Common Problem Stages Tech Companies Encounter After Going Into Production

Once a tech company releases a product into production, the real-world use often reveals a range of issues that may not have surfaced during development and testing. These problems can arise due to various factors such as the complexity of the system, user interactions at scale, or unforeseen dependencies. Below is a breakdown of common problem stages tech companies face post-production, listed in the typical order in which they are encountered, and how they usually approach these challenges.

1. Real-Life Bugs

• Problem Stage: Despite extensive testing, real-life user interactions often expose bugs that weren’t caught during development. These could be edge cases, inconsistencies across devices, or issues related to unexpected user behavior.
• Why it Happens: Testing environments often don’t fully replicate the complexity of real-world usage. Once a product reaches production, diverse user conditions, browser compatibility, or unanticipated input combinations can cause bugs.
• How Companies Approach It:
  • Bug Tracking and Reporting Systems: Companies use tools like Jira, Bugzilla, or GitHub Issues to log and track bugs reported by users or monitoring systems.
  • Hotfixes and Patch Releases: When critical bugs are discovered, companies prioritize hotfixes (urgent updates) to address them quickly without waiting for the next major release.
  • Beta Testing & Canary Releases: Some companies mitigate this by rolling out updates to a small percentage of users before a full release, catching issues early with limited exposure.
  • Root Cause Analysis: After addressing the issue, teams perform an in-depth analysis to understand how the bug slipped through and how to prevent similar ones in the future.

2. Performance Issues

• Problem Stage: As the number of users grows, performance issues may arise. Pages might load slowly, APIs could become sluggish, or response times might lag during peak usage periods.
• Why it Happens: Performance issues often stem from underestimating resource demands, inefficient code, or database bottlenecks. What works well in a development environment can perform poorly at scale.
• How Companies Approach It:
  • Performance Monitoring Tools: Tools like New Relic, Datadog, and Google Analytics help track performance metrics in real time and identify bottlenecks.
  • Profiling and Optimization: Teams profile the codebase to identify slow processes or inefficient algorithms, and work on optimization techniques such as caching, lazy loading, and asynchronous programming.
  • Database Tuning: Optimizing database queries, indexing tables, or implementing caching layers like Redis or Memcached to reduce strain on databases during high traffic periods.
  • Auto-Scaling Infrastructure: Using cloud services (AWS, Azure, Google Cloud), companies can set up auto-scaling to automatically increase or decrease resources based on traffic and demand.

3. Scalability Issues

• Problem Stage: As the user base grows or the product expands in functionality, scalability issues become apparent. The system that was initially designed to handle a few thousand users now struggles to accommodate millions.
• Why it Happens: Scalability issues typically arise due to architecture limitations, such as a monolithic design, poor database structure, or reliance on synchronous processes.
• How Companies Approach It:
  • Microservices Architecture: Companies often move from monolithic to microservices architecture, breaking down the application into smaller, independently deployable services that can scale horizontally.
  • Database Sharding: Dividing databases into smaller pieces (shards) to distribute the load more evenly, preventing a single database from becoming a bottleneck.
  • Load Balancing: Implementing load balancers (e.g., Nginx, AWS ELB) to distribute incoming traffic across multiple servers, preventing any one server from being overwhelmed.
  • Containerization and Orchestration: Using Docker and Kubernetes to easily manage and scale containerized applications across different environments.

4. Breaking Changes in Dependencies

• Problem Stage: Many tech companies rely on third-party libraries, frameworks, or APIs, which are regularly updated. A breaking change in a critical dependency can disrupt the system’s functionality, causing failures or unexpected behavior.
• Why it Happens: Breaking changes occur when a library, API, or service updates in a way that isn’t backward compatible. The system may rely on a deprecated feature or a change in the API’s structure that results in errors.
• How Companies Approach It:
  • Dependency Management Tools: Tools like npm, pip, or composer provide ways to lock specific versions of dependencies to avoid unintended upgrades. Using semantic versioning helps companies track compatible updates.
  • Regular Updates and Testing: Running scheduled updates and testing dependencies in staging environments before rolling them into production helps catch breaking changes early.
  • Vendor Communication: Maintaining open communication with third-party vendors or providers helps companies stay informed about upcoming changes and prepare accordingly.
  • Automated Testing and CI/CD: Continuous Integration (CI) pipelines that automatically run tests against updated dependencies can catch issues before deployment.

5. Security Vulnerabilities

• Problem Stage: As the system grows, security vulnerabilities can emerge, whether from external attacks, poor code practices, or insecure configurations. These vulnerabilities put user data, business operations, and reputation at risk.
• Why it Happens: Attackers constantly exploit new security weaknesses, while internal issues like unpatched systems or insecure APIs can also create vulnerabilities.
• How Companies Approach It:
  • Penetration Testing and Audits: Regular security audits and penetration tests (e.g., through tools like OWASP ZAP) help uncover vulnerabilities.
  • Security Best Practices: Implementing secure coding practices, using HTTPS, enforcing data encryption, and securing API endpoints.
  • Security Patches and Updates: Companies need to stay vigilant by applying security patches and updates to all components of the tech stack.
  • Monitoring and Alerts: Tools like Splunk and AWS CloudTrail can help detect suspicious activities in real-time, and alert teams to potential threats.

6. User Experience (UX) and Feedback Loop

• Problem Stage: After launch, companies often receive user feedback about usability issues or new feature requests that can lead to frustrations or lower engagement. These issues aren’t necessarily technical bugs but relate to user satisfaction and product fit.
• Why it Happens: The gap between what was designed and how users interact with the system can result in a mismatch between expectations and reality. Users may request new features, point out usability flaws, or experience confusion about how things work.
• How Companies Approach It:
  • User Feedback Tools: Companies use tools like Hotjar, FullStory, or Qualtrics to gather insights from real user behavior and feedback.
  • A/B Testing: Running A/B tests allows companies to experiment with different features or layouts to see which one resonates best with users.
  • UX/UI Design Improvements: Based on feedback, teams might revisit the design and navigation of their product, simplifying workflows and improving accessibility.

7. Technical Debt Accumulation

• Problem Stage: Over time, as features are added and quick fixes are applied, technical debt accumulates, leading to a fragile codebase that becomes difficult to maintain or scale further.
• Why it Happens: Rushed development timelines, poor architectural decisions, or lack of refactoring contribute to the buildup of technical debt. Teams often prioritize delivering new features over fixing underlying issues.
• How Companies Approach It:
  • Refactoring: Companies allocate time to refactor old code, improve modularity, and simplify complex areas of the codebase.
  • Code Reviews: Regular code reviews ensure that new code adheres to best practices and doesn’t add unnecessary complexity.
  • Automated Tests: Increasing the coverage of unit, integration, and end-to-end tests helps maintain code quality and reduces the risk of introducing new bugs when refactoring.

By navigating these stages efficiently, companies can continually improve their product’s reliability, performance, and user satisfaction while scaling to meet growing demand. Each challenge brings an opportunity to evolve the product and refine internal processes.