Are you ready for application and data modernization?

Successful application modernization means evaluating multiple readiness factors. Your organization is only as ready as its people, infrastructure, development processes, data management, application estate, and operations.

A readiness assessment helps you identify potential gaps and develop a strategic plan to address them. This comprehensive evaluation ensures that your organization is prepared to embrace modern technologies, enhance operational efficiency, and drive innovation.

Modernizing applications is not just about upgrading technology. It’s also a cultural transformation that affects the way you operate and deliver value to your customers. With a clear understanding of the current state and a well-defined roadmap, you can navigate the complexities of modernization with confidence.

We recommend fostering a culture of continuous improvement. This mindset enables you to take advantage of the latest advancements in technology, achieve sustainable growth, and maintain a competitive edge in today’s dynamic business environment.

The following sections outline the key areas to assess when evaluating enterprise readiness for application modernization.

Assess organizational maturity

A maturity assessment shows you the capabilities and readiness of your development teams. You can use the Developer Velocity Index (DVI) to assess your teams and get guidance. The index evaluates tools, culture, and processes that impact developer productivity and helps you progress over time.

For example, your DVI score can help you enhance productivity and collaboration and make choices about tools and platforms, such as Azure DevOps, GitHub, and Visual Studio. You can also compare your DVI score with industry standards to get a benchmark and identify areas for improvement.

To help teams ramp up as they take on new technologies and platforms, AI copilots can play a role. For example, Copilot in Visual Studio and Copilot in GitHub help boost developer agility by providing code suggestions, optimization features, and inline documentation.

Assess infrastructure readiness

To evaluate your technology landscape, assess the following:

• Hardware and software. Assess whether your current hardware and software can support modern applications and AI workloads.
• Scalability. Ensure your infrastructure can scale to meet future demands.
• Cloud integration. Evaluate readiness for integrating with cloud services to enhance capabilities.

To review network connectivity, assess the following:

• Bandwidth and latency. Ensure your network can handle the increased traffic and low latency requirements of modern applications.
• Redundancy. Implement redundant network paths to ensure high availability and reliability.
• Security. Ensure robust network security measures are in place to protect data and applications.

To manage dependencies, assess the following cloud integration points:

• Hybrid cloud. Evaluate the readiness for a hybrid cloud setup, where some resources are on-premises and others are in the cloud.
• Multicloud. Consider the ability to integrate and manage resources across multiple cloud providers.

To assess monitoring and management, review the following:

• Tools and platforms. Implement monitoring tools to track performance, availability, and security of your infrastructure.
• Automation. Use automation for deployment, scaling, and management to improve efficiency and reduce manual intervention.

Assess process readiness

Application modernization requires efficient development processes for versioning source code and a workflow for continuous integration and continuous deployment (CI/CD). Consider the following best practices:

• Set up a source code repository. You can use GitHub to build a single repository for all projects (monorepo) or create one for each project (multirepo).
• Follow a branching strategy, commit changes frequently, and use pull requests to propose changes.
• Implement a code review process to ensure code quality, consistency, and adherence to best practices. Encourage constructive feedback and collaboration.
• Set up semantic versioning and maintain a change log.
• Automate build processes using CI/CD pipelines and the environment of your choice, such as GitHub Actions, Jenkins, or Azure DevOps. Integrate automated unit, integration, and end-to-end testing to ensure code quality and catch issues early.
• Use monitoring and alerting systems to track the performance and health of applications after deployment.

To learn more about DevOps practices and tools, see What is DevOps?

Assess data management readiness

A review of the current state of your data includes data sources, types, volumes, and storage locations. High data quality and robust governance frameworks are critical for reliable AI outcomes.

Assess the following:

• Data quality. Evaluate the accuracy, completeness, and consistency of your data. Quality goes with governance and includes data cleansing, validation, and compliance with data privacy regulations.
• Data governance. Ensure policies and procedures are in place for data management, including data security, privacy, and compliance.
• Data lakes and warehouses. Make sure your modern data infrastructure includes data lakes and warehouses to make large volumes of structured and unstructured data readily available for AI training and inference.
• Real-time data processing. Consider the needs of AI models, which use real-time data to make timely and accurate predictions that help enhance decision-making.
• Data integration. Check the ability to integrate data across different systems and platforms.
• Data migration and modernization. Make sure your plan includes data cleansing, transformation, and validation.

Assess application estate readiness

To ensure that your application estate is ready for modernization and AI integration, consider the following factors.

Assess application health

• Assess the current performance, stability, and scalability of your applications.
• Monitor key metrics such as response times, error rates, and resource utilization.
• Identify applications that are underperforming or experiencing frequent issues so you can prioritize the ones to modernize or optimize first.

Review your technology stack

• Review your current technology stack help to determine the level of effort required to migrate and modernize.
• Evaluate the technologies used by your applications and their compatibility with modern platforms.
• Review programming languages, frameworks, databases, and other tools.

Review licensing and support

• Review the licensing agreements and support contracts for your existing applications.
• Ensure that you have the necessary licenses to continue using the software.
• Make sure you have access to support if needed.

Assess modernization potential

• Consider current performance, scalability, and alignment with your business goals.
• Look for applications that are underperforming, experiencing frequent issues, or struggling to meet growing demands.
• Assess the technology stack used by these applications to determine if they rely on outdated or unsupported technologies that could benefit from an upgrade.
• Consider the strategic importance of each application to your business operations. Business-critical applications that don’t use modern architectures or technologies are prime candidates for modernization.

• Use a framework like the Eisenhower Matrix to assess applications based on their business value and the effort needed to modernize.
• Engage with stakeholders to understand their pain points and gather insights on which applications could deliver the most value if modernized.

Assess enterprise readiness for AI adoption

One of the key goals of the App Modernization Guidance is to prepare your organization to implement responsible AI capabilities. A frank assessment of your infrastructure, data governance, and team expertise will tell you whether your organization is ready. For example, to take advantage of Azure OpenAI Service and language models, you need to ensure that data is well-organized and accessible, and that teams are proficient in AI technologies.

To ensure ethical and fair AI practices, your organization should define its approach to responsible AI. A framework establishes guidelines for transparency, accountability, and fairness in AI systems. By implementing robust governance and oversight mechanisms, you can mitigate biases, ensure compliance with ethical standards, and build trust with stakeholders.

The following best practices can help your organization successfully infuse AI capabilities.

Support data integration using data lakes

Data lakes provide an enterprise-scale solution for storing vast amounts of raw data from diverse sources. Effective data integration ensures that this data is readily available for advanced analytics and AI-driven insights—the foundation of any AI strategy.

Maintain high data quality

High-quality data is the backbone of AI. To ensure data quality, you must implement rigorous data validation, cleansing, and enrichment processes for the data that feeds your AI models. Accurate and reliable data enhances the overall effectiveness of AI initiatives.

Choose model training or RAG architecture

With so many approaches to generative AI, you need to determine whether traditional model training or retrieval-augmented generation (RAG) architecture is most suitable for your needs. Model training builds predictive capabilities from historical data, while RAG architecture combines data retrieval with generative models, offering real-time insights and enhanced performance for sensitive data applications.

Streamline with machine learning operations (MLOps)

For operational efficiency, we recommend implementing machine learning operations (MLOps), an integration of machine learning and DevOps practices. MLOps practices help you streamline the deployment, monitoring, and management of AI models. By adopting MLOps, you help ensure that AI models are delivered efficiently and reliably, fostering collaboration between data science and operations teams and accelerating AI adoption.

Use model versioning for compliance and reproducibility

Robust model versioning helps you track changes, ensure reproducibility, and manage multiple iterations of AI models. This practice is essential for maintaining compliance with regulatory standards and for facilitating continuous improvement of AI solutions.

Ensure data security

Data security is paramount for anyone integrating sensitive data into AI workflows. You must implement robust security measures, such as encryption, access controls, and continuous monitoring to help protect data from unauthorized access and breaches. Appropriate data security measures also help you comply with data protection regulations and maintain stakeholder trust.

Use API management for seamless integration

To support efficient data access and integration, you need an API management solution. By providing secure and scalable APIs, you can make your data and services easily accessible to approved stakeholders and applications—a step that enhances overall efficiency and supports AI-driven decision-making.

Avoid data drift

Data drift occurs when the statistical properties of the input data change over time, potentially degrading model performance. You must implement monitoring systems to detect data drift and retrain models as necessary. You should also regularly update models to ensure they remain accurate and effective in changing environments.

Assess operational readiness

Operational readiness ensures that you’re using the optimal tools and methodologies to streamline processes. Operational readiness is also a mindset.

We recommend the following best practices.

Adopt DevOps practices

DevOps practices support efficient collaboration and software development processes and include the following:

• Continuous integration (CI). Integrate code changes frequently into a shared repository, ensuring early detection of integration issues.
• Continuous delivery (CD). Automate the process of packaging, testing, and deploying software to ensure swift and reliable releases.
• Automation. Use automation tools to streamline the build, test, and deployment processes, reducing manual intervention and errors.
• Monitoring and feedback. Implement continuous monitoring and feedback loops to identify and resolve issues quickly.

Plan for incident management

Create an incident response plan that document how to handle unexpected issues or outages. After an incident, conduct a review to analyze the root cause and implement preventive measures.

Manage changes

A best practice in project management is to establish a change control board (CCB) to review and approve significant changes during your application modernization projects. The CCB should maintain detailed change logs of all changes made to your system, including the rationale and impact.

Develop training and skills

Operational readiness requires people with the right training. Regularly assess the skills of team members and identify gaps. Then provide training and upskilling opportunities to ensure your teams are proficient in modern tools and practices.

Establish a culture of incremental innovation

When teams feel free to experiment, a culture of innovation naturally arises. Make sure your teams can test new ideas and learn from their failures. Application modernization efforts benefit from a culture of incremental improvements, where the focus is on making small, continuous improvements rather than large, infrequent changes. To further drive innovation, foster cross-functional collaboration among development, operations, and other stakeholders to drive innovation.

Get agile

To manage projects and workflows efficiently, adopt Agile methodologies such as Scrum or Kanban. An Agile mindset helps teams and processes stay flexible as requirements and priorities change. It also helps you respond to feedback and deliver value quickly with its emphasis on rapid iteration cycles.

Deliver operational excellence

To achieve operational excellence, you must focus on customer and stakeholder value by keeping an eye on revenue, risk, and cost impacts. To do this, you can:

• Implement practices to ensure the reliability and availability of applications and services.
• Integrate security practices into the development and deployment processes using DevSecOps practices.
• Ensure compliance with industry regulations and standards.

For more ideas, see the Azure Operational Excellence documentation, an organized collection of service specific guidance for streamlining operations.

Next steps

Maximize value in your application modernization plan