Why Migrate Your BI Platform?

Migrating to a modern Business Intelligence platform goes beyond  transferring data and reports; it’s a strategic move to unlock efficiency, innovation, and growth. Organizations gain lower operational costs, AI-powered insights, and automated workflows, all while scaling effortlessly with evolving data demands. Modern BI tools also seamlessly integrate with ERP and CRM systems, strengthening governance and security along the way.Just like moving into a new house, migration provides a rare opportunity to clean up and start fresh. It is the ideal time to remove redundancies, keep only what is valuable, and establish stronger guardrails. With Datalogz’ Control Tower, this process is simplified through end to end visibility, automated metadata management, and proactive identification of critical assets and dependencies, making the entire process smoother and more reliable.

When Is the Right Time to Migrate?

Deciding when to migrate to a new BI platform often depends on organizational triggers and pain points. While every company’s situation is unique, there are common scenarios that signal the time is right:

Cost pressures: Rising infrastructure, licensing, and maintenance expenses create the need for a more efficient and cost effective BI environment.

Data and report overload: Your current BI system struggles with performance bottlenecks, governance gaps, or scaling issues as data volume grows.

Mergers, acquisitions, or restructuring: Organizational change often requires consolidating BI environments, rationalizing tools, aligning reporting standards, and guiding users through transitions.

Push for unified analytics: Modern platforms like Microsoft Fabric combine analytics, governance, and data engineering in one place—ideal for organizations seeking efficiency and collaboration.

These moments are opportunities to modernize your analytics, clean up legacy clutter, and build a stronger foundation.

Step 1: Assess Your Current BI Environment(s)

A successful migration begins with a clear inventory of your existing BI landscape. Without this foundation, you risk migrating outdated assets, overlooking critical dependencies, or missing opportunities to streamline your new environment. Here’s what to review, and how to categorize the elements that make up your environment:

Reports, dashboards, and data sources: Capture all assets, including ETL/ELT processes, user roles, and folder structures.

Asset classification: Identify what’s business-critical, moderately used, or obsolete to prioritize migration efforts.

Health check and cleanup: Flag stale reports, broken dependencies, or outdated configurations. Removing unused assets now prevents unnecessary migration work later.

Dependency mapping: Some reports rely on custom calculations, complex data sources, or specific refresh schedules. Document these relationships early to avoid surprises during migration.

Stakeholder engagement: Work with key users to validate asset importance and align license assignments with actual usage.

Traditional BI inventory methods rely on manual spreadsheets and fragmented documentation, which are prone to errors and inefficiencies. Datalogz modernizes this process through automated metadata extraction, which powers the following key capabilities:

A comprehensive Inventory that automatically documents all BI assets (reports, dashboards, data sources) in a centralized operational catalog, eliminating manual tracking and version control issues.

Intelligent Monitoring that links alerts to assets, flagging issues like unused content, broken dependencies, or performance bottlenecks in real time.

Data-Driven Decision Making Uses extracted metadata to generate:

• Similarity graphs to identify duplicate reports
• Actional Insights and Recommendations with an impact analysis
• Usage analytics to highlight underutilized assets and licenses

By replacing manual processes with automated metadata management, Datalogz reduces human error while providing complete visibility into your BI environment—turning migration preparation from a time-consuming chore into a strategic advantage.


‍(Note: Upcoming operational lineage features will further enhance connection mapping capabilities.)

Step 2: Plan Your Migration Strategy

With a complete inventory of your BI environment in hand, the next critical phase is designing your migration approach. This step determines how efficiently you'll transition, how well you'll manage risks, and how smoothly your team will adapt to the new system. Every migration is different, so you’ll want to take a targeted approach, and strategize carefully. To make it easier to get started, we mapped the strategy to the use case:

Strategy

Best For

Watch Out For

By Department

Clear ownership, defined responsibilities

Cross-departmental dependencies

By Project

Project-based teams

Risk of silos

Data-Driven

Large enterprises (prioritize high-value assets)

Requires robust analytics

Hybrid

Flexible, adaptable organizations

Needs meticulous coordination

By Department: Ideal for organizations with clear ownership and defined responsibilities. This approach simplifies change management but may face challenges with cross-departmental dependencies.

By Project: Suited for project-based teams, ensuring related content stays cohesive. However, it requires careful coordination to avoid silos.

Data-Driven: Focuses on migrating high-value assets first, making it effective for large enterprises. This method demands robust analytics to prioritize effectively.

Hybrid: A flexible, balanced approach that adapts to most organizations. While versatile, it requires meticulous planning and coordination.

Start with a pilot migration where you focus on one department or a set of high-impact reports. Then, create feature mappings between platforms. For example, Tableau workbooks may translate to Power BI reports, and published data sources to datasets. Define clear success metrics, such as data accuracy, performance optimization, user adoption, and cost savings.Datalogz empowers your migration strategy by highlighting your organization’s most engaged departments and top-performing reports. These insights enable you to choose the right migration path and efficiently select pilot candidates, cutting down on preparation time and ensuring a focused, impactful transition.

Step 3: Prepare the New BI Environment

Execution is where the strategy becomes reality. This includes rebuilding reports, reestablishing data connections, and validating visuals and calculations. Key metrics should be compared between old and new platforms. User Acceptance Testing (UAT) must be carried out, along with performance testing, to optimize query speed and refresh schedules. Training sessions should be role-based, tailored for analysts, executives, and IT teams.

Datalogz partners with consulting firms that provide accelerators and tools to enable faster, more streamlined migrations. During execution, Datalogz adds value with BI Similarity Tracking, ensuring that the most important reports are rebuilt in the new platform with a high similarity score and without missing critical dependencies. Usage analytics then highlights power users who can serve as platform champions, while governance monitors deliver immediate visibility into compliance and confirm that rules are being followed as the new environment goes live.

Understand the asset model
Review how assets are named, categorized, and organized. This foundation preserves consistency and makes the new environment intuitive for users.

Replicate folder structures
‍Mirror existing folder hierarchies where appropriate, maintaining familiarity and continuity for business users.

Configure roles and permissions
Define access levels carefully, setting up both user roles and row-level security to safeguard data integrity and compliance.

Plan for capacity
‍Estimate user concurrency and data volumes to size the system appropriately. This ensures performance remains reliable as adoption scales.

Select the right licensing model
‍Choose a licensing strategy that balances current cost efficiency with flexibility for future growth and evolving usage patterns.

Elevate governance
‍Use migration as the moment to strengthen governance. Align controls with current priorities, including security, data quality, and reporting discipline. Establish clear ownership, document standards, and define accountability mechanisms to prevent sprawl in the new environment.

Datalogz streamlines preparation by integrating with Entra ID to map users and departments to the right data. It analyzes usage patterns to optimize licensing costs, and estimates compute requirements from historical workloads to right-size resources. With these important metrics in place, you’ll begin the journey of a new BI platform with maximum visibility into your environment, setting the stage for optimization and proactive governance to be activated from the start.

Datalogz also built its own capacity estimator. Using standardized metadata about your source environment we map customizable parameters to the target environment to derive a baseline capacity estimate. This can then be used to adjust various non-metadata measures like anticipated usage to further refine the estimate.

Step 4: Execute the Migration

Execution is where the strategy becomes reality. This includes rebuilding reports, reestablishing data connections, and validating visuals and calculations. Key metrics should be compared between old and new platforms. User Acceptance Testing (UAT) must be carried out, along with performance testing, to optimize query speed and refresh schedules. Training sessions should be role-based, tailored for analysts, executives, and IT teams.

• Rebuild reports and connections.
• Validate visuals, calculations, and metrics.
• Run UAT and performance tests.
• Train users by role.

Datalogz partners with consulting firms that provide accelerators and tools to enable faster, more streamlined migrations. During execution, Datalogz adds value with BI Similarity Tracking, ensuring that the most important reports are rebuilt in the new platform with a high similarity score and without missing critical dependencies. Usage analytics then highlights power users who can serve as platform champions, while governance monitors deliver immediate visibility into compliance and confirm that rules are being followed as the new environment goes live.

Step 5: Go Live, Monitor and Drive Adoption

Migration doesn’t end once reports are rebuilt and users are trained. The post-migration phase is about stabilizing the new environment, driving adoption, and continuously improving performance, governance, and cost efficiency. This ensures your BI platform delivers sustainable value long after go-live.

Go-live should be gradual rather than abrupt. The old platform should be phased out only once reports in the new environment are validated. Running both systems in parallel for a time allows users to adapt smoothly. Performance monitoring with Fabric Capacity Metrics or Looker Admin Panel is critical, as is ongoing feedback collection.

Datalogz provides the observability and guardrails across BI tools needed to keep your BI environments healthy and cost effective before, during, and after migration:

Adoption Insights: Surfaces usage trends and highlights where additional training or support may be needed.

Governance Monitors: Continuously tracks governance rules to ensure compliance and prevent sprawl.

Security Monitors: Provides visibility into user permissions and data entitlements, flagging potential risks.

Performance Monitors: Measures query execution and refresh speeds, enabling proactive tuning.

Cost Monitors: Delivers real time visibility into licensing and compute costs, surfacing inefficiencies and enabling proactive cost control.

Capacity Monitoring: Connects usage analytics with compute and RAM insights to enable holistic tracking of system load. This allows you to plan ahead to avoid overages, right size infrastructure, and optimize spend while maintaining performance.

Step 6: Leverage Advanced Capabilities

AI powered insights: Apply machine learning to uncover patterns and forecasts.

Natural language queries: Allow users to ask questions in plain language.

Automated reporting: Schedule reports, set alerts, and generate narratives automatically.

Streamlined workflows: Automate ETL and ELT processes for faster, cleaner data access.

Collaboration tools: Enable commenting, sharing, and visibility across teams.

Datalogz extends these capabilities with operational tags that can be used to train large language models for deeper, domain-specific intelligence. Governance and security monitors keep advanced features compliant and safe, while performance monitors track the impact of AI workloads on queries and refreshes. Cost and capacity monitoring adds visibility into compute and RAM usage, helping you plan ahead, prevent overages, and optimize spend. Together, these capabilities let your organization innovate confidently on a strong, governed BI foundation.

Common Pitfalls to Avoid

Most BI migrations fail because of common pitfalls such as blind lift and shift, broken dependencies, uncontrolled BI growth, poor training, or weak governance. The result is wasted spend, frustrated users, and security risks.

Datalogz eliminates these risks. With continuous monitoring of permissions, security, performance, and costs, along with built in governance guardrails, you can launch your new BI environment lean, compliant, and ready to scale. No wasted reports. No missed dependencies. No costly errors.

With Datalogz, every migration becomes a clean start and a competitive advantage.

Need Help?

BI migrations don’t have to be risky, complex, or resource-draining. Partner with our BI migration experts and accelerate your journey with Datalogz Control Tower.