Taking an Organisation from On-Prem to the Cloud

The Complexity of Cloud Migration

Transitioning from on-premises systems to the cloud is far from a straightforward lift-and-shift process. Successful cloud migrations require meticulous planning and strategic decision-making. Key considerations include:

• Change Management – Human resistance to change is one of the biggest obstacles in cloud migration. People often do not fully understand what cloud migration entails, making education and buy-in from senior leadership and end users critical.
• Cost vs. Value Analysis – Moving to the cloud must make financial sense. Leaders must evaluate the return on investment (ROI) and assess whether automation and cloud infrastructure will genuinely add value.
• Automation with Purpose – Not every process should be automated. If automating a task takes longer than manually performing it, it may not be worth the investment.
• Prioritization & Business Alignment – Cloud migration efforts should align with business KPIs. Understanding what the company aims to achieve will guide prioritisation and ensure that data initiatives deliver tangible value.

Additionally, companies must assess their current data landscape and identify bottlenecks that can be resolved through cloud migration. Understanding how different business units interact with data and ensuring that their needs are met should be part of the initial planning phase. Without this, cloud migration efforts can lead to inefficiencies and misalignment with business goals.

A strong cloud migration strategy must also include a clear roadmap. This roadmap should outline milestones, risk assessments, contingency plans, and success metrics. Teams should continuously evaluate progress to ensure that the migration aligns with business priorities and does not disrupt ongoing operations.
 
Common Challenges in Cloud Migration

Organizations face numerous technical and operational challenges when migrating to the cloud, including:

• Unrealistic Expectations – Stakeholders often expect immediate benefits, but migrations require careful planning and extensive testing.
• Legacy Data Models – Lifting and shifting existing systems without re-architecting them can lead to inefficiencies and increased costs.
• Lack of Proper Testing – Insufficient testing can cause pipelines to break post-migration, leading to loss of trust in the system.
• Technical Debt – Failing to optimise during migration creates long-term issues that are costly and time-consuming to fix.
• Security and Compliance Risks – Moving to the cloud introduces new security concerns, requiring organisations to implement stringent governance and security policies.
• Stakeholder Buy-In – Without strong executive sponsorship, data teams may struggle to secure the necessary funding and resources for a successful migration.

Businesses should avoid accumulating technical debt by planning migrations properly. Simply moving legacy systems to the cloud without optimising them can make operations slower and more expensive. To mitigate risks, companies should allocate sufficient time for testing, ensure that data models are optimised for the cloud environment, and consider hybrid models when full migration isn’t feasible immediately.

One approach that works well is conducting parallel runs, where the legacy system and the new cloud platform run simultaneously for a period before fully decommissioning the old system. This allows businesses to identify any discrepancies and adjust their strategies before making a complete transition.

Another challenge often overlooked is the cultural shift required for cloud adoption. Employees accustomed to traditional on-prem systems may need additional training and reassurance to embrace cloud-based solutions. A well-structured change management program can help ease this transition.
 
Building the Right Data Team for Cloud Success

The right team structure is crucial for a smooth transition to the cloud. Companies should consider a mix of skill sets, including:

• Data Engineers – Responsible for designing, building, and maintaining the cloud data pipelines.
• Cloud Architects – Ensuring that the infrastructure is scalable, cost-effective, and secure.
• BI and Analytics Experts – Providing insights and ensuring that data is leveraged effectively for business decision-making.
• Security Specialists – Ensuring compliance with governance policies and protecting sensitive data.

Hiring individuals with experience in both legacy and cloud environments is beneficial, as they can bridge the gap between old and new technologies. Training internal teams on cloud best practices is equally essential.

The Role of Data Governance and User Adoption

Data governance should not be an afterthought in cloud migration. Organisations should integrate governance principles from the beginning, including data access policies, security protocols, and compliance measures.

Ensuring data quality and establishing a standardised data governance framework can prevent common issues such as inconsistent data definitions, duplication, and lack of access controls. Businesses should work closely with cybersecurity teams to ensure that cloud environments remain compliant with internal policies and external regulations.

Another key aspect is user adoption. Conducting beta testing, gathering user feedback, and providing training help teams transition smoothly to new cloud-based tools. Additionally, measuring usage analytics of reports in legacy systems can help businesses determine which reports should be migrated, preventing unnecessary work and costs.

It’s also crucial to involve business users early in the process. By incorporating their feedback and demonstrating the benefits of cloud-based reporting and analytics, organisations can increase adoption rates and improve the overall success of their migration efforts.

A common pitfall is assuming that users will adapt without support. Providing hands-on training sessions, user guides, and accessible support teams can significantly improve adoption rates and ensure that employees are confident in using the new system.

Gen AI and the Future of Data Engineering

With the rise of Generative AI (Gen AI), the role of data professionals is evolving. Tools like Databricks Assistant are improving productivity by diagnosing errors in SQL queries and assisting with debugging. AI-powered assistants can help reduce the time spent on troubleshooting, allowing data engineers to focus on strategic initiatives.

For example, AI-driven assistants can scan an organisation’s metadata to suggest more efficient ways to optimise queries, identify inefficiencies in data pipelines, and even automate certain aspects of data transformation. This enhances overall efficiency and allows teams to deliver insights faster.

However, AI cannot replace the need for human judgment in data modeling, architecture, and business alignment. While AI can automate certain processes, it still requires human input for requirements gathering, solutioning, and strategic decision-making. Data teams must continuously refine their skills in AI-assisted engineering while maintaining a deep understanding of traditional data management principles.

Looking ahead, businesses should consider AI as a co-pilot in their cloud migration journeys. AI can assist in data cataloging, suggest best practices for governance, and help identify areas where automation can yield the highest return on investment.

As AI adoption grows, organisations should also ensure ethical AI usage by implementing proper governance policies. Transparency, accountability, and bias detection should be fundamental considerations when deploying AI-driven solutions.

Final Thoughts

Migrating from on-prem to the cloud is not just a technical challenge; it’s a strategic initiative that requires cross-functional collaboration and meticulous planning. By focusing on business alignment, prioritisation, governance, and automation with intent, organisations can navigate the transition more effectively.

The future of data engineering will be shaped by automation, AI-driven optimisation, and a greater emphasis on strategic problem-solving. Businesses that successfully integrate these elements into their cloud strategy will gain a competitive edge and maximize the benefits of their data initiatives.

For a deeper dive into insights and experiences, tune in to the full *Lead with Data* podcast episode.