I have been actively involved in assisting various customers with their data migration and data modernization initiatives over the past few years. Reflecting on the challenges they faced and the valuable lessons learned, I believe sharing insights that can benefit the broader community is essential.

In the current landscape, many organizations are transitioning from managing on-premises big data enterprise warehouses such as Oracle, SQL, or Hadoop to cloud solutions like Snowflake, Synapse, or Databricks. This shift is driven by factors such as improved efficiency, cost reduction, scalability, and enhanced user experiences. However, the process of migrating an entire data ecosystem from on-premises to the cloud presents numerous challenges and unforeseen scenarios. A robust data strategy is crucial, taking into account the existing systems, the nature of produced data, usage patterns, and specific requirements across various departments and user segments. Here, I outline key recommendations and considerations that should form a part of your comprehensive data strategy:

Understanding the Portfolio and Planning Capacity 

It's crucial to invest time in understanding the existing landscape, understanding the involved data tools and platforms, and gathering all users impacted directly or indirectly. In larger organizations, this step is often overlooked until the decision to switch or sunset has been made, leading to challenges in user onboarding and training. Assessing requirements during the initial stages aids in planning capacity and securing volume discounts for services like Databricks or Snowflake. Some key elements to include in the early planning or discovery phase are estimated data size (historical and daily volumes), data sources (type and format), user base with varying data needs (data analysts, data scientists, business users, consuming applications, etc.), understanding data ingestion and transformation needs, and identifying data consumption methods and models.

Establish User Personas and Data Accessibility Strategy 

This involves defining user roles and access levels early in the process. Implementing Role-Based Access Control (RBAC) for various roles like Administrator, Data Engineer, and Data Scientist, along with the creation of domain groups, streamlines user onboarding and management. Additionally, assessing security requirements based on data sensitivity is crucial, especially for confidential or Personally Identifiable Information (PII). Identifying the need for column-level masking and defining policies for data masking based on user roles enhances security measures.

Services and Tooling 

Based on your initial assessment, you must map the required toolsets and services to your needs. Here are the key considerations:

• Which cloud provider – Azure, AWS, Google, etc.
• Selection of data stores and new warehouse – Azure Data Lake, AWS S3, Snowflake, Azure Synapse, etc.
• Data ingestion tools depending on the data formats and sources – Data Factory, Azure Synapse pipelines, AWS Glue etc.
• Tooling for ETLs or ELTs – Databricks, dbt, Matillion etc.
• Data Quality and Data Governance – Purview, Collibra, Anomalo, Monte Carlo, etc.

Latency and Performance Considerations 

In building a new data ecosystem, prioritize minimizing delays and boosting performance for timely data availability. Optimize data processing with distributed computing, leverage real-time streaming, and incorporate in-memory databases for quick retrieval. Implement caching mechanisms for frequently accessed data to ensure swift access to high-demand information. These steps collectively contribute to a responsive and efficient data ecosystem.

Data Observability and Data Quality 

To ensure robust Data Observability and Quality, consider establishing dashboards for data ingestion pipelines, conducting accuracy checks with assigned quality scores, and implementing checks for data freshness and availability. Additionally, it includes anomaly detection mechanisms, setting up automated alerts for deviations, encouraging user feedback, performing regular data profiling, and maintaining comprehensive documentation and catalogs for datasets. These measures collectively contribute to a well-monitored, high-quality data ecosystem that meets both observability and quality standards. 

Teams/Org Structure/Various Workstreams 

Creating a resilient and effective data ecosystem involves thoughtful considerations for the structure of data teams and organizational frameworks. Consider establishing clear communication channels and collaboration mechanisms between data teams and other departments to foster cross-functional synergy. Define roles and responsibilities within the data teams, ensuring a balance between specialization and flexibility. Encourage a culture of continuous learning and skill development, given the rapidly evolving nature of data technologies. Implement data governance policies to maintain data integrity and compliance. Consider incorporating dedicated data architects, engineers, scientists, and analysts, ensuring a diverse skill set that aligns with organizational goals. Embrace scalable and agile methodologies to adapt swiftly to changing data requirements. Regularly assess and optimize the organizational structure to accommodate growth and evolving data needs, fostering an environment prioritizing innovation, collaboration, and efficiency within the broader data ecosystem.

Managing the Data Operations 

Establishing and effectively managing an L1 team for data operations demands a strategic approach, beginning with a thorough assessment of the criticality of data sources and mission-critical data pipelines. It’s crucial to identify the level of urgency and sensitivity associated with each data component to determine the need for an L1 or Operations and Maintenance (O&M) team. Establish clear guidelines and protocols for the L1 team, defining their roles and responsibilities in monitoring and responding to day-to-day data issues. Implement proactive measures such as automated alerts and routine checks to ensure prompt detection and resolution of operational issues. Regular training sessions and knowledge-sharing mechanisms should be in place to keep the L1 team well-equipped to handle evolving data challenges. Additionally, foster a culture of continuous improvement within the team, encouraging feedback loops and iterative enhancements to optimize data operations efficiency.

Decommissioning/Sunsetting

The comprehensive inventory from the initial discovery phase (point #1 above) should give you the existing data sources, applications, and infrastructure to identify dependencies and interdependencies. Prioritize data migration based on criticality, starting with non-business-critical functions to validate the new system's efficacy. Establish a phased approach, gradually decommissioning legacy components and verifying data integrity throughout the process. Communicate transparently with stakeholders, providing ample training and support during the transition. Ensure the new cloud-based system aligns with regulatory and compliance requirements and update documentation to reflect the changes accurately. Implement robust data archiving procedures for historical data and monitor closely to promptly address any unforeseen issues. Conduct thorough testing and validation before final decommissioning and continuously assess the performance and security of the new system post-migration. This careful and phased approach ensures a smooth and successful sunset of the legacy data ecosystem while optimizing the benefits of the new cloud-based infrastructure.

User Experience and Onboarding

Begin by understanding user needs and workflows, ensuring the new system aligns with their expectations. Design an intuitive and user-friendly interface, prioritizing simplicity and efficiency. Provide comprehensive training sessions and resources for users to familiarize themselves with the new data ecosystem, offering ongoing support through user forums or help desks. Implement a phased onboarding process, allowing users to acclimate gradually. Solicit user feedback regularly to address any pain points and enhance the UX iteratively. Communicate transparently about the benefits of the new system, highlighting improved functionalities and efficiencies. Establish clear documentation and tutorials to aid users in navigating the new ecosystem independently. Continuous monitoring of user interactions and feedback will enable timely adjustments, fostering a positive and productive user experience within the new data ecosystem.

Data Retention, Archival, Backup, and Disaster Recovery

Effectively managing data retention, archival, backup, and disaster recovery in a new data ecosystem is crucial for ensuring data integrity and business continuity. Consider categorizing data based on its criticality and compliance requirements, guiding decisions on retention periods. Establish automated backup processes to routinely capture and store data securely. Implement a robust disaster recovery plan that includes regular testing and drills to validate its effectiveness. Define clear archival policies, identifying data that can be safely moved to long-term storage. Regularly review and update these policies to align with evolving business needs and regulatory changes. Monitor data lifecycle management closely, ensuring timely deletion of obsolete or non-compliant data. Document all procedures comprehensively to facilitate seamless recovery and adherence to compliance standards. Regularly review and update disaster recovery plans, archival policies, and backup procedures to align with evolving business needs and regulatory changes. This holistic approach to data management supports resilience, compliance, and efficient recovery in the face of unexpected events.

In addition to the considerations above, it's pivotal to address diverse data consumption methods tailored to various user profiles. Understand the unique needs of data analysts, data scientists, business users, and applications consuming data. Evaluate and optimize data delivery mechanisms, visualization tools, and reporting formats to ensure a user-centric approach. This inclusive strategy ensures that the new data ecosystem not only meets technical requirements but also aligns seamlessly with the preferences and workflows of different user groups.

These considerations serve as a starting point for crafting a comprehensive plan for your new data ecosystem. I'm eager to learn about your experiences and challenges from your data modernization journey. Feel free to share your insights or pose any questions in the comments. Your engagement is valued. Thank you for reading!!