Modern businesses must react more quickly to customer needs and internal demands. Yet outdated data systems and slow analytics infrastructures leave teams chasing yesterday’s answers. According to Forbes , 82% of organizations plan to increase investment in data modernization services.
If you’re a data-driven company, you’ll want a clear plan for how modern and advanced data stacks can replace legacy systems. In this guide, we’ll unpack the key platforms, tangible outcomes, and proven steps to modernise your data ecosystem so you can move from reactive to forward-looking without disruption.
Key Takeaways Legacy systems consume up to 80% of IT budgets on maintenance, leaving almost nothing for innovation or growth. Manual data migration causes data loss, security risks, and quality issues that automated tools prevent. Slow reports, data silos , and inability to run AI workloads signal your business needs modernization now. Data migration is the foundation of modernization because modern platforms need accessible, well-structured data to work.Automated migration accelerators cut project timelines from months to weeks while reducing costs and preserving accuracy. What Are Data Modernization Services? Data modernization services help organizations move from outdated, siloed systems to modern, cloud-based, and scalable data environments. These services cover everything from data migration, integration, and cleansing to governance, analytics, and real-time processing.
The goal is to make enterprise data accessible, accurate, and ready for AI-driven insights. Companies often use platforms like Microsoft Azure, AWS, Google Cloud, Snowflake, or Databricks to modernize infrastructure and improve decision-making.
With these services, businesses replace manual reporting with automated pipelines, connect structured and unstructured data, and reduce maintenance costs while improving agility and compliance with frameworks like GDPR and HIPAA.
Why Data Modernization Can’t wait? Your legacy systems are bleeding money. Every day you delay modernization, the costs climb higher while your competition moves faster.
Let’s talk about what outdated infrastructure is really costing you.
The Real Price of Legacy Systems Organizations spend the majority of their IT budgets just maintaining existing systems. That leaves a small fraction for innovation and growth.
Think about that. Most of your tech budget goes to keeping old systems running instead of building something new.
Legacy systems cost tens of thousands of dollars annually per system just for basic maintenance. But that’s only the visible cost.
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1. Security Vulnerabilities Most technology professionals worry about security gaps in legacy systems. Outdated software doesn’t get security patches. Old encryption standards can’t protect your data . When breaches happen, the costs are massive.
Your legacy systems are essentially open doors for cyberattacks.
2. Integration Nightmares Nearly half of technology professionals struggle to integrate newer technologies with legacy applications. Your old systems weren’t built to talk to modern tools. Every integration requires custom code, workarounds, and manual processes.
This creates data silos. Information gets trapped in outdated systems where no one can use it effectively.
3. Developer Productivity Drain Developers spend a significant portion of their week dealing with technical debt. When you ask them how much time they “waste” on legacy code maintenance, many say it’s close to half their work week.
Your tech talent isn’t building the future. They’re propping up the past.
4. Talent Scarcity Programmers who know legacy languages are retiring. Universities stopped teaching these languages years ago. Few graduates have the skills to maintain your old systems.
Finding people who can maintain your legacy systems gets harder every year. And more expensive.
5. Blocked Growth Most CIOs cite legacy technology complexity as their top obstacle to digital transformation . Your old infrastructure can’t scale when business grows. It can’t handle increased transaction volumes or modern user demands.
When competitors launch new features in weeks, you’re still stuck in months-long development cycles.
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The Cost of Not Modernizing Your Data Infrastructure In the banking sector, legacy tech eats roughly 70 % of IT budgets , stalling innovation and new initiatives. Studies report more than 2.3 trillion USD spent globally on transformation efforts that still struggle, often because the old systems were never replaced. Failing to act means you’re maintaining old tech instead of preparing for future change. The cost isn’t just financial—it hits agility, innovation, and competitive edge.
Addressing legacy systems through smart data modernisation services helps free up budget, enable faster insights, and support the technology needs of tomorrow rather than yesterday.
Signs Your Business Needs Data Modernization Services Your team spends hours waiting for reports that should take minutes. Business decisions get delayed because data queries take too long to run, and dashboards load at a crawl.
Monthly reports that once took hours now require overnight processing Users complain about system timeouts and frozen screens during peak hours Simple data requests from leadership turn into multi-day projects 2. Data Silos Across Departments Critical business information sits trapped in disconnected systems that don’t talk to each other. Marketing can’t access sales data, finance operates in isolation, and no one has a complete picture of your customers.
Each department maintains separate databases with conflicting customer information Teams manually export and email spreadsheets to share basic data Your organization lacks a single source of truth for key business metrics 3. High Maintenance Costs for Legacy Systems You’re spending most of your IT budget just keeping old systems running. Maintenance fees keep climbing while the systems become harder to support and more expensive to operate.
Over 60% of your IT budget goes to maintaining existing infrastructure You’re paying premium rates for consultants who know outdated technologies Hardware refresh cycles drain capital budgets every few years 4. Inability to Support AI and Machine Learning Workloads Your current infrastructure can’t handle modern analytics tools or AI applications . While competitors leverage machine learning for insights, your systems weren’t built for these advanced capabilities.
Predictive analytics projects fail due to inadequate data processing power Your data scientists spend more time preparing data than building models Real-time AI applications are impossible with your current batch processing approach 5. Compliance and Security Vulnerabilities Your legacy systems lack modern security features and struggle to meet evolving regulatory requirements. Data breaches become more likely as outdated encryption and access controls leave gaps in protection.
Audit findings consistently flag data security and governance issues Manual processes for compliance reporting create risk of human error Your systems can’t provide the data lineage and audit trails regulators require 6. Limited Scalability and Flexibility Your infrastructure can’t grow with your business needs. Adding new data sources takes months, system performance degrades as data volumes increase, and expanding to new markets is technically impossible.
System crashes or severe slowdowns occur during peak business periods Adding new product lines requires expensive infrastructure upgrades International expansion is blocked by technical limitations in your current setup 7. Poor Data Quality and Accessibility Business users can’t trust the data they see, and finding the right information feels like searching for a needle in a haystack. Inconsistent data across systems leads to conflicting reports and bad decisions.
Different departments report contradictory numbers for the same metrics Data analysts spend 80% of their time cleaning and preparing data Critical business information exists somewhere but no one knows where to find it 8. Manual Data Processes Consuming Resources Your team wastes countless hours on repetitive data tasks that should be automated. Data entry, validation, and transformation happen manually, creating bottlenecks and introducing errors.
Staff members manually copy data between systems on a daily basis Monthly close processes require all-hands-on-deck manual reconciliation efforts Simple data updates require IT tickets and days of waiting 9. Inability to Access Real-Time Data Business decisions rely on outdated information because your systems only update nightly or weekly. By the time you see the data, opportunities have passed and problems have escalated.
Sales teams quote prices based on yesterday’s inventory levels Customer service reps can’t see current account status during calls Management dashboards show data that’s already 24-48 hours old 10. Difficulty Integrating New Technologies Every new tool or platform requires expensive custom integration work. Your IT team spends months connecting systems that should work together seamlessly, and some integrations prove impossible.
New software purchases sit unused because integration is too complex API connections break frequently and require constant maintenance Cloud applications can’t connect to your on-premise systems Why Data Migration Is an Essential Part of Modernization Data migration forms the foundation of any successful modernization effort. You can’t modernize what you can’t move. Legacy data trapped in outdated systems needs a clear path to modern platforms where it can actually deliver value.
Migration isn’t just about moving files from one place to another. It’s about transforming data structure, improving quality, and establishing it in an environment built for speed and scale. Without proper migration, your modernization project stalls before it starts.
Modern cloud platforms, advanced analytics tools, and AI capabilities all depend on accessible, well-structured data. Migration makes that possible by breaking data free from legacy constraints and positioning it where your business can actually use it.
What Are the Risks and Challenges Associated with Manual Data Migration? 1. Data Loss and Corruption During Transfer Manual data migration significantly increases the risk of losing critical business information during the transfer process. Human errors in copying, pasting, or exporting data can result in incomplete datasets, corrupted files, or permanent deletion of valuable records. Even small mistakes compound quickly when dealing with millions of records across multiple systems.
Manual processes struggle to maintain consistent data formats across different systems and platforms. Date formats change, number formats vary, and text fields get truncated or modified during manual transfers. These inconsistencies create downstream problems that take months to identify and fix, undermining the entire modernization effort.
3. Extended Project Timelines and Delays What should take weeks stretches into months or years when done manually. Staff members can only process limited amounts of data each day, creating massive bottlenecks in your migration timeline. Business operations suffer as projects drag on, and stakeholders lose confidence in the modernization initiative before seeing any results.
4. High Labor Costs and Resource Drain Manual migration consumes enormous amounts of staff time and budget. Your team spends thousands of hours on repetitive data entry and validation tasks instead of strategic work. The cost of dedicated personnel, overtime pay, and opportunity cost of diverted resources quickly exceeds the investment in automated solutions.
5. Human Error and Quality Issues People make mistakes, especially during repetitive tasks involving massive data volumes. Typos, transposition errors, missed records, and incorrect mappings happen frequently in manual processes. These errors compromise data integrity and create trust issues that persist long after migration completes, requiring expensive cleanup efforts.
6. Lack of Audit Trail and Accountability Manual processes rarely maintain comprehensive records of what changed, when, and by whom. When data issues surface weeks or months later, tracing the root cause becomes nearly impossible. This lack of documentation creates compliance risks and makes it difficult to verify migration accuracy or troubleshoot problems.
7. Security Vulnerabilities and Compliance Risks Manual data handling exposes sensitive information to unnecessary security risks. Files get emailed, saved to unsecured drives, or accessed by unauthorized personnel during manual transfer processes. Each touchpoint increases the chance of data breaches, regulatory violations, and compliance failures that can result in significant fines.
8. Difficulty Handling Large Data Volumes Manual methods simply can’t scale to handle modern data volumes efficiently. Organizations dealing with terabytes or petabytes of data find manual migration practically impossible. Performance degrades, errors multiply, and staff burnout becomes inevitable when facing overwhelming data quantities that demand automated approaches.
9. Inability to Validate Data Accuracy Manually checking millions of records for accuracy is unrealistic and ineffective. Spot checks miss systematic problems, and comprehensive validation requires automation. Without proper validation, you migrate bad data to your new system, creating a modernized platform built on a foundation of inaccurate information.
10. Business Disruption and Downtime Manual migration often requires extended system downtime that disrupts business operations. The slow pace of manual processes means longer blackout periods where neither old nor new systems are fully operational. This downtime translates directly to lost revenue, frustrated customers, and damaged business relationships.
11. Missed Data Relationships and Dependencies Complex data relationships between tables, systems, and applications get lost in manual migration efforts. Staff members struggle to identify and preserve these critical connections, resulting in broken links, orphaned records, and systems that don’t function properly. Rebuilding these relationships after migration costs far more than preventing the problem.
12. Limited Testing and Rollback Capabilities Manual processes make thorough testing difficult and rollback nearly impossible. Creating test environments, running validation scenarios, and maintaining backup copies for potential rollback requires automation. Manual approaches leave you committed to whatever gets migrated, with no easy way to undo problems or learn from test runs.
How Kanerika Automates Migrations with Proprietary Accelerators As a premier Data and AI services company, Kanerika understands that enterprises need to upgrade from their legacy tools to achieve growth, drive innovation, and stay ahead of the competition. We also know that manual migration can be extremely time and resource consuming while being highly error prone. That’s where our FLIP migration accelerators come into picture, automating around 70% to 80% of the migration process and significantly saving time, money, and effort.
We offer specialized migration accelerators for:
Azure Data Factory/Synapse to Microsoft Fabric SSIS/SSAS to Microsoft Fabric 1. Tableau to Power BI Migration Organizations switching from Tableau to Power BI benefit from better Microsoft ecosystem integration, lower licensing costs, and enhanced collaboration features. Our automated migration accelerator transfers your entire data architecture to Power BI while preserving all structures and relationships with complete integrity.
Complex Tableau calculations are automatically converted to optimized DAX formulas, maintaining accuracy and performance throughout the transition. Your dashboards are recreated in Power BI with exact functionality, where every chart, filter, and element matches your original design.
Our accelerator uses intelligent automation to optimize conversions, identify improvements, and future-proof your BI environment. This ensures seamless adoption while eliminating the manual effort typically required for such complex migrations.
Companies moving to Talend from Informatica gain open-source flexibility, reduced licensing costs, and improved cloud integration capabilities. FLIP securely connects to your Informatica repository, extracting workflows, mappings, and components into a structured package ready for conversion.
The accelerator automatically detects exported files, validates components, and initiates conversion with real-time status tracking. Informatica elements are intelligently converted into functional Talend jobs while preserving all business logic and transformation rules without manual intervention.
Converted components are packaged and placed in target folders, ready for direct import into Talend Studio. This eliminates manual rework and dramatically reduces migration timelines from months to weeks.
3. Crystal Reports to Power BI Migration Modernizing from Crystal Reports to Power BI enables self-service analytics , cloud-native capabilities, and reduced dependency on legacy systems. The accelerator extracts key metadata from Crystal Reports (RPT files), ensuring seamless transfer of datasets, visual elements, and formulas into Power BI.
Our solution intelligently maps Crystal Reports visuals such as tables, charts, crosstabs, and conditional formatting into their Power BI equivalents. Report structure and design are preserved while gaining modern interactive capabilities that users expect from contemporary BI tools.
Generated PBIX files are ready for direct import into Power BI , automatically populated with relevant datasets and requiring minimal manual adjustments. Comprehensive validation ensures data consistency and business continuity throughout the transition.
4. Cognos to Power BI Migration Transitioning from Cognos to Power BI delivers better integration with Microsoft tools, lower total cost of ownership, and an improved user experience. FLIP connects directly to your Cognos environment using secure credentials, automatically fetching and cataloging all available reports for seamless processing.
The accelerator analyzes complex multi-page reports with various visualizations including bar charts , pie charts, world maps, and list tables. All data connections and calculations are extracted automatically, ensuring nothing gets lost during the migration process.
The entire Cognos report layout converts into Power BI format while preserving all visual components, data relationships, and formatting. You receive a convenient package containing fully functional Power BI dashboards, ready for immediate implementation without manual rework.
5. SSRS to Power BI Migration Businesses adopting Power BI from SSRS embrace cloud-first strategies, better mobile experiences, and enhanced interactive capabilities. The solution extracts comprehensive metadata from SSRS reports including datasets, parameters, and visualizations while maintaining business logic integrity.
Report elements are intelligently mapped to Power BI equivalents, preserving formatting, filters, and conditional elements that users depend on. Our accelerator generates production-ready PBIX files with automated data connections, reducing deployment time from weeks to days.
Built-in validation ensures report accuracy and functionality match original SSRS output. This gives stakeholders confidence in the migrated reports from day one while maintaining continuity in business reporting processes.
6. SSIS to Microsoft Fabric Migration Moving from SSIS-based data foundations to Microsoft Fabric provides cloud scalability, reduced infrastructure management, and modern data integration capabilities. Upload your SSIS .dtsx files to FLIP, which automatically processes existing packages and prepares them for cloud transformation without extensive manual configuration.
FLIP extracts comprehensive SSIS metadata and converts it into optimized Power Queries . All business logic, transformations, and data relationships are preserved during this automated conversion, ensuring your data pipelines function exactly as designed.
The tool generates ready-to-deploy semantic models and Power Query templates directly in your Fabric workspace. Dataflow Gen2 replicates your SSIS logic in the cloud environment, with final validation confirming all structures, connections, and data flows are production-ready.
7. SSAS to Microsoft Fabric Migration Upgrading from SSAS tabular models to Microsoft Fabric brings cloud-native architecture, better performance, and simplified infrastructure management. Upload your SSAS .bim files for comprehensive functionality extraction, where our assessment tool analyzes relationships, hierarchies, roles, perspectives, and calculation groups.
You receive a detailed compatibility report outlining the migration strategy and expected outcomes before migration begins. FLIP programmatically transforms SSAS tabular models into Fabric-compatible semantic models with preserved business logic and analytical capabilities.
The solution generates production-ready semantic models hosted directly in Microsoft Fabric. Original functionality including hierarchies, calculated columns, security roles, and all analytical relationships remain intact, ensuring business users experience no disruption in their analytical workflows.
What Are the Benefits of Migrating with Kanerika FLIP? 1. Time Savings – Cut Migration from Months to Weeks FLIP automates 70% to 80% of the migration process, dramatically reducing timelines. What traditionally takes months of manual effort now completes in weeks, allowing you to realize value from your new platform faster.
2. Zero Disruption – Migrate Without Impacting Ongoing Operations Our migration accelerators work in the background without interrupting your business operations. Teams continue using existing systems while FLIP handles the transition, ensuring business continuity throughout the entire migration journey.
Eliminate the need to hire specialized consultants or divert internal resources from strategic projects. FLIP handles the heavy lifting through automation , freeing your team to focus on innovation rather than repetitive migration tasks.
4. Accuracy Assured – Preserve Report Logic, Visuals, and Calculations Intelligent conversion algorithms maintain complete fidelity of your business logic, formulas, and dashboard designs. FLIP ensures nothing gets lost in translation, preserving the analytical capabilities your business depends on.
5. Cost Efficiency – Reduce Migration Expenses Automated migration eliminates expensive manual labor, reduces consultant fees, and minimizes project overhead. The combination of faster timelines and reduced resource requirements delivers significant cost savings compared to traditional migration approaches.
6. Built-In Validation – Ensure Data Quality and Consistency Comprehensive automated testing validates every aspect of your migration. FLIP checks data accuracy , functional equivalence, and performance metrics, giving you confidence that migrated systems work exactly as intended.
FLIP generates fully functional, deployment-ready files that require minimal post-migration adjustments. Whether it’s PBIX files, Talend jobs, or Fabric semantic models, outputs are ready for immediate implementation in production environments.
Move Beyond Legacy Systems and Embrace Data Modernization for Better Insights! Partner with Kanerika Today.
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Frequently Asked Questions What is the difference between data migration and data modernization? Data migration simply moves data from one system to another without fundamental changes. Data modernization transforms your entire data infrastructure , including architecture, processes, and technologies, to enable advanced analytics, AI capabilities, and real-time insights while improving scalability and performance.
How long does a typical data modernization project take? Timeline varies based on data complexity and scope. Small projects complete in 8-12 weeks, while enterprise-wide modernization takes 6-12 months. Using automated migration accelerators like FLIP can reduce timelines by 60-70%, cutting months-long projects to weeks.
How much does data modernization cost? Costs depend on data volume, system complexity, and migration scope. Small businesses invest $50,000-$150,000, mid-sized companies spend $200,000-$500,000, while enterprise projects range from $500,000-$2 million. Automated accelerators significantly reduce these costs by minimizing manual labor and consultant fees.
Can we modernize data without disrupting business operations? Yes, modern migration approaches enable zero-disruption transitions. Phased migration strategies, parallel system operations, and automated tools allow business continuity. Teams continue using existing systems while new platforms are built and tested, switching over only when everything is validated and ready.
What happens to our legacy systems during modernization? Legacy systems typically remain operational during migration for business continuity. After successful modernization and validation, they’re gradually phased out. Some organizations maintain legacy systems temporarily for compliance or maintain hybrid environments where certain workloads remain on-premise while others move to cloud.
How do you ensure data security during the modernization process? Security measures include end-to-end encryption, role-based access controls, secure transfer protocols, and compliance with regulations like GDPR and HIPAA. Automated migration tools minimize human touchpoints, reducing breach risks. Comprehensive audit trails track all data movements, ensuring accountability and regulatory compliance throughout.
