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Traditional Analytics or Agile Analytics: which one fits you best?

 In this article, we explore the key differences between Agile Analytics and Traditional Analytics to help organisations make informed decisions about their analytical strategies.

All organisations do analytics to extract insights from their data. Some organisations even make most of their decisions based on this data. Are there any distinct ways in which we can do analytics? We’ve found two prominent approaches: Agile Analytics and Traditional Analytics. Each has its own set of principles, practices, and advantages. In this article, we explore the key differences between Agile Analytics and Traditional Analytics to help organisations make informed decisions about their analytical strategies.

As the name suggests, this form of analytics has been around since Big Gates was modelling on his desk back when Microsoft was still a cool startup.

**Sequential Process:**

Traditional Analytics typically follows a sequential, waterfall-like process. It involves distinct phases such as data collection, cleansing, transformation, analysis, and reporting. Each phase is completed before moving on to the next, often resulting in lengthy project timelines.

**Rigorous Planning:**

In the traditional approach, extensive planning is done upfront to define requirements and scope. Changes to the project scope are often challenging to accommodate once the project is underway.

**Fixed Scope and Deliverables:**

Projects are planned with fixed scope and deliverables, making it challenging to adapt to evolving business needs or to incorporate new data sources during the project.

**Comprehensive Documentation:**

 Documentation is a key aspect of traditional analytics. Detailed documentation is created at each stage to provide a comprehensive understanding of the entire process.

**Long Development Cycles:**

The emphasis on thorough planning and documentation often leads to longer development cycles, and insights are delivered at the end of the project.

Since the early 2010s everything needed to become ‘agile’, because as we learnt everything is agile is better? well, it depends but for analytics, approaching things in a more ‘agile’ fashion actually makes sense

**Iterative and Incremental:**

 Agile Analytics, inspired by the principles of Agile software development, adopts an iterative and incremental approach. It divides the project into smaller, manageable iterations or sprints, each delivering a potentially shippable product increment.

**Adaptive Planning:**

 Agile Analytics embraces adaptive planning, allowing teams to respond to changing requirements and priorities. Planning is continuous and flexible, enabling adjustments based on ongoing feedback.

**Embraces Change:**

 One of the core principles of Agile is its adaptability to change. Agile Analytics acknowledges that requirements evolve throughout the project, and it accommodates changes to ensure that the end product aligns with business needs.

**Collaboration and Communication:**

 Agile places a strong emphasis on collaboration and communication within cross-functional teams. Regular meetings, such as daily stand-ups, facilitate constant communication and coordination among team members.

**Early and Continuous Delivery:**

 Agile Analytics aims for early and continuous delivery of insights. This allows stakeholders to gain value incrementally throughout the project, rather than waiting for a comprehensive solution at the project's end.

Agile Software Development and Agile Analytics go hand in hand, sharing principles of flexibility and collaboration. Aligning analytics with an Agile mindset is crucial for the following reasons:

1. **Rapid Adaptation:** Agile development involves frequent requirement changes. Agile Analytics is flexible and adapts quickly to evolving project needs.
2. **Frequent Releases:** Agile development operates on short cycles. Agile Analytics ensures rapid insights to support the quick-release cadence of Agile teams.
3. **Collaborative Decision-Making:** Agile promotes team collaboration. Agile Analytics involves stakeholders, ensuring insights align with business goals.
4. **User-Centric Focus:** Agile emphasizes delivering value to users. Agile Analytics provides rapid feedback on user behaviour, enhancing user-centric solutions.

In summary, aligning analytics with an Agile mindset promotes collaboration, flexibility, and continuous improvement, ensuring timely and relevant insights for Agile development teams. This integrated approach supports data-driven decision-making and contributes to overall project success.

Experience ZEN Software’s Agile Analytics

The next step is to employ an Analytics platform based on the same tenets for your Software Development. With Agile Analytics, you measure critical metrics like feature/non-feature balance, deployment frequency, and SRE Error budgets and monitor DORA metrics and Error Budgets.

Measuring Error Budgets in Agile Analytics

Gain unparalleled visibility into your sprint performance. Don't miss this chance to break free from traditional constraints and propel your organization to new heights.

We can help you out with a DevOps Assessment, Continuous Delivery conversion, a Lean Software Development Transformation or an SRE implementation. Start your Agile Analytics journey today!

Get more features delivered sustainably: as easy as connecting to Jira

Software developers often find new feature development to be the most enjoyable aspect of their work. It allows them to exercise their creativity, solve interesting problems, and deliver tangible results. Additionally, the team’s product owner’s primary focus is usually on adding new features to the product, as they believe it is what customers want and what will generate revenue.

Software developers often find new feature development to be the most enjoyable aspect of their work. It allows them to exercise their creativity, solve interesting problems, and deliver tangible results. Additionally, the team’s product owner’s primary focus is usually on adding new features to the product, as they believe it is what customers want and what will generate revenue. In this context, developers often prioritize feature development over other aspects, such as bug fixing or code optimization, because "we are being paid for features, not for bugs." Luckily there is now a solution available that is as easy as connecting to Jira: Agile Analytics Sprint Insights!

However, this singular focus on new feature development can have detrimental effects in the long run. Software that is constantly adding new features without proper attention to quality and stability can become slow, unstable, and insecure. Neglecting bug fixing and technical debt accumulation can lead to a deteriorating codebase and increased complexity, making it difficult to maintain and enhance the software over time. Moreover, security vulnerabilities may arise due to rushed development, lack of proper testing, or inadequate consideration of potential risks.

To address these issues, it is crucial to maintain a balance between feature development and non-feature work. Understanding the importance of allocating time and resources for bug fixing, code optimization, and security enhancements is essential for building a sustainable and robust software product. 

Leverage the power of AI and Large Language Models

Agile Analytics leverages the power of GPT and Large Language Models to differentiate between feature work and non-feature work by analyzing the content and structure of work items or tickets. By processing the textual information within these items, the model can determine the category of work that best corresponds to each item.

The underlying language model, such as GPT, has been trained on a vast amount of text data and has learned to understand the context and meaning of words and phrases. When applied to Agile Analytics, the model can interpret the descriptions, titles, and other relevant information provided within the work items. By analyzing this text, the model can identify patterns, keywords, or specific language that indicates whether the work item represents feature development or non-feature work, such as bug fixing or code refactoring.

Agile Analytics Sprint Insights further enhances this capability by allowing teams to train the model and fine-tune its performance.

Empowering your teams is as easy as [starting your free trial](https://www.agileanalytics.cloud/start-free-trial) and browsing to ‘settings’, ‘Sprint insights’ and pressing the **‘Connect to Jira’** button. Teams can gain visibility into the balance between feature and non-feature work today! 

To empower teams with insights into their software operation, Agile Analytics offers a solution that measures DORA Metrics, Sprint Insights, Error Budgets, Software Stock and detects Leaked Private Keys. 

Our amazing platform provides valuable analytics that can be used during Sprint Review sessions or as a bi-weekly sprint report, enabling the team to assess the stability and sustainability of their software. **Allow us to supercharge your software operation!**

What DORA Metrics Can Reveal About Your Team Performance

Ever wondered how fast your code goes from "I got an idea" to "Boom, it's live"? That's where Lead Time for Changes comes in – it's like your code's speedometer. Wanna know if your code changes are speedy like The Flash or just taking a stroll? In coding, finding the perfect rhythm is the key!

Ever wondered how fast your code goes from "I got an idea" to **"Boom, it's live"**? That's where *Lead Time for Changes* comes in – it's like your code's speedometer. Wanna know if your code changes are speedy like The Flash or just taking a stroll? In coding, finding the perfect rhythm is the key!

Lead Time for Changes is one of the DORA Metrics. Lead Time for Changes measures the time it takes for a code change to progress from the initial commit to being deployed and running in a production environment. This metric essentially quantifies the speed and efficiency of your development pipeline, offering valuable insights into the effectiveness of your team's workflows.


Key Components of **Lead Time for Changes** are:
- **Commit-to-Deploy**: This is the duration from the moment a developer commits a code change to the version control system to the point where it is successfully deployed in a production environment.
- **Review and Approval Time**: Lead Time for Changes includes the time spent on code review and obtaining necessary approvals before a change is merged into the main codebase.
- **Build and Test Time**: It accounts for the time taken to build and test the code change to ensure it meets quality standards and doesn't introduce regressions.
- **Deployment Time**: This is the time it takes to deploy the code change into the production environment, making it available to end-users.

So, what does Lead Time for Changes reveal about your team's performance?

**Short Lead Times:** Teams with short lead times demonstrate high efficiency in their development processes. For example, if a code change moves seamlessly from development to deployment in a matter of hours, it indicates an optimised pipeline.

**Potential Bottlenecks:** On the contrary, prolonged lead times may signal bottlenecks in various stages of the development lifecycle. For instance, a code review or testing delay might extend the lead time, highlighting areas that need attention and improvement.

**Short Lead Times:** A Short Lead Time for Changes reflects the team's agility and responsiveness to changing requirements. Suppose a team can implement and deploy user-requested features within a short timeframe. In that case, it suggests adaptability and a capacity to meet market demands swiftly.

**Long Lead Times:** Extended lead times may hinder a team's ability to respond promptly. For instance, if a competitor introduces a new feature, a team with long lead times may struggle to match the pace, potentially affecting its competitiveness in the market.

**Code Review Bottlenecks:** If lead times are primarily extended due to lengthy code review processes, the team might need to assess and optimize its review mechanisms. This could involve implementing tools for automated code review or establishing clearer guidelines for more efficient reviews.

**Testing and Deployment Challenges:** If testing or deployment times significantly contribute to the lead time, addressing these areas could involve investing in automated testing procedures or refining deployment scripts for faster and more reliable releases.

**Setting Reduction Targets:** By regularly monitoring lead times, teams can set targets for lead time reduction. For example, if the average lead time for changes is currently two days, the team might aim to bring it down to one day over the next few sprints.

**Iterative Process Refinement:** Teams can leverage lead time insights to refine their processes iteratively. If a retrospective analysis reveals that lead times increased after a certain process change, the team can revert or adjust the change and observe the impact.

The Lead Time for Changes metric serves as a versatile tool for teams to assess and enhance their development practices. Teams can pinpoint areas for improvement using DORA Metrics, foster a culture of collaboration and continuous improvement, and ultimately deliver higher-quality software more efficiently.

For teams keen on harnessing the power of DORA metrics, our amazing Platform, Agile Analytics, is ready to assist! Unlocking a wealth of insights, this app provides an **easy-to-set-up and easy-to-operate tool for measuring productivity and optimising development processes**. 

Curious to experience the transformative impact? **Take advantage of our free 60-day trial or book a demo with our talented team of Agile Coaches**. Witness firsthand how Agile Analytics can propel your team towards enhanced efficiency and success.

Stop Guessing, Start Measuring in your Quality Gate Meetings

Quality Gates serve as checkpoints or milestones within the software development lifecycle to ensure that specific quality criteria are met before progressing to the next phase. Too often, we see that while the process is implemented, the data that is ‘brought’ to the table is not empirical. We see a lot of HiPPO (Highest Paid Person’s Opinion) behaviour going on in these meetings. We believe we have a solution to that challenge! 

Quality Gates serve as checkpoints or milestones within the software development lifecycle to ensure that specific quality criteria are met before progressing to the next phase. Too often, we see that while the process is implemented, the data that is ‘brought’ to the table is **not empirical**. We see a lot of HiPPO (Highest Paid Person’s Opinion) behaviour going on in these meetings. **We believe we have a solution to that challenge! Stop Guessing; start Measuring!**

Using Agile Analytics' [Sprint Insights](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#sprintinsights), powered by Large Language Models and GPT, teams can gain valuable insights into the **distinction between feature development and maintenance/non-feature work**. This capability enhances team autonomy by providing objective measures of the time allocated to each type of work. By leveraging advanced natural language processing, the tool can analyze and categorize tasks, allowing teams to understand how their time is being allocated and make data-driven decisions. This knowledge empowers teams to prioritize effectively, optimize resource allocation, and ensure a balanced focus on both feature development and maintenance, ultimately improving productivity and delivery outcomes.

Measuring the number of open changes in source control, often referred to as **['software stock'](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock)**, is crucial in understanding the amount of change accumulated and serves as a valuable quality metric. 

Measuring the number of open changes in source control, or the software stock, provides valuable insights into the amount of pending change and serves as a quality metric. By monitoring the stock and taking appropriate action, such as adjusting schedules or allocating resources, the development team can **minimize the risk** of encountering unstable conditions during the software's release to production.

Looking for Private Keys in your internal source control system is crucial to ensure that unauthorized access to protected cloud resources is prevented within your organization. Mistakenly committing a private key to the central repository can easily occur, and even if the key is removed from the latest commit, it may still exist in older revisions. By leveraging Agile Analytics' [Leaks Finder](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys), you can detect the presence of these keys in previous versions and take appropriate actions to secure your cloud resources. This becomes an essential security compliance metric, ensuring that sensitive information is not inadvertently exposed and **safeguarding your organization's data and systems**.

Setting up the process of [Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets) empowers your teams by **granting them increased autonomy** in decision-making regarding feature development and quality aspects. [Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets) provide a defined threshold of acceptable errors or issues in production. By having this framework in place, teams can determine whether they can focus on full-throttle feature development or if attention needs to be given to addressing quality concerns. This approach allows teams to strike a balance between innovation and maintaining high-quality standards, giving them the flexibility to make informed choices and prioritize their efforts effectively.

The industry standard DORA (DevOps Research and Assessment) metrics offer a valuable framework for measuring productivity and identifying high-performing teams while considering the quality aspects of software delivery. [DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) encompass four key metrics: Deployment Frequency, Lead Time for Changes, Mean Time to Recover, and Change Failure Rate.

[DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) enable the measurement of team productivity through quantitative indicators like Deployment Frequency and Lead Time for Changes. These metrics allow organizations to track and compare **productivity levels, identify bottlenecks, and drive improvement efforts**. Additionally, [DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) incorporate quality aspects through Mean Time to Recover and Change Failure Rate, ensuring that productivity gains are achieved without compromising software quality. By including these quality metrics, organizations can identify high-performing teams that consistently deliver value while upholding high-quality standards.

At ZEN Software, we are ready to assist you with Agile Analytics, a comprehensive product that provides detailed reports for Quality Gate meetings and the Quality gate process. By leveraging real production data, we supercharge your software operation, enabling effective quality management in software development. Contact us to learn how we can enhance the quality of your software and drive your projects towards success.

What are Service Level Objectives (SLOs) and why should I measure them?

Have you ever wondered why some software applications perform better than others? The answer is simple: they have well-defined Service Level Objectives (SLOs).

SLOs are a critical component of Site Reliability Engineering (SRE) that defines the target level of service quality that a system should provide. I will explain what SLOs are, why they are important and provide examples of common SLOs.

SLOs, or Service Level Objectives, are **specific, measurable goals** that help software development teams ensure that their systems are functioning correctly and efficiently. The main purpose of SLOs is to set a threshold of acceptable service quality for a system, which is usually expressed as a percentage of uptime or response time. By measuring system performance against SLOs, teams can determine whether their systems are meeting their target service levels or need improvement.

 
Service Level Objectives (SLOs) are measurable goals that define the desired level of performance for a service. Different types of SLOs can be defined based on various aspects of service performance. Here are some common types:

**Availability SLOs**:
- *Definition:* These SLOs measure the percentage of time a service is available and operational.
-  *Example:* Achieving 99.9% availability means the service is allowed to be down for a maximum of 8.76 hours per year.    

**Latency SLOs**:
- *Definition:* Latency SLOs focus on the time it takes for a service to respond to a request or perform a specific action.
- *Example:* A latency SLO might specify that 95% of requests should be processed in under 100 milliseconds.    

**Throughput SLOs**:
- *Definition:* Throughput SLOs measure the number of operations a service can handle within a specified time frame.
- *Example:* Ensuring a web service can handle 1000 requests per second without degrading performance.    

**Error Rate SLOs**:
- *Definition:* These SLOs focus on the percentage of requests that result in errors or failures.
- *Example:* Keeping the error rate below 0.5% indicates a high level of reliability.    

Two of the most common SLOs are availability and latency. Let’s take a closer look at them and configure them in Agile Analytics.

Agile Analytics allows you to easily set up and manage Service Level Objectives for your services and alert when an SLO is out of target or when the corresponding Error Budget is depleted.

An Availability SLO defines the minimum acceptable level of uptime for a service over a given period. For example, an e-commerce website may have an availability SLO of 99.9%, meaning that the website should be up and running at least 99.9% of the time each month. Availability SLOs are crucial for ensuring that customers can access the service when they need it and can help teams prioritize efforts to improve uptime.

Set up tracking an Availability SLO in Agile Analytics is easy

tracking availability SLO in Agile Analytics

Common filters to measure availability (good-bad ratio):



A Latency SLO defines the maximum acceptable response time for a service. For example, a banking application may have a latency SLO of 500ms, meaning that the application should respond to user requests within 500ms or less. Latency SLOs are essential for ensuring that services are responsive and fast, which can help improve user experience and satisfaction.

Setting up a Latency SLO in Agile Analytics is super easy

Here is a common Latency SLO (Distribution cut) setup  in Agile Analytics:

**Threshold bucket:** 19
**Good Below Threshold:** True

“Measuring things brings knowledge” – Dutch proverb.

In the end, it's crystal clear that SLOs are a crucial part of keeping your software services up and running like a champ. With the top dogs being availability and latency, you can quickly measure how much time your service spends being a couch potato and how quickly it jumps into action when someone needs it. With Agile Analytics, you can track SLOs faster than Usain Bolt running the 100m dash, get instant insights into what's going on, and transform your services into a lean, mean performance machine. So, what are you waiting for? **Try out [the 60-day free trial of Agile Analytics](https://www.agileanalytics.cloud/start-free-trial) and get your software services in tip-top shape today!**

How ChatGPT can enhance your software development team performance

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

ChatGPT is designed to generate natural language responses to user inputs, making it a very valuable tool in a wide range of applications, from language translation to chatbots. Now, let's dive into how your software development team can use ChatGPT to boost performance.

Collaboration is key to the success of any software development team. ChatGPT can be used to enhance collaboration by facilitating communication between team members.  ChatGPT allows team members to communicate effectively, provide real-time feedback on task progress, and collaborate on code and documentation. 

Using our AnalyticsBot as part of your Slack workspace, your teams can now employ chatGPT to suggest implementations or to help out with coding problems. Take this example, for instance:

By mentioning AnalyticsBot, Paul now has a solve for his coding problem.

ChatGPT can be used to improve code quality by analyzing code and identifying areas for improvement. 

ChatGPT can help you improve your code quality by providing a second set of eyes on your code, a.k.a code review. By submitting your code to ChatGPT, the language model can analyze the code and provide suggestions for improvements. For example, it can recommend better variable names, suggest ways to optimize code, or identify potential security vulnerabilities. 

It can also contribute to code refactoring by suggesting improvements to code snippets. ChatGPT can suggest ways to remove duplicate code, recommend better variable or function names, or identify areas where the code can be simplified.

ChatGPT can assist in staying up-to-date with the latest best practices in software development as well. By analyzing your code and comparing it to industry standards, ChatGPT can identify areas where your code can be improved to align with best practices. It can recommend the use of design patterns, suggest the use of automated testing, or highlight areas where code comments could be added etc.

With its ability to estimate project completion times and improve collaboration, ChatGPT is like the sidekick that helps developers become the superhero they always should have been. But like any good sidekick, ChatGPT comes with its quirks and unique personality. Sometimes it may be wrong or give an estimation that's a little off. So we have to be aware of it and not rely on AI completely.

This is where us mere mortals come in; Based on years of experience and with the right robotic tools, ZEN Software’s Agile Analytics and services will supercharge your software operation.

**Want to increase your software development team's productivity? Try Agile Analytics today with our 60-day free trial and watch your performance soar.**

 

No more Manual Hassles! Automate Your Non-Functional Measurement for Maximum Efficiency!



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Software quality goes beyond functionality. Non-functional aspects like **performance**, **reliability**, and **security** are crucial. Our Agile Analytics SaaS platform offers a comprehensive solution to manage and measure these dimensions effectively. By leveraging DORA metrics and SRE Error Budgets, teams can optimise deployment, reliability, and security, while also monitoring software stock and detecting leaked private keys.



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Software quality goes beyond functionality. Non-functional aspects like **performance**, **reliability**, and **security** are crucial. Our Agile Analytics SaaS platform offers a comprehensive solution to manage and measure these dimensions effectively. By leveraging DORA metrics and SRE Error Budgets, teams can optimise deployment, reliability, and security, while also monitoring software stock and detecting leaked private keys.

**1. [DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics)**: Measure Deployment Frequency and MTTR Agile Analytics SaaS integrates  [DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics), providing insights into deployment frequency and Mean Time between failure (MTTR). These metrics optimise software agility, adaptability, and recovery time, enabling data-driven decisions for continuous improvement.



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**2. [SRE Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets)**: Enhancing Reliability The platform introduces [SRE Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets), defining acceptable error rates and quantifying software reliability. Monitoring error budgets allows proactive adjustments in planning and resource allocation to maintain reliability and enhance end-user satisfaction.



IFS logo: Agile analytics, Error Budgets, DOR

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**3. Quantify [Software "Stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and Detect [Leaked Private Keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys)**. Agile Analytics SaaS introduces innovative metrics to measure [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and detect[ leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys). Quantifying [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) improves workflow efficiency, while detecting [leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys) safeguards sensitive data and prevents security breaches.



Analytic insights to optimize stats for agile Institute for Fiscal Studies.

To excel in the digital landscape, software quality must prioritise non-functional aspects. The Agile Analytics SaaS platform **empowers** teams to optimise deployment, reliability, security, and workflow efficiency. By leveraging **[DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics)** and SRE **[Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets)**, organisations can make **data-driven decisions** for **continuous improvement**. Additionally, monitoring [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and detecting [leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys) ensures **enhanced productivity** and **security**. With Agile Analytics SaaS, automate your non-functional quality management and maximise your team’s ability to deliver!