching problems early is a game-changer. With strong data observability, you’re not just reacting to issues; you’re preventing them. 

• AI stays reliable, and early anomaly detection means your AI-powered sales forecasts and recommendations aren’t thrown off by bad data. 

• No more last-minute fire drills, automated alerts flag inconsistencies before they become major problems. 

• Compliance headaches are avoided, with better visibility into your data, meeting regulatory requirements becomes a lot easier. 

By identifying and addressing data quality issues early, organizations can minimize financial risks, safeguard AI reliability, and maintain a competitive edge. 

How to implement data observability 

Implementing data observability might sound daunting, but breaking it down into actionable steps can make the process straightforward and effective. Here's how you can get started: 

Step 1. Understand your data infrastructure 

First things first: get to know your data landscape. Identify where your data comes from, how it flows through your systems, and where it ends up. This means mapping out all your data sources, integrations, and endpoints. By doing this, you can spot any weak spots or areas lacking visibility.  

Step 2: Choose the right observability tools 

Once you've mapped your data infrastructure, it's time to select tools that fit your organization's needs. Look for platforms that offer real-time monitoring, anomaly detection, and data lineage tracking. 

Outreach, for example, provides an all-encompassing solution as the only workflow-first AI Revenue Workflow Platform, empowering sales, customer success, and RevOps leaders to operate efficiently and effectively with a repeatable, scalable process.

Step 3: Set up monitoring & alerting systems 

With the right tools in place, establish monitoring and alerting mechanisms to keep an eye on your data's health. Set up real-time alerts for anomalies like unexpected data spikes or schema changes. This proactive approach ensures that your team can address issues before they escalate, maintaining trust in your data and preventing potential disruptions.  

Bonus tip: Set up a real-time dashboard to easily spot any issues with your data. It’s a great way for the team to stay on top of things and keep everyone aligned on what matters most. 

How Outreach helps with data observability

Outreach's unified AI Revenue Workflow Platform eliminates the data observability challenges that plague teams managing 4-6 disconnected revenue tools. Instead of monitoring multiple fragmented pipelines, you get complete visibility through our integrated architecture.

Unified data collection through the data cloud

The foundation starts with our four-layer Data Cloud, where every email, call, meeting, and CRM update flows through one system. Our Smart Data Enrichment Service adds third-party intelligence from ZoomInfo and SalesIntel through pre-built connectors, ensuring consistent data quality without manual reconciliation or conflicting records.

AI agents that monitor data health

Our AI agents actively improve data quality while executing workflows. 

• Deal Agentautomatically detects missing MEDDPICC fields and anomalies during deal progression, flagging risks before they impact forecasts. 
• Research Agent continuously validates and updates account intelligence, ensuring data freshness. 
• The Revenue Agent performs prospecting tasks, such as enriching accounts and prospects with insights, identifying new prospects within existing accounts, and discovering net new accounts and prospects with relevant insights and data signals. It also has built-in AI personalization capabilities to help you craft personalized messaging for each prospect

Real-time anomaly detection

The platform provides immediate alerts when data quality issues arise, such as volume drops, schema changes, or enrichment conflicts. Our traffic-light health scoring makes complex patterns instantly visible: green for healthy progression, yellow for emerging risks, red for critical issues. 

Enterprise-grade governance ensures complete data lineage tracking and SOC 2 compliance. By consolidating your revenue stack, you're ensuring every AI model trains on complete, trustworthy data. We're also the first in our industry to achieve ISO 42001 certification – the world’s first international standard for AI Management Systems, further reinforcing our commitment to responsible, auditable AI.

Turning data integrity into sales confidence

As businesses embrace AI for smarter decision-making, predictive analytics, and pipeline management, data observability will become the secret sauce to keeping things running smoothly. The best part? It’s not just for data teams anymore—sales, marketing, and revenue operations leaders are getting in on the action too. 

That’s where Outreach comes in. With our AI Revenue Workflow Platform, you can harness the power of clean, reliable, and actionable data to fuel AI-driven sales strategies. 

FAQs about data observability 

This section is designed to answer additional common questions about data observability, helping you gain a more comprehensive understanding of its importance and how it can benefit your organization. Whether you’re new to data observability or looking to sharpen your knowledge, we’ve got you covered. 

What is an observation in a data set?

An “observation” in a data set refers to a single data point or record; for example, one customer purchase, one website visit, or one support ticket. Observational data is any collection of these points captured without interference, such as tracking customer behavior in your CRM. 

While this is different from “data observability,” the two concepts are connected: observability ensures those individual observations are accurate, timely, and usable for AI models and revenue forecasting.

How does data observability impact revenue operations? 

Better data quality is the backbone of strong forecasting, accurate pipeline tracking, and AI-driven sales execution. By ensuring that data is reliable and up-to-date, revenue operations teams can make more informed decisions, optimize sales strategies, and drive better business outcomes. 

How does data observability improve AI model accuracy? 

AI models thrive on high-quality, real-time data. Data observability guarantees that data is fresh, complete, and consistent, which reduces errors and boosts the accuracy of AI-driven activities like sales forecasting, lead scoring, and customer engagement. The result? More precise predictions and smarter sales execution. 

What industries need data observability the most? 

While many industries benefit from data observability, those that rely heavily on AI and automation, such as SaaS, finance, healthcare, and e-commerce, stand to gain the most. These industries need high-quality data to ensure their systems run smoothly, predictions are accurate, and customer experiences are optimized. 

Can small businesses benefit from data observability? 

Absolutely! Any business utilizing AI, automation, or analytics can benefit from improved data reliability. For small businesses, having trustworthy data ensures that decisions are based on accurate information, which can make a huge difference when optimizing sales, marketing, and customer operations. 

With these answers in mind, you now have a clearer understanding of how data observability can give your organization a competitive edge. Ready to take your sales execution to the next level? Let’s get started!