Data Quality & Observability: The New Strategic Language Between Business and IT

In the new digital paradigm, data and software are no longer just support components—they are the beating heart of business value, the connective tissue linking products, customers, operations, and strategic decisions. Yet most organizations continue to underestimate a critical risk: operating with unreliable data and non-observable systems. This doesn't just lead to inefficiencies—it creates invisible but deep fractures between business layers—strategy, operations, and technology—that result in delayed decisions, loss of internal trust, reputational risks, and missed opportunities.

In this context, the synergy between Data Quality, Observability, and Site Reliability Engineering (SRE) is no longer a technical option reserved for IT teams: it has become a strategic and cross-functional necessity. This convergence enables a shared language among CEOs, CTOs, operational teams, and data teams—where the same signals (KPIs, reliability metrics, performance trends, anomalies) are interpreted consistently and in context across all business functions.

Only through this integration can organizations shift from a culture of "isolated insights" to one driven by shared signals and traceable decisions. In other words: visibility and trust in data become competitive assets, enabling responsiveness, collaboration, and scalability. When business and engineering look at the same system with aligned metrics, every decision—from a feature release to a strategic review—is grounded in common, verifiable, and measurable truths.

1. From Digital Transformation to Operational Transparency

Many companies claim to be “digital,” but what is often missing is the ability to clearly and promptly see what is happening inside their own systems, processes, and data flows. Having a digital platform or an app does not equate to being truly digital. Today, real digital transformation is not measured by the amount of technology adopted, but by the organization’s ability to seamlessly connect strategy, operations, and technology through transparency.

Operational transparency means:

• Seeing what is happening in systems in real time

• Understanding the business impact of a technical anomaly or data error

• Acting quickly, based on informed and coordinated responses

To achieve this, three interdependent pillars are required:

• Reliable data → Without a solid data foundation, metrics mislead, dashboards deceive, and decisions are based on subjective interpretations. Data Quality ensures consistency, accuracy, and traceability, reducing the risk of poor decision-making.

• Transparent systems → It’s not enough to know something went wrong—you need to know where, why, and how to prevent it. Observability provides deep, contextual visibility into the behavior of applications, data flows, and infrastructure.

• Rapid and systemic responses → Errors must be detected, contextualized, and mitigated quickly. This is where SRE comes into play, combining automation, reliability, and impact metrics (SLOs, SLIs) to turn technical events into coordinated operational responses.

Digital transformation is not about the passive adoption of technological tools—it’s about building an ecosystem where digital signals are readable, reliable, and actionable across the entire organization.

It’s the shift from a reactive, fragmented system to an organization that operates with visibility, consistency, and speed.

2. Data Quality: Much More Than a Data Team Concern

One of the most common mistakes companies make is treating data quality as an issue limited to BI, AI, or Data Governance teams—something to be handled downstream, often seen as mere “clean-up” or “correction” after the fact. But this view is not only incomplete—it’s harmful.

In today’s digital world, every system, service, or application is simultaneously a producer, transformer, and consumer of data.

• The frontend collects user inputs that become events and metrics

• The backend processes, stores, and exposes them to other services

• ETL pipelines transform them for analytical purposes

• AI models interpret them to generate predictions

• Dashboards visualize them to support operational or strategic decisions

In this chain, a single weak link can compromise the overall reliability of the data.

The most common causes of poor data quality include:

• Duplicated logic across systems and teams, leading to inconsistencies

• Lack of validation at data entry points (frontend, APIs, ingestion)

• Opaque pipelines with undocumented or unversioned transformations

• Lack of ownership, where no one is accountable for data correctness or freshness

• Absence of continuous monitoring, allowing silent errors to persist in production for days or weeks

The result? Contradictory reports, unstable metrics, flawed AI predictions, useless alerts, and business decisions based on incorrect numbers.

The truth is simple: Data Quality is not a task—it’s a shared responsibility. It must become a cross-functional pillar, embedded in the software lifecycle just like testing, security, and performance.

This means:

• Introducing semantic data validations at the code level

• Integrating data quality tests into CI/CD pipelines

• Recognizing that inconsistent data is a bug in its own right

• Assigning data ownership to the teams that generate or modify it

• Equipping teams with data observability tools to detect anomalies, outliers, and schema breaks in real time

A culture of Data Quality doesn't just improve analytics and reporting—it boosts the reliability of the entire digital system, reduces operational risks, and strengthens the effectiveness of every decision, from a released feature to a boardroom strategy.

3. Observability: Not Just for SecDevOps, but for the Entire Business

In the common mindset, observability is often confined to the technical domain: logs, metrics, dashboards, and alerts for DevOps and SRE teams. But this view is limiting. In reality, modern observability is a critical function for the entire business, as it represents the organization's ability to understand—in real time—what is happening within its systems, data, and user interactions.

It’s no longer just about “monitoring if a server is up.” It’s about knowing what is happening, why it’s happening, where it’s happening, and what business impact it has.

The three key dimensions of modern observability:

A. Technical Covers the classic elements:

• Logs, metrics, distributed traces

• System events, application errors, resource consumption

• Intelligent, correlated alerts

Goal: Understand what’s happening within software and hardware components.

B. Functional Makes product dynamics and user experience observable:

• Feature performance

• Conversion funnels

• Clickstream, retention, user journey failures

• A/B testing and monitored rollouts

Goal: Understand the real-world impact on user behavior and the effectiveness of released features.

C. Data Often the most neglected, but increasingly the most critical. Includes:

• Data integrity and completeness

• Propagation delays

• Semantic or schema anomalies

• Failed validations or inconsistencies across sources

Goal: Ensure that the data being used, displayed, or analyzed is reliable, up-to-date, and consistent.

True observability is cross-functional: it connects code, processes, users, and data. When even one dimension is missing, blind spots arise.

A real-world example: A report displays incomplete values. Technically, there’s no observable issue—no errors in the logs, no alerts. The bug is in the data: an ETL pipeline skipped the nightly refresh due to an untracked semantic anomaly. Without observability across data + functional + technical layers, teams are left guessing, investigating blindly, wasting time—and trust.

From Technical Tool to Operational Truth Platform

A strong observability strategy:

• Prevents blame games between teams

• Reduces mean time to detection and resolution (MTTD, MTTR)

• Builds a culture of shared accountability

• Provides actionable insights—not just raw numbers

This is what makes it strategic for the business. If teams can:

• Correlate system metrics with user impact

• Read data integrity in real time

• Understand the root cause without manual escalation

…then the business becomes faster, safer, and more proactive.

Observability is not a support tool—it’s the nervous system of the digital enterprise. It enables both technical and non-technical teams to work from shared, measurable, and verifiable signals.

4. Site Reliability Engineering: From Reactive to Predictive

In today’s digital ecosystem, system reliability is no longer just a technical responsibility—it’s a competitive asset. An unreliable platform means lost customers, damaged reputation, revenue loss, and in regulated environments, even legal risks and penalties.

Site Reliability Engineers (SREs) are the technical guardians of this reliability. But their role now goes far beyond simply “fixing incidents.” SREs are the architects of systemic resilience: they ensure that systems not only work, but continue to work under pressure, in dynamic, complex, and distributed environments.

From Reactive to Predictive

Traditionally, reliability was managed reactively: wait for something to break, then fix it. Today, thanks to the convergence of Observability, Data Quality, and automation, the approach has radically changed: SREs can now anticipate, prevent, mitigate—and directly contribute to business goals.

What SRE Teams Need to Operate with a Business-Driven Mindset

A. Clear SLIs/SLOs tied to real impact metrics

A.1 - Measuring generic uptime or CPU usage is no longer enough.

A.2 - Service Level Indicators (SLIs) and Service Level Objectives (SLOs) must align with user value, such as:

• % of failed orders

• Latency in the purchase funnel

• Average response time of customer support

• Success rate of critical API transactions

This shift turns reliability from a technical metric into a strategic KPI.

B. Metrics and traces aligned with system dependencies

B.1 - No system operates in isolation: it depends on internal services, external APIs, data flows, and even AI models.

B.2 - SREs must be able to correlate dependencies across the entire execution chain. A slowdown may not originate from “that service” but from an upstream dependency feeding incomplete data.

C. Smart alerting based on clean, contextualized data

C.1 - The “alert storm” is a well-known issue: too many signals, all equal, no clear priorities.

C.2 - A modern alerting approach relies on:

• Validated data (Data Quality)

• Dynamic thresholds

• Cross-signal correlation

• Context-aware alerts that indicate not just what happened, but how critical it is and where to act

A useful alert drives a concrete action—not just an investigation.

D. Tooling that supports drill-down and automation

D.1 - SRE teams are most effective when equipped with tools that allow:

• Visual navigation across events and traces

• Integrated root cause analysis (log, metric, data)

• CI/CD integration for automated rollbacks, remediations, and tests

A dashboard that’s just a snapshot slows you down. An interactive, automated one empowers you.

The Synergy with Observability and Data Quality

SRE cannot operate in isolation. Its effectiveness depends on the quality of the signals it receives:

• If the data is dirty, the alert is misleading

• If the system isn’t observable, root cause analysis takes longer

• If there’s no shared data culture, KPIs are disconnected from business reality

SRE + Observability + Data Quality = a shift from disaster management to reliability management.

From Technical Role to Strategic Lever

With the right tools and data, SREs become active business partners:

• Collaborating with product owners to define user experience-based SLOs

• Contributing to proactive security (SecReliabilityOps)

• Supporting scalability while maintaining control over operational risk

• Helping teams release faster—with greater confidence

It’s no longer just about “keeping the system running.” It’s about creating an environment where innovation is possible, safe, and scalable.

5. Shared Dashboards: The Foundation for Organizational Alignment

One of the most recurring issues in digital companies is the fragmentation of information: every team works with its own metrics, its own dashboards, and its own definitions of “success” and “critical issues.”

The result? Decisions are made based on misaligned—and often contradictory—signals.

A concrete example: User churn increases. The business team suspects a pricing or communication issue. In reality, technical analysis reveals a bug in the mobile payment process. But the discovery comes days later—because the product and engineering dashboards aren’t integrated.

This is a structural problem:

• Managers read aggregated, often “cleaned” KPIs

• Engineers analyze granular, operational metrics

• Data scientists detect anomalies in datasets that don’t appear on business dashboards

• The product team works with engagement numbers, unaware of the underlying data stability

This information asymmetry slows down action, fuels misunderstandings, creates frustration, and blocks the creation of a truly shared data-informed culture.

The Solution: Shared, Multi-Level, Interconnected Dashboards

Building a single source of truth—accessible to all but viewed at different levels depending on role—is one of the most powerful transformations for strategic-operational alignment.

Layered Structure of Shared Dashboards:

Shared dashboards foster cross-functional understanding, highlight dependencies, and enable faster, better-informed decisions—based on aligned, transparent signals that everyone in the organization can trust.

6. A New Strategic Framework: Business-Driven Observability

In today’s digital landscape, observability can no longer be confined to the technical domain. It must evolve into a cross-functional framework capable of connecting code, platforms, data, and strategic decisions in a continuous and verifiable flow.

Business-Driven Observability was created with exactly this goal: to enable a consistent interpretation of the entire business stack—from code commit to boardroom decisions. Every observable level becomes a point of truth, allowing teams to measure, explain, and improve.

The 5-Level Model

Deeper Dive into Each Level

L.1. Code – The foundation of reliability

• Clean, readable code supports debugging, observability, and automated testing

• Tools: linting, test coverage, code quality scores, static analysis

• Culture: teams are responsible for writing not just working code, but observable code

If the code is a "black box," every error becomes a mystery.

L.LL.2. Platform – The pipeline as a trust channel

• CI/CD must provide full traceability: who deployed what, when, where, and why

• Every release must be monitorable, reversible, and auditable

• Techniques: canary deployments, progressive rollouts, post-deploy validations

An untracked deployment is a doorway to uncertainty.

L.3. System – The core of classic observability

• Continuous monitoring of availability, errors, and performance

• User-oriented SLOs and SLIs—not just machine-centric metrics

• Distributed tracing to correlate events across complex architectures

If the system is a “silent system,” every issue becomes reactive.

L.4. Data – The new critical layer for business and AI

• Upstream validations, freshness monitoring, outlier management

• Observability across data flows: from sources to transformations, from APIs to AI models

• Explicit governance and ownership

Corrupted or incomplete data turns KPIs and AI into little more than a gamble.

L.5. Decision – The ultimate goal: data-informed decision-making

• Dashboards must be built on metrics traceable back to their source

• The business should never have to ask, “Where does this number come from?”

• KPIs with drill-down capabilities: from churn to feature, from feature to bug, from bug to commit

If leadership is making decisions based on opaque data, risk increases across every area: sales, compliance, investment.

Why This Model Is Different

Business-Driven Observability:

• Is not just an IT strategy—it’s a model for collaboration across roles and functions

• Goes beyond infrastructure—to include data and decisions

• Doesn’t just react to problems—it lays the foundation to prevent them

• Measures not just what’s measurable—but what’s useful to the business

Expected Impact of the Framework

True observability is not just about solving problems—It’s about enabling better decisions.

7. Measurable Results: What Changes with This Synergy

When Data Quality, Observability, SRE, and shared visibility work together in an integrated way, the organization shifts from a reactive, fragmented approach to a proactive, intelligent, and measurable operating model.

This synergy doesn’t just enhance technical quality—it transforms decision-making, boosts productivity, and builds trust across teams.

Here’s how this change plays out in key areas:

Incident Response

Impact: Reduced MTTR, less stress on on-call teams, improved service availability, and lower impact on end users.

Dashboard Quality

Impact: The business makes decisions based on data it trusts, reducing the risk of incorrect or misaligned actions.

AI/ML

Impact: More reliable models, better generalization, fewer production errors, and higher ROI from AI projects.

Hidden Costs

Impact: Hidden costs become visible and manageable metrics, giving greater control over reputation and profitability.

Collaboration & Culture

Impact: Stronger inter-team relationships, fewer conflicts, broader accountability, higher motivation, and better alignment with business goals.

The Value of Synergy

When reliable data, observability, automation, and shared visibility come together, companies don’t just solve problems better—they prevent them, understand them, and turn them into measurable value. This isn’t just about technical efficiency—it’s about operational excellence applied to business outcomes.

Every avoided error, every informed decision, every second saved in diagnosis becomes a tangible competitive advantage.

Observability, Data, and Reliability Are One Strategic Investment

It’s not enough to “do SecDevOps” or “launch an AI project.” You need to build a company culture grounded in visibility, reliability, and shared truth.

Companies that invest in:

• Data Quality by design

• Observability at every level

• Clean, traceable engineering

• SRE as a bridge between systems and business

…are laying the real foundation for innovation, trust, and scalability.

Conclusion: Observability, Data, and Reliability Are a Single Strategic Investment

In today’s landscape—where every company is challenged to compete in a fast-moving, digital, and distributed market—adopting advanced tools or launching superficial initiatives like “doing DevOps,” “implementing AI,” or “improving security” is no longer enough.

What truly makes the difference is not the technology itself, but the culture built around it. A culture grounded in visibility, reliability, and shared truths is not just good engineering practice—it is a strategic lever that impacts:

• The quality of decisions

• Internal and external trust

• The ability to innovate without compromising resilience

• The long-term sustainability of the business

Companies that invest holistically in these four pillars position themselves for real transformation:

A. Data Quality by Design

No more late-stage fixes or downstream data cleaning. A structured approach brings data quality into the core of architecture, process, and product design. Consistent, validated, and monitored data enables trustworthy decisions, effective AI, and continuous compliance.

B. Observability at Every Level

From CPU usage to feature effectiveness, from data integrity to strategic business impact. Observability is not just “advanced monitoring”—it is an organizational capability to see, understand, and act on everything that matters, quickly.

C. Clean, Traceable Engineering

Readable, testable code with clear ownership and shared standards. Not just “elegant code,” but essential for scaling, innovating, and responding safely and efficiently. Every commit, feature, and release becomes explainable, reversible, and measurable.

D. SRE as a Bridge Between Systems and Business

Modern SREs do more than keep systems running—they enable resilience as a service. They turn technical metrics into operational insights, acting as intelligent sensors that help the business anticipate, not just react.

The Result? Not Just Reliable Systems—but Intelligent Organizations

Companies that integrate these principles will:

• Dramatically reduce delays, errors, and silos

• Increase trust in data, processes, and people

• Innovate faster without sacrificing control or security

• Scale consistently, even in regulated or high-stakes environments

In a world where every second matters, every bug has a cost, and every piece of data drives a decision… Real digital transformation doesn’t come from buying more software— It comes from building the foundations to interpret, manage, and lead change.