Observability Survey

Instrumentation and Data Collection

Instrumentation Coverage

To what extent have you implemented instrumentation to collect logs, metrics, and traces across your various environments (development, testing, and production)? Consider the comprehensiveness of your approach and its ability to cover critical areas of your system.

Not instrumented at all.

Basic instrumentation is limited to development environments only.

Partial instrumentation covers both development and testing environments, but significant gaps remain.

Comprehensive instrumentation is implemented in all environments except production, providing good but not complete coverage.

Full instrumentation is implemented across all environments, including production, ensuring complete coverage.

Tools for Monitoring

What types of tools do you use to monitor your infrastructure and applications, and to what extent are they diverse and integrated within your daily workflows and operational processes?

We do not use any monitoring tools.

We rely primarily on open-source tools with minimal integration into our workflows, leading to occasional monitoring gaps.

We use a combination of open-source and commercial tools, with partial integration that still leaves some room for improvement.

We primarily use commercial tools that are well-integrated into our workflows, providing reliable monitoring capabilities.

We have fully integrated a comprehensive suite of commercial monitoring tools into our workflows, ensuring continuous and effective monitoring.

Data Quality and Relevance

What measures do you have in place to ensure that the data collected through your instrumentation is accurate, relevant, and actionable?

We do not have any processes in place to ensure data quality, leading to potential inaccuracies and irrelevance.

We perform occasional manual checks on data quality, which helps but is not fully reliable.

We have automated checks for critical data points only, providing some assurance of quality but leaving gaps.

We implement comprehensive automated checks for most data points, ensuring a high level of accuracy and relevance.

We utilise rigorous automated and manual processes to ensure all data is accurate, relevant, and actionable at all times.

Instrumentation Complexity and Overhead

How do you manage the complexity and performance overhead introduced by your instrumentation practices?

We do not consider instrumentation complexity or performance overhead, which sometimes affects system performance.

We occasionally review and address performance issues caused by instrumentation, but not systematically.

We have basic guidelines in place to manage complexity and performance overhead, leading to moderate efficiency.

We regularly monitor and optimise the performance impact of our instrumentation, ensuring minimal disruption.

We employ advanced strategies and tools to minimise complexity and performance overhead without compromising data quality.

Use of Distributed Tracing

To what extent have you implemented distributed tracing to monitor and troubleshoot interactions between different components of your system?

We do not use distributed tracing.

We have implemented tracing in a limited capacity, covering only a few critical components.

We have partial tracing coverage, useful but with significant gaps.

We have extensive tracing coverage across most components, effectively helping in troubleshooting.

We have comprehensive distributed tracing implemented across all components, providing full visibility into system interactions.

Data Management and Optimisation

Effectiveness of Data Retention Policies

How effective are your data retention policies in managing storage costs while ensuring that only relevant data is retained? Consider the balance between data retention duration and cost efficiency.

We do not have data retention policies, leading to excessive storage costs and irrelevant data.

Our data retention policies are minimally effective, with occasional issues in managing costs and relevance.

Our policies are somewhat effective but could be improved to better balance costs and data relevance.

Our data retention policies are effective, generally balancing costs and maintaining relevant data.

Our data retention policies are highly effective, ensuring optimal cost management and data relevance.

Balancing Data Granularity with Cost

How do you balance the granularity of the data you collect with the associated costs of storage and processing?

We do not consider data granularity, leading to high storage and processing costs.

We minimally balance data granularity with costs, often resulting in inefficiencies.

We balance data granularity with costs to some extent, but there are areas for improvement.

We effectively balance data granularity with costs, optimising storage and processing.

We have advanced practices that perfectly balance data granularity with cost efficiency.

Automated Data Management

To what extent have you implemented automated processes for data management, including retention, filtering, and prioritisation?

We do not use automated data management processes.

We have minimal automation, relying mostly on manual processes.

We have some automation, but many processes are still manual.

We have a good level of automation that handles most data management tasks efficiently.

Our data management processes are fully automated, ensuring efficiency and accuracy.

Cost Management Practices

Do you have practices in place to manage the costs associated with data storage and processing, while ensuring that data quality is maintained?

We do not have any cost management practices, leading to uncontrolled expenses.

Our cost management practices are minimal and occasionally ineffective.

We have some cost management practices, but they need improvement.

Our cost management practices are effective and generally keep expenses under control.

We have highly effective cost management practices that optimise expenses while maintaining data quality.

Relevance of Collected Data

How do you ensure that the data collected through your observability practices is relevant and useful for your operational and business needs?

We do not have any processes to ensure data relevance, leading to a lot of irrelevant data.

We have minimal processes to ensure data relevance, with occasional irrelevant data being collected.

We have some processes in place, but there's room for improvement in ensuring data relevance.

We have effective processes that generally ensure the relevance of the data collected.

We have highly effective processes that ensure all collected data is relevant and useful.

Full-Scale Utilisation of Observability

Effectiveness of Enhanced Alerting and Monitoring

How well-optimised are your alerting and monitoring systems in improving system reliability and reducing alert fatigue?

Our alerting and monitoring systems are not optimised, causing frequent noise and missed critical alerts.

Our systems are minimally optimised, leading to occasional noise and alert fatigue.

Our systems are moderately optimised, but there is still some alert fatigue and missed opportunities for automated responses.

Our systems are well-optimised, with minimal noise and effective use of SLOs and automated responses.

Our systems are highly optimised, eliminating noise and fully leveraging SLOs and automated responses.

Advanced Diagnostic and Debugging Capabilities

How effectively does your team utilise observability data for root cause analysis and error tracking to improve system reliability?

We do not use observability data effectively for diagnostics, leading to prolonged downtime.

We have minimal diagnostic capabilities, resulting in occasional delays in identifying root causes.

We use observability data for diagnostics, but there are gaps in our error tracking and root cause analysis processes.

We effectively use observability data for diagnostics and have solid error tracking processes.

We fully leverage observability data for advanced diagnostics, with seamless error tracking and root cause analysis.

Utilisation of User Behavior Insights

Does your organisation leverage user behaviour insights derived from observability data to enhance user experience?

We do not use user behaviour insights, missing opportunities to improve user experience.

We minimally use behaviour insights, occasionally influencing decisions.

We somewhat use behaviour insights, but they are not consistently applied in decision-making.

We effectively use behaviour insights, regularly improving user experience.

We fully leverage user behaviour insights, consistently driving user-centric decisions and improvements.

Automated Response and Scaling

To what extent do you use automated responses and scaling based on observability data?

We do not use automated responses or scaling, leading to frequent manual interventions.

We minimally use automated responses, with limited impact on system uptime and performance.

We use automated responses and scaling, but there is room for improvement in their effectiveness.

We effectively use automated responses and scaling, significantly improving system uptime and performance.

We fully leverage automated responses and scaling, ensuring optimal system performance and reliability.

Training and Observability Documentation for Practices

How comprehensive is your training and documentation for observability practices? Consider the accessibility and impact of these materials on team readiness and system performance.

We do not have training or documentation for observability practices, leading to low team readiness.

Our training and documentation are minimal, resulting in occasional gaps in readiness and performance.

We have some training and documentation, but they are not always accessible or up-to-date.

We have comprehensive training and documentation, generally accessible and effective in improving readiness and performance.

We have highly comprehensive training and documentation, always up-to-date and easily accessible, ensuring high team readiness and optimal system performance.

Business Alignment and Usage

Use of Observability Data by Product Owners

How effectively do product owners utilise observability data to prioritise features and improvements?

Product owners do not use observability data for prioritisation, relying solely on intuition.

Product owners rarely use observability data, occasionally referencing it for major decisions.

Product owners sometimes use observability data, integrating it into the decision-making process sporadically.

Product owners often use observability data, regularly incorporating it into prioritisation decisions.

Product owners always use observability data, systematically driving feature prioritisation and improvements based on insights.

Accessibility of Observability Reports

How accessible and easy to understand are your observability reports for non-technical stakeholders, such as executives and business managers?

Observability reports are not accessible to non-technical stakeholders, leading to misunderstandings.

Reports are minimally accessible, with occasional clarity issues.

Reports are somewhat accessible but often require technical interpretation.

Reports are accessible and generally understandable for most non-technical stakeholders.

Reports are highly accessible and easy to understand for all non-technical stakeholders, facilitating informed decision-making.

Frequency of Data-Driven Strategic Decisions

How often is observability data used to drive strategic business decisions within your organisation?

Observability data is never used for strategic decisions.

Data is rarely used, only for significant issues or opportunities.

Data is sometimes used, influencing some strategic decisions.

Data is often used, regularly informing strategic decisions.

Observability data is always used, consistently driving strategic decisions across the organisation.

Impact of Observability Data on Business Outcomes

What impact does the use of observability data have on business outcomes, such as revenue growth, customer satisfaction, and operational efficiency?

Observability data has no measurable impact on business outcomes.

The impact is minimal, with occasional positive outcomes.

The impact is moderate, contributing to some positive business outcomes.

The impact is significant, frequently improving business outcomes.

The impact is substantial, consistently driving major improvements in business outcomes.

Training and Awareness of Observability Among Non-Technical Staff

What level of training and awareness exists among non-technical staff regarding the importance and usage of observability data?

There is no training or awareness among non-technical staff.

Minimal training exists, with limited awareness.

Moderate training programs exist, with some awareness.

Strong training programs ensure good awareness among non-technical staff.

Comprehensive training programs and initiatives ensure high awareness and understanding among all non-technical staff.

Continuous Improvement and Innovation

Continuous Improvement Initiatives

Do you foster a culture of continuous improvement and innovation in observability within your organisation?

We do not have any initiatives to promote continuous improvement in observability.

We have occasional initiatives, but they are not systematic.

We have regular initiatives that encourage some level of continuous improvement.

We have well-established programs that consistently promote continuous improvement and innovation.

We have a deeply ingrained culture of continuous improvement and innovation, supported by extensive initiatives and programs.

Employee Training and Development

Are training and development opportunities provided for employees to improve their observability skills and adopt new technologies?

We do not provide any training or development opportunities related to observability.

We provide occasional training, but it is not comprehensive.

We offer regular training, but there is room for improvement in coverage and depth.

We provide frequent and comprehensive training and development opportunities.

We have an extensive training and development program that ensures all employees are highly skilled in observability and new technologies.

Feedback Mechanisms

Do you collect and use feedback from observability practices to drive continuous improvement?

We do not have any feedback mechanisms for observability practices.

We have informal feedback mechanisms, but they are not structured.

We have some structured feedback mechanisms, but they are not regularly utilised.

We have well-structured feedback mechanisms that are regularly utilised to drive improvements.

We have highly effective feedback mechanisms that are systematically used to continuously enhance observability practices.

Documentation and Knowledge Sharing

How comprehensive and up-to-date is your documentation for observability practices, and how effectively is knowledge shared across your organisation?

We do not have documentation or knowledge sharing processes for observability practices.

Our documentation and knowledge sharing are minimal and outdated.

Our documentation and knowledge sharing are somewhat comprehensive but need improvement.

Our documentation is comprehensive and up-to-date, and knowledge is effectively shared.

Our documentation is highly comprehensive and always up-to-date, with excellent knowledge sharing practices that ensure all team members are informed.

Monitoring Industry Trends and Best Practices

How closely do you monitor industry trends and best practices in observability and incorporate this knowledge into your practices?

We do not monitor industry trends or best practices in observability.

We minimally monitor trends and occasionally incorporate best practices.

We somewhat monitor trends and incorporate best practices intermittently.

We closely monitor industry trends and regularly incorporate best practices.

We continuously monitor industry trends and best practices, systematically integrating these insights into our observability practices to stay at the forefront.

OBSERVABILITY ASSESSMENT