Unclear migration objectives
Lack of alignment between data and business teams
Absence of governance and ownership models
Failure to account for downstream impact
Reports and dashboards are not isolated assets. They are part of a broader decision ecosystem. Changes in data models or logic often have cascading effects that are not planned for.
In contrast, technical failures tend to be symptoms, not root causes. A broken dashboard or a slow report is often a manifestation of deeper issues such as poor data modeling, lack of validation frameworks, or rushed execution timelines.
The Core Issue
The underlying issue is that migration is treated as a data movement problem, when in reality it is a data meaning and usage problem.
When enterprises fail to preserve how data is defined, interpreted, and consumed, they risk rebuilding the same inefficiencies in a new environment. This is why many migrations that are completed on time and within budget still fail to deliver measurable business impact.
Understanding this distinction sets the foundation for avoiding the most common mistakes that follow.
Mistake #1: Treating Migration as Lift and Shift
One of the most common and costly mistakes we see is treating BI migration as a direct translation exercise. Reports, dashboards, and data pipelines are moved from legacy platforms to modern tools like Power BI or Tableau without re-evaluating how they were designed in the first place.
At a surface level, this approach appears efficient. It minimizes disruption and accelerates timelines. However, it fundamentally ignores the fact that most legacy BI environments carry years of accumulated inefficiencies.
The Problem with Lift and Shift
Legacy systems such as IBM Cognos often contain:
- Redundant reports with overlapping logic
- Hardcoded business rules embedded in dashboards
- Inefficient data models built around historical constraints
- Metrics that lack clear ownership or consistent definitions
When these are migrated as-is, organizations do not modernize. They simply replicate complexity in a new platform.
This creates several downstream issues:
- Performance bottlenecks persist despite moving to a more capable platform
- Data inconsistencies continue, often amplified due to differences in calculation engines
- Maintenance overhead increases, as teams now manage modern tools with legacy design patterns
What Gets Missed
A lift and shift approach overlooks a critical opportunity. Migration is the point at which enterprises can:
- Rationalize reports and eliminate redundancy
- Standardize metric definitions and data models
- Introduce governance and ownership frameworks
- Align analytics outputs with current business priorities
Skipping this step results in a system that is technically upgraded but operationally unchanged.
What Successful Organizations Do Differently
High-performing teams treat migration as a design reset, not a transfer exercise. They:
- Conduct report and dashboard rationalization before migration
- Redefine data models to align with modern architectures
- Validate business logic and metric consistency
- Use automation-led tools to migrate only what is necessary, not everything that exists
In practice, this means fewer reports, cleaner models, and significantly higher trust in the output.
The key shift is simple but critical. Migration should not ask, "How do we move everything?" It should ask, "What is worth moving, and how should it be redesigned for the future?"
Mistake #2: Ignoring Total Cost of Ownership
A second, equally critical mistake is underestimating the true cost of migration by focusing only on upfront implementation expenses. Many organizations build their business case around licensing savings or infrastructure reduction, particularly when moving to platforms like Power BI. What gets overlooked is the broader Total Cost of Ownership (TCO) across the lifecycle of the new environment.
Where Cost Assumptions Break Down
At the outset, migration appears financially attractive. Legacy platforms such as IBM Cognos or older on-premise stacks carry visible costs in infrastructure, support, and licensing. However, the new environment introduces a different cost structure that is often not fully modeled.
Common gaps include:
- Data engineering and transformation costs
Rebuilding pipelines, optimizing queries, and restructuring data models require sustained effort beyond initial migration. - Ongoing cloud consumption
Storage, compute, and query costs scale with usage. Without optimization, these can exceed legacy costs over time. - Tool sprawl and duplication
Multiple BI and data tools coexist during and after migration, leading to parallel licensing and operational overhead. - Support and maintenance overhead
Modern platforms require continuous monitoring, governance, and enhancement to maintain performance and reliability. - The Hidden Cost Multiplier
One of the most underestimated factors is inefficiency carried forward from legacy systems. When organizations adopt a lift and shift approach, they migrate:
— Redundant datasets
— Inefficient queries
— Unused or low-value reports
These directly translate into higher compute and storage costs in the new environment. What was previously a performance issue becomes a recurring financial burden.
Misjudging Legacy vs New Cost Dynamics
Legacy environments often have fixed and predictable costs. Modern cloud-based BI ecosystems operate on variable consumption models. This shift requires a different financial mindset.
Without proper controls:
- Costs scale with user activity and data volume
- Poorly optimized models lead to repeated compute consumption
- Lack of governance results in uncontrolled data growth
As a result, organizations that expected cost reduction often see neutral or even higher long-term spend.
What Successful Organizations Do Differently
Organizations that manage TCO effectively approach migration with a financial engineering mindset, not just a technical one.
They:
- Build a comprehensive TCO model that includes migration, optimization, and steady-state operations
- Rationalize assets before migration to reduce unnecessary cost carryover
- Implement usage monitoring and cost governance frameworks early
- Align architecture decisions with cost-performance trade-offs
They also evaluate funding strategies, including vendor incentives and co-funded migration programs, to offset initial investment.
The key insight is that cost savings do not come from the act of migration itself. They come from what is migrated, how it is designed, and how it is governed post-migration.
Mistake #3: Choosing the Wrong Partner
Migration outcomes are heavily influenced by the partners involved. Yet, many enterprises treat partner selection as a procurement exercise rather than a strategic decision. This often leads to misaligned incentives, suboptimal execution models, and limited accountability for outcomes.
The Core Issue: Misaligned Incentives
Large system integrators (SIs) and generalist vendors typically operate on effort-based revenue models. The longer and more complex the migration, the higher the billable engagement.
This creates an inherent conflict:
- There is limited incentive to accelerate migration through automation
- Minimal focus on reducing scope through rationalization
- Preference for manual, resource-intensive execution models
As a result, enterprises end up funding inefficiency rather than eliminating it.
Lack of Specialized Expertise
BI and data migration is not a generic IT activity. It requires deep expertise across:
- Source and target BI platforms such as IBM Cognos, Tableau, and Power BI
- Metadata extraction and transformation
- Report and dashboard conversion logic
- Data validation and reconciliation frameworks
Generalist partners often lack this specialization. They compensate with larger teams and longer timelines, which increases cost and risk without improving outcomes.
Vendor-Led Bias
Another common issue is platform bias. Partners aligned with specific vendors may push:
- Tool-specific architectures that limit flexibility
- Migration approaches optimized for licensing expansion rather than business value
- Decisions that lead to long-term lock-in
This restricts the enterprise's ability to make objective, future-ready technology choices.
Limited Accountability for Business Outcomes
Many engagements are structured around delivery milestones, not business success metrics. Once reports are migrated and systems are live, the partner’s role is considered complete.
This leaves critical gaps:
- No ownership of data accuracy post-migration
- No accountability for user adoption or performance issues
- Limited support for optimization and governance
From a CDAO perspective, this is where most value is lost.
What Successful Organizations Do Differently
Organizations that succeed in migration treat partner selection as a capability decision, not just a commercial one.
They:
- Prioritize specialized migration expertise over generic scale
- Evaluate partners based on automation capabilities and accelerators
- Align engagement models with outcome-based metrics, not just effort
- Ensure platform-agnostic recommendations to avoid lock-in
In several successful programs, we have leveraged specialized tools such as Migrator IQ to reduce manual effort, improve accuracy, and accelerate timelines. These approaches shift the focus from labor-driven execution to automation-led transformation.
The key takeaway is clear. The wrong partner does not just slow down migration. It structurally increases cost, risk, and long-term inefficiency.
Mistake #4: Relying on Manual Migration
Despite the availability of advanced tooling, many enterprises continue to rely heavily on manual processes for BI and data migration. This typically involves hand-coding report logic, recreating dashboards, and rewriting data pipelines when moving from platforms like IBM Cognos or Tableau to Power BI. While this approach may seem controlled and flexible, it introduces significant risks that scale with the size and complexity of the migration.
The Problem with Manual Execution
Manual migration is inherently:
- Time-intensive
Each report, dataset, and transformation must be individually analyzed, rewritten, and validated. - Error-prone
Subtle differences in business logic, calculations, or filters can lead to inconsistencies that are difficult to detect at scale. - Difficult to standardize
Different developers interpret and implement logic differently, leading to variability across reports.
As the number of assets increases, these issues compound, making timelines unpredictable and quality inconsistent.
Where Manual Migration Fails at Scale
In large enterprises, BI environments often include:
- Hundreds to thousands of reports and dashboards
- Complex interdependencies between datasets and metrics
- Embedded business logic accumulated over years
Manual approaches struggle to handle this level of complexity. Common outcomes include:
- Inconsistent report outputs between source and target systems
- Incomplete migration of dependencies, leading to broken dashboards
- Extended validation cycles, delaying go-live timelines
From a data leadership perspective, this directly impacts trust in the new system.
The Hidden Cost of Rework
Errors introduced during manual migration are rarely isolated. A single misinterpreted metric or transformation can propagate across multiple reports.
This leads to:
- Repeated cycles of validation and correction
- Increased dependency on subject matter experts for reconciliation
- Delayed user adoption due to lack of confidence in outputs
What initially appears as a flexible approach becomes a high-cost rework loop.
What Successful Organizations Do Differently
Organizations that scale migration effectively adopt an automation-led approach.
They:
- Use specialized tools to extract and convert metadata systematically
- Automate report and dashboard conversion wherever feasible
- Implement standardized validation frameworks to compare source and target outputs
- Reduce manual intervention to exception handling, not core execution
In practice, this significantly improves:
- Speed of migration
- Accuracy and consistency of outputs
- Predictability of timelines and costs
In several programs, we have incorporated automation accelerators such as Migrator IQ to reduce manual effort and ensure higher fidelity in report conversion.
The shift is not about eliminating human involvement. It is about repositioning it from repetitive execution to oversight, validation, and optimization.
Mistake #5: Not Planning for Funding
One of the less visible but highly consequential mistakes in BI and data migration is the lack of a clear funding strategy. Many programs are initiated with a narrow budget view that covers execution, but not the full lifecycle of migration and optimization.
The Core Problem
Migration is often treated as a one-time capital expense rather than a phased investment tied to measurable outcomes.
This leads to:
- Underfunded programs that stall mid-way
- Compromises in scope, quality, or validation
- Inability to invest in optimization post-migration
From a CDAO perspective, this directly impacts data quality, reporting reliability, and long-term adoption.
Missing the ROI Narrative
A common gap is the absence of a clearly defined return on investment (ROI) framework.
Organizations struggle to quantify:
- Cost savings from retiring legacy platforms like IBM Cognos
- Productivity gains from modern tools such as Power BI
- Reduction in manual effort through automation
- Business impact of faster and more reliable insights
Without this, migration is seen as a cost center rather than a value driver, making it harder to secure sustained funding.
Overlooking External Funding Opportunities
Enterprises often miss opportunities to offset migration costs through:
- Vendor incentive programs
- Cloud migration credits
- Co-funded transformation initiatives
These mechanisms can significantly reduce upfront investment, but they require early planning and alignment with partners.
Budgeting Only for Execution Another critical issue is budgeting only for the migration phase, while ignoring:- Data validation and reconciliation
- User training and change management
- Performance optimization and governance
This results in technically completed migrations that fail to deliver business value due to lack of post-migration investment.
What Successful Organizations Do Differently
Organizations that execute effectively treat migration funding as a strategic investment model, not a fixed budget.
They:
- Build a phased funding plan aligned with milestones and outcomes
- Define a clear ROI narrative tied to cost, efficiency, and business impact
- Leverage vendor and partner co-funding programs where applicable
- Allocate budget for post-migration optimization and adoption
In several successful cases, we have seen organizations combine internal investment with partner-led funding models to accelerate migration while reducing financial risk. We have had success with funding from partners like AWS, Salesforce and Migrator IQ
The key insight is that funding is not just about enabling migration. It shapes the quality, speed, and ultimate success of the program.
Mistake #6: Poor Technology Decisions
Technology selection during migration is often treated as a downstream decision. In reality, it is one of the most consequential choices an enterprise makes, with long-term implications for scalability, cost, and flexibility.
The Core Issue
Many organizations select target platforms based on:
- Existing vendor relationships
- Licensing incentives
- Short-term cost considerations
While these factors are relevant, they frequently override deeper architectural evaluation. This leads to environments that are technically functional but strategically constrained.
Lock-In and Architectural Constraints
A common outcome of poor technology decisions is platform lock-in.
When enterprises commit too early to a specific ecosystem without evaluating interoperability:
- Data pipelines become tightly coupled to a single platform
- Switching costs increase significantly over time
- Innovation is limited by vendor-specific capabilities
For example, moving entirely into a single BI and data stack without considering integration flexibility can restrict how tools like Power BI interact with other systems or future technologies.
Misalignment with Workload Requirements
Not all BI and analytics workloads are the same. However, many migrations adopt a one-size-fits-all architecture.
This leads to:
- Over-provisioned infrastructure for simple reporting needs
- Underperforming systems for complex, high-volume analytics
- Inefficient cost-performance trade-offs
Without workload-level planning, organizations either overspend or under-deliver.
Carrying Forward Technical Debt
When migration decisions prioritize speed over design, legacy constraints are often carried into the new environment.
This includes:
- Poorly structured data models
- Inefficient query patterns
- Lack of modular, scalable architecture
Over time, this recreates the same limitations that prompted the migration in the first place.
What Successful Organizations Do Differently
Organizations that make effective technology decisions approach migration as an architecture redesign opportunity.
They:
- Evaluate platforms based on scalability, interoperability, and long-term fit
- Design modular architectures that avoid tight coupling
- Align technology choices with specific workload requirements
- Balance performance, cost, and flexibility rather than optimizing for a single dimension
They also ensure that BI tools such as Tableau or Power BI are selected in the context of the broader data ecosystem, not in isolation.
The key takeaway is that technology decisions made during migration are difficult and expensive to reverse. Getting them right requires a forward-looking approach that prioritizes adaptability as much as immediate functionality.
Mistake #7: Ignoring Adoption
One of the most overlooked yet critical failure points in BI and data migration is user adoption. Many programs are declared successful once reports are migrated and systems go live. From a data leadership perspective, this is only the midpoint. If users do not trust or actively use the new system, the migration has effectively failed.
The Core Issue
Adoption is often treated as a downstream activity rather than a design principle.
Organizations focus on:
- Migrating reports and dashboards
- Ensuring technical compatibility
- Meeting delivery timelines
What gets deprioritized is how users will interact with, interpret, and rely on the new environment.
Why Adoption Breaks Down
Even when migrating to modern platforms like Power BI or Tableau, adoption challenges persist due to:
- Inconsistent metrics and definitions
Users see different numbers for the same KPI compared to legacy systems, leading to immediate distrust. - Changed report structures and workflows
Familiar dashboards are redesigned without adequate transition support, disrupting established decision processes. - Lack of user involvement during migration
Business stakeholders are not engaged in validation or design, resulting in misalignment with actual needs. - Insufficient training and enablement
Users are expected to adapt to new tools without structured onboarding or guidance.
The Trust Deficit
The most significant impact of poor adoption is loss of trust in data.
When users encounter:
- Discrepancies in numbers
- Missing reports or broken dependencies
- Slower or unfamiliar interfaces
They revert to:
- Legacy systems (if still available)
- Offline reports and spreadsheets
- Shadow analytics practices
This undermines the entire purpose of migration.
What Successful Organizations Do Differently
Organizations that achieve strong adoption treat it as a core success metric, not a post-migration activity.
They:
- Involve business users early in the migration lifecycle for validation and feedback
- Ensure metric consistency and transparency through clear data definitions and lineage
- Provide structured training and enablement programs tailored to different user groups
- Monitor usage patterns and adoption metrics post-migration and iterate accordingly
They also align report design and user experience with how decisions are actually made, rather than simply replicating or redesigning dashboards in isolation.
The key insight is straightforward. Adoption is not achieved after migration. It is designed into the migration.
What Successful Migrations Do Differently
After examining the most common failure patterns, a clear contrast emerges. Successful BI and data migrations are not defined by the tools selected, but by the discipline applied in execution, governance, and design.
i. They Lead with an Automation-Led Approach
High-performing organizations minimize manual intervention and rely on automation to drive consistency and scale.
They:
- Use specialized tools to extract, transform, and migrate metadata systematically
- Automate report and dashboard conversion wherever feasible
- Implement repeatable validation frameworks to ensure parity between source systems like IBM Cognos and target platforms such as Power BI
This approach reduces execution time, improves accuracy, and makes outcomes more predictable.
ii. They Treat Migration as a Redesign Opportunity
Rather than replicating legacy environments, successful teams use migration to modernize.
They:
- Rationalize reports and eliminate redundancy
- Redesign data models for performance and scalability
- Standardize metric definitions and governance structures
The result is a cleaner, more efficient analytics environment that is easier to maintain and scale.
iii. They Establish Strong Governance Early
Governance is not introduced after migration. It is embedded from the start.
This includes:
- Clear ownership of datasets, metrics, and reports
- Defined data lineage and traceability
- Standardized development and validation practices
This ensures consistency and prevents the reintroduction of legacy inefficiencies.
iv. They Align Business and Data Teams
Successful migrations are not driven by IT alone.
Organizations ensure:
- Continuous involvement of business stakeholders
- Validation of reports and metrics against real use cases
- Alignment between data outputs and decision-making needs
This directly improves adoption and trust.
v. They Engineer for Cost and Performance
Instead of assuming cost savings, they actively design for it.
They:
- Optimize data models and queries for efficient compute usage
- Monitor and control cloud consumption patterns
- Align architecture decisions with workload requirements
This results in sustainable cost structures rather than unexpected overruns.
vi. They Plan for Adoption as a Core Outcome
Adoption is treated as a measurable objective.
Teams:
- Provide structured training and enablement
- Track usage and engagement metrics
- Continuously refine reports and dashboards based on feedback
This ensures that the new platform delivers real business value.
The Underlying Pattern
Across all successful migrations, there is a consistent shift in mindset.
Migration is not treated as a project with a defined end date. It is treated as a program of transformation that spans design, execution, and continuous optimization.
This is what enables organizations to move beyond technical completion and achieve measurable impact from their BI and data investments.
Authors
