Executing a successful EA strategy involves addressing multifaceted challenges with coordinated solutions. A few of these challenges and some strategies to mitigate them are described below.
Securing Stakeholder Buy-in
Bringing together diverse leadership and IT teams with business stakeholders is necessary for a successful EA strategy but can be difficult to accomplish. Active communication channels that highlight EA benefits, shared governance with stakeholders, pilot projects that showcase potential, and wedding EA models with business KPIs all help in this regard.
Managing Resource Allocation
EA strategy initiatives require dedicated budgets and skilled talent, which are hard to secure given competing priorities. Associating resources with business KPIs, centralizing EA funding models, and actively upskilling talent can help overcome these issues.
Integrating Emergent Technology
Learning and applying new technologies helps keep tech talent engaged, but the use of new tools must seamlessly integrate with legacy IT systems and support wider business objectives. Pilots, prototypes, and feature flags are useful to demonstrate the safe introduction of technical change and ensure that changes do not adversely impact existing systems. This helps foster a culture comfortable with change, continuous integration, and delivery.
Ensuring Scalability and Flexibility
EA artifacts need to handle both success and failure gracefully. Resources like system architecture diagrams, capability maps, and data models must display stability while also accommodating surges, shifts, and changing business needs over time.
Hard lines and rigid EA structures cause friction between tech teams and the wider business. A successful EA strategy accounts for changes in scale and focus over long durations by insulating EA models with modular, service-based designs and abstraction layers that are easily extended and managed.
Implementing Effective Data Management
Capturing quality data and keeping it secure lies at the bedrock of any data-driven business. Data dictionaries, decision records, master data management, and data governance policies all help enforce standards for reliable data.
GDPR, CCPA, and other legal frameworks feed into EA strategies for data management and may ultimately dictate the level to which data standards apply throughout the enterprise.
Establishing Governance and Compliance
Cross-team EA programs need oversight and decision-making processes such as architecture review boards, change control workflows, and communication schedules. Templates, archives of best practices, policy documents, and solution designs allow for consistency in governance and external compliance requirements.
Oversight and decision-making may be structured hierarchically or distributed throughout an organization. As long as established patterns and processes for compliance and governance are effective and documented, EA programs can be successful.
Handling Complexity
Navigating the complexity of aligning diverse business requirements, integrating legacy systems, and managing data architectures and technologies present significant challenges when developing an EA strategy. Some strategies to overcome these challenges include:
- Iterative Development: Releasing smaller changes more often.
- The Pareto Principle (80/20 rule): Improving developer productivity by streamlining workflow, reducing mundane tasks, and allowing developers to focus on creative tasks.
- Abstraction: Simplifying the complex details of an EA strategy by presenting only the essential features of a system, component, or concept with the level of abstraction adjusted for different audiences, such as developers, product owners, and other stakeholders.
- Modular Design: Breaking down a complex system into smaller modules or components.
Successful EA strategies adapt to change and foster tech cultures comfortable with constant movement. Through robust frameworks and well-defined processes, organizations can overcome these and other challenges to EA execution.