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Top 10 Barriers in Digital Product Engineering: Overcoming Scalability Challenges

Today's digital landscape constantly evolves, and technology advancements are driving higher user expectations. Product engineers find themselves confronted with the task of staying abreast of the most recent trends and advancements in their domain.

This dynamic environment creates a monumental task for engineers to design, develop, and maintain digital products, which include software, mobile applications, websites, and other digital media, all of which must deliver business value and enhance user experiences.

Consequently, digital product engineering has emerged as a critical technique. It allows engineers to develop scalable, cost-efficient, and durable digital products that meet the needs of businesses and consumers.

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Understanding digital product engineering

Digital product engineering involves the full spectrum of IT architectures, technologies, tools, frameworks, and development processes within the product's lifecycle. It includes planning and strategy, research and analysis, UX design, product development and prototyping, quality assurance and testing, support and maintenance, and product optimization.

At the heart of digital product engineering lies scalability, a fundamental concept that gauges a product's capacity to seamlessly manage a growing workload without experiencing any decline in performance. Engineers must build scalability into the process from the point of ideation; it can no longer be an afterthought. The product must be performance bulletproofed and capable of increasing in size and complexity regardless of additional resources and processing power needed to sustain peak performance. Otherwise, users will experience bottlenecks, sluggish response times, and other inefficiencies. Inevitably, the product's value and shelf life will come to a screeching halt, along with user satisfaction.

Here are the top 10 barriers to overcoming scalability challenges in digital product engineering.

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Barrier #1: Inadequate Infrastructures

Legacy systems, databases, and outdated software impede successful digital product engineering initiatives. Legacy systems rely on insufficient and time-consuming manual version control and change management.

Cloud-based systems, applications, databases, and infrastructure modernization can fix this problem. With cloud-based systems, changes can be made quickly and effectively, allowing engineers to easily handle version control and change management while ensuring design consistency throughout the product's lifecycle.

Cloud systems are easily scalable and reliable and don't require much maintenance effort. They can grow and change along with the products that use them with little to no human intervention.

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Barrier #2: Poor codebase architecture

Accumulating legacy code, or technical debt, often results in slow product performance, lagging development speed, lack of scalability, long backlogs of new features, and user frustration.

To avoid technical debt, engineers sacrifice old code in favor of modular code and microservices that represent the foundation of good software architecture. They are related concepts, but they are not the same.

Modular code breaks down larger software programs into smaller, manageable parts, improving code maintainability, higher code reuse, faster configuration changes, easier debugging and testing, increased productivity, and better scalability.

Microservices are independently operated cloud-native services that communicate with each other to build an application. They offer rapid development, scalability, and flexibility when building complex applications.

Both concepts allow organizations and their digital products to grow and scale quickly. They are easier to maintain and produce higher-quality outcomes.

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Barrier #3: Limited resource allocation

Limited or poor resource allocation can impede and stall a project and create overworked teams.

Efficient resource allocation is essential in digital product engineering and involves recognizing available resources and assigning the right people with the necessary resources to tasks at the right time.

Strategies to optimize resource allocation include:

• Assigning skilled people to tasks that align with their expertise ensures maximum productivity and minimizes rework.
• Promoting cross-functional collaboration where teams share resources, software, and equipment to eliminate redundancies.
• Outsourcing tasks to external partners with specialized expertise lets in-house staff focus on their core competencies.
• Load balancing divides tasks among computers and resources to maximize efficiency, minimize response time, and avoid congestion.
• Prioritizing projects to focus on the most critical tasks helps teams optimize their resources and time, leading to increased work efficiency and better results.
• Resource scaling adjusts resources to changing demands that maximize performance and minimize costs, enabling resource allocation while conserving resource utilization.

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Barrier #4: Inefficient database design

Efficient databases are critical for engineers to achieve scalability, performance, data integrity, and uncomplicated maintenance.

Best practices for efficient database design include:

• Analyze business needs and determine key objects to store in the database. Once identified, entities and their relationships can be specified, such as one-to-one, one-to-many or many-to-many.
• Normalizing by mixing data within a database diminishes redundancy and improves data integrity.
• Creating indexes on frequently used columns in queries enhances query performance, such as usage patterns and types of indexes.
• Choosing between SQL (Structured Query Language) and NoSQL databases is crucial. While SQL databases rely on predefined schema to store data, NoSQL is a dynamic database that does not require static schema and can effortlessly scale.

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Barrier #5: Lack of automation in scaling

Ensuring the quality and dependability of digital products becomes more tricky as products scale. Automation is vital to reducing engineers' time and resources by streamlining manual, repetitive tasks prone to human error.

When tasks, like build and test, are integrated into CI/CD (Continuous Integration/Continuous Deployment) pipelines, they become fully automated, resulting in greater efficiency, heightened accuracy and consistency, quicker time to insights, and product scalability.

Another example where automation shines is in scaling workloads. Manual workload scaling is innately error-prone because it's hard to predict changes and the resources needed to handle them, leading to costly over- or under-provisioning. When auto-scaling is applied to workloads in the cloud, as the demand for a workload changes, the amount of resources allocated to support it adapts automatically to demand and performance needs.

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Barrier #6: Inadequate monitoring and analytics

Inadequate monitoring and analytics can become a stopgap for digital products, deterring engineers from tracking product performance and making sound decisions. Conversely, real-time monitoring and analytics are vital to successful digital products as they improve decision-making, ensure peak performance, and reduce downtime.

These observability tools perform Application Performance Monitoring (APM) and log management on the fly. They conduct A/B testing to gain insights into user interactions, detect bugs, enhance security, predict trends for roadmap planning, and assure consistency by following the same practices.

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Barrier #7: Security and compliance concerns

In the digital realm, security and compliance play pivotal roles, as regulatory authorities meticulously examine products to ensure adherence to personal data protection and confidentiality requirements.

Companies must abide by a swath of standards to prevent human data breaches. Some of these standards include GDPR (General Data Protection Regulation), HIPPA (Health Insurance Portability and Accountability Act), ISO/IEC 27001 (Information Security Management), and PCI-DSS (Payment Card Industry Data Security).

Thus, data security is no longer the sole responsibility of information security officers. Engineers must integrate robust security measures, technologies, frameworks, and procedures like encryption and IAM (Identity and Access Management) into developing products to protect personal data and enforce these rigorous standards.

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Barrier #8: Poorly managed dependencies

Managing dependencies (tasks, people, processes, and systems) that rely on each other is integral for determining why tasks are not completed or what went wrong and necessary to ensure cycle time and product quality.

Best practices for dependency management include:

• Identifying dependent functions and where they live.
• Mapping dependencies and making them visible.
• Creating cross-functional Agile teams that are skilled in multiple tasks.
• Embracing continuous improvement involves implementing incremental changes through the PDCA (Plan, Do, Check, Act) cycle, with the aim of reducing unnecessary dependencies.
• Leveraging automation to build dependency management into workflow planning and execution.
• Conducting dependency injection, a programming technique that makes an object independent of its dependencies and creates single responsibility codes.
• Practicing version control to track and manage file changes to identify what has changed by whom, when, and why.

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Barrier #9: Lack of scalability testing

Scalability testing, a form of load testing, evaluates a product's performance across varying workloads. Its absence can result in performance issues stemming from unanticipated surges in user traffic or data load, causing delays in response times and system crashes.

To prevent these issues, engineers should implement scalability testing into the development lifecycle, which can be executed throughout the product's tenure.

The types of performance metrics that measure load and stress include:

• Throughput
• Time analysis
• Response time
• Screen transition
• Performance under load
• Network usage
• CPU / memory usage
• Server response

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Barrier #10: Resistance to change

Resistance to change is inevitable and part of human nature. Organizations can overcome resistance to scalability by embracing shift-left Agile development methodologies, modernizing technical stacks, and investing in upskilling and scalability initiatives.

Nurturing a culture centered around scalability in businesses involves fostering employee engagement. This is achieved by promoting open communication, encouraging cross-functional collaboration, embracing effective change management, providing remote work options, fostering transparency, and creating opportunities for continuous growth and development.

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Conclusion

The absence of digital product engineering can significantly hinder an organization's capacity to adapt to market shifts, potentially stifling growth and constraining innovation.

By overcoming these obstacles, businesses can pave the way to scalable digital products, guaranteeing the success and sustainability of their products in a rapidly evolving digital landscape.

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Resources

https://www.ust.com/productengineering

https://www.ust.com/en/cloud/application-modernization

https://www.ust.com/en/xpanxion/ust-product-and-application-engineering