Chapter #7 Open IT&S Telemetry Adoption

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Open IT&S Telemetry Adoption is the organizational capability to instrument, collect, route, enrich, govern and operationalize telemetry, that is traces, metrics, logs, profiles, UX and signals, so telemetry becomes a repeatable, governed input to decisions across product, engineering, finance and executive leadership. Accessed with Applied Observability™ the practice of using telemetry to establish direct cause-to-effect links between user actions, system state, and business KPIs; operationalized through playbooks, SLOs, and automation.

This chapter talks about the concept and solutioning that is Open IT&S Telemetry Adoption, and not the company and product OpenTelemerty. This research focuses on capability, patterns, governance, business outcomes and playbook, not vendor marketing or a single product implementation.

This is where industry Enterprises, Digital businesses, IT and Engineering Organizations achieve their benefits and outcomes during modernization, cloud migration, resilience programs, digital product scaling, and cost optimization phases. 

Organizations will benefit from and achieve outcomes for. 

Unified Telemetry Foundation to Lower Operational Cost

· Eliminates vendor lock-in through OTel standardization.

· Reduces redundant agents, collectors, and billing sprawl.

· Provides a single, governed telemetry pipeline across infra, app, and business signals.

Improved Reliability & Reduced Downtime

· Faster MTTD/MTTR through correlated traces, logs, metrics, and events.

· Organizations shift from reactive firefighting to predictive detection.

Acceleration of Digital Transformation

· Standard telemetry enables modular architecture, AIOps adoption, platform engineering, and DevOps improvements.

· Creates a scalable signal fabric that future-proofs the organization for new tooling.

Data Quality & Governance at Scale

· Enforced semantic conventions and schemas (ECS, OTel) increase trust in operational and business insights.

· Prevents “shadow telemetry” from teams instrumenting differently.

Cross-Functional Alignment on Outcomes

· Shared dashboards for Product, Engineering, Reliability, and Finance.

· Enables decision-making based on real-time digital experience OKRs.

Faster Innovation Through Feedback Loops

· Experimentation and A/B test signals feed directly into product iteration velocity.

· Developers get immediate visibility into the impact of changes.

Executives and leaders could formulate ROI. For CIO, CTO, CISO, CDO, CFO, VP Engineering, and Head of SRE this applies during strategic planning, quarterly business reviews, budget cycles, and digital product performance reviews. 

Business-Level Storytelling from Technical Signals

· Executives get a mapped narrative: telemetry, digital experience, revenue, customer retention.

· No more raw dashboards, instead “This latency spike increased failed checkouts by 3.2%”.

Improved Strategic Decision-Making

· Leaders see which investments, platforms, features, and cloud infrastructure drive business outcomes.

· OTel standardization turns telemetry into an asset instead of a cost center.

Clear Line-of-Sight into Customer Outcomes

· C-suite sees how performance, stability, or regressions impact customer satisfaction or churn.

· Help justify investments in DevOps, SRE and Platform teams, or reliability improvements.

Predictability & Risk Reduction

· Leaders gain early warnings about reliability risks, scaling issues, and security anomalies.

· Helps CFO and CIO align on predictable cost and operational risk management.

Budget Optimization with Context

· Telemetry becomes the basis for cost-informed engineering.

· “This component costs $X per month and generates $Y in revenue impact.”

Executive Alignment & Communication

· All leaders align around the same OKRs: customer experience, operational reliability, cloud efficiency.

· Reduces cross-department conflict (Product vs. Engineering, Finance vs. Technology).

With end-users of digital services, enterprise buyers, and channel partners this applies to customer journeys, SLAs, digital experience moments of truth, and product adoption cycles.

More Reliable Digital Experiences

· Faster page loads, more resilient APIs, fewer checkout failures, fewer 500 errors.

· Predictive detection prevents customer-visible outages.

Higher Trust & Brand Confidence

· Customers see stable, reliable, low-friction digital interactions.

· B2B clients experience consistent SLA performance.

Reduced Friction in Product Journeys

· OTel-based instrumentation identifies root causes of abandonment.

· Leads to smoother onboarding, lower error friction, and better conversion.

Faster Feature Delivery & Improvements

· Feedback loops accelerate product iteration.

· Customers see improvements week-to-week, not quarter-to-quarter.

More Personalized, Responsive Experiences

· Observability data feeds AI-driven personalization, adaptive UI, or tailored automation.

· Better recommendations, faster support, more intuitive workflows.

Transparency for B2B Customers

· Business clients can view real-time performance dashboards, status pages, or SLA analytics.

· Enhances trust and partnership-level transparency.

By using these technologies, you can expose the cause-and-effect relationships between user actions, infrastructure states, and business KPIs (Key Performance Indicators). This means you can see how changes in your system affect user behavior and business outcomes, and vice versa.

E.g. An engineer mentions that at AspectIQ, they have helped organizations integrate observability into their digital transformation initiatives. This means they have worked with companies to ensure that the insights gained from telemetry data are used to drive both operational excellence and strategic decisions. This integration helps organizations become more agile, efficient, and aligned with their business goals.

Organizations getting started with open telemetry may begin by sending data directly to an observability backend. Using the Open-Telemetry collector as part of your observability architecture provides numerous benefits and is recommended for any production deployment.

Applied ObservabilityTM: The Playmaker’s Framework, is the practice of turning telemetry into business level evidence. Linking user actions to infrastructure state to business KPIs and operationalizing those insights across Orgs; product, finance, and Ops. Splunk and other voices define applied observability as the use of telemetry artifacts as evidence for asset discovery, optimization and decisioning.

Strategic Drivers:

· Standardization and portability reduce vendor lock-in and duplicated instrumentation effort.

· Faster MTTR and actionable RCA occur when many orgs report meaningful MTTR reductions when telemetry is standardized and high quality.

· Cost control and FinOps are central collector pipelines that enable selective sampling and routing, lowering ingestion and storage costs.

· AI and automation readiness for unified telemetry feeds are the raw material for generative and augmented operations and automated root-cause workflows. Vendors highlight the convergence of OTel and AI for faster incident triage.

· Business differentiation of observability tied to UX metrics, SLOs, and revenue KPIs moves engineering telemetry from “nice to have” to profit impact.

High-Level Architecture Pattern:

· Instrument apps with open telemetry SDKs and semantic conventions. 

· Deploy the gateway, daemonset, or sidecar to aggregate, filter, enrich, and route telemetry to one or more backends. 

· Enrich telemetry with business context. 

· Build dashboards, SLOs, alerting, and automated playbooks tied to business KPIs. 

· Feed telemetry into ML/AI pipelines for anomaly detection, predictive ops, and automated remediation.

1. Standardized Instrumentation (SDK & Semantic Conventions)

2. OTel Collector Strategy (gateway, sidecar, daemonset)

3. Signal Prioritization & Sampling (FinOps-aware telemetry)

4. Business Context Enrichment (KPIs in traces/metrics)

5. SLO/SLA Playbooks & Error Budgeting

6. Full-Stack Correlation (UX → Backend → Infra)

7. Data Governance & Privacy Controls

8. Cost-Aware Observability (FinOps integration)

9. Platform Engineering + Observability-as-a-Product!

10. AIOps & Assistive Automation

11. Observability Data Lake / Analytics Fabric

12. Developer Experience (DX) & Feedback Loops

13. Governed Multi-Backend Strategy

14. Executive KPIs & Storytelling