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 Yet outdated data systems and slow analytics infrastructures leave teams chasing yesterday’s answers.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 modernize your data ecosystem so you can move from reactive to forward-looking without disruption.
TL;DR
Legacy systems eat up to 80% of IT budgets on maintenance alone. Manual data migration causes loss, security gaps, and quality problems. Modern platforms need accessible data to work, but moving it safely is hard. Automated migration accelerators solve this by cutting project time from months to weeks. FLIP converts your Tableau, Cognos, Informatica, SQL Services, and SSRS systems into modern platforms like Power BI , Fabric, and Databricks while preserving accuracy and slashing costs.
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Data modernization services help organizations move from outdated, siloed systems to modern, cloud-based, and scalable data environments.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.
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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 Let’s talk about what outdated infrastructure is really costing you. .
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 Outdated software doesn’t get security patches.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.
What is the 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.
Core Areas of Data Modernization
Data modernization isn’t a single project. It’s a comprehensive transformation across multiple connected areas. Each area tackles specific business challenges while building toward an integrated, modern data ecosystem that delivers real-time insights, AI capabilities, and business agility.
1. Data Warehouse Modernization Transform your traditional data warehouses from rigid, on-premise systems to flexible, cloud-native platforms that deliver faster insights at lower costs. Modern warehouses support real-time analytics, AI integration, and unlimited scaling while cutting down on infrastructure headaches.
Modern cloud architecture scales automatically and only charges you for what you actually use
Real-time data processing replaces those painful overnight batch processing windows
Advanced query engines deliver 10x faster performance through columnar storage and parallel processing
Built-in AI and machine learning capabilities enable predictive analytics without separate tools
Automated migration accelerators handle Oracle, Teradata, SQL Server, and other legacy platform transitions
2. Data Lake Modernization Turn your messy data repositories into organized, high-performance platforms that support both data science exploration and business intelligence. Modern data lakes solve the “data swamp” problem through automated governance, smart storage formats, and advanced processing.
Medallion architecture automatically organizes your data into Bronze, Silver, and Gold quality layers
Advanced storage technologies like Delta Lake provide transaction support and time travel capabilities
Intelligent data cataloging discovers and documents your data assets automatically with lineage tracking
Smart query optimization delivers 5x faster performance through partitioning, caching, and acceleration
Automated lifecycle management reduces storage costs by moving older data to cheaper tiers
3. ETL and Pipeline Modernization Replace those rigid batch processing systems with flexible, scalable data pipelines that handle both streaming and batch workloads. Modern pipelines cut data delays from hours to minutes while improving reliability and letting business users create their own integrations.
Modern ELT architecture leverages cloud computing power to transform data faster than traditional ETL
Real-time streaming processes data as it arrives through Kafka, Event Hubs, and change data capture
Low-code visual interfaces let business analysts build data pipelines without writing code
Intelligent monitoring automatically detects issues, sends alerts, and recovers from failures
Serverless computing scales processing power automatically and charges only for actual usage
4. BI and Analytics Modernization Transform static reporting tools into interactive, self-service analytics platforms that empower everyone in your organization. Modern BI eliminates IT bottlenecks while providing advanced visualizations, natural language queries, and collaborative analytics.
Self-service analytics let business users create reports using drag-and-drop tools and plain English questions
Interactive dashboards enable real-time data exploration with drill-down capabilities and dynamic filtering
AI-powered analytics automatically discover trends, detect anomalies, and suggest actionable insights
Cloud-native platforms provide unlimited user capacity and global access from any device
Migration accelerators automatically convert existing Tableau, Cognos, SSRS, and Crystal Reports
5. AI and Real-Time Analytics Enable predictive insights and automated decision-making by combining machine learning with streaming data processing. Real-time AI capabilities help your organization shift from reactive to proactive, using data to predict and influence what happens next.
Streaming platforms process millions of data points per second with lightning-fast response times
Machine learning models provide predictive maintenance, fraud detection, and personalized customer experiences
Automated decision engines reduce manual intervention while improving accuracy and response speed
Edge computing brings analytics processing closer to data sources for immediate local insights
Real-world applications include dynamic pricing, supply chain optimization, and operational intelligence systems
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
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.
Cognos vs Power BI: A Complete Comparison and Migration Roadmap
A comprehensive guide comparing Cognos and Power BI, highlighting key differences, benefits, and a step-by-step migration roadmap for enterprises looking to modernize their analytics.
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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:
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. SQL Services to Microsoft Fabric Migration FLIP exports your existing SSIS packages, SSAS models, and SSRS reports from SQL Server into standard file formats. You upload these files to the platform and select your target Fabric workspace . The accelerator analyzes everything automatically and converts all components into Fabric data pipelines , semantic models, and reports while preserving your business logic and security settings.
The migration moves you from fragmented SQL Server tools into one unified analytics platform. You eliminate server maintenance and get automatic scaling based on your actual workload. Infrastructure costs become flexible instead of fixed. Your teams can collaborate better because everything lives in the same workspace. Processing speeds improve through distributed computing, and you gain real-time data capabilities that weren’t possible with legacy SSIS constraints.
The migration starts when you connect to your Informatica PowerCenter repository and export workflows, mappings, and transformation logic. These components get packaged with all their dependencies. You then upload the package and configure your target Alteryx environment. The conversion process maps Informatica transformations to Alteryx equivalents while maintaining your data quality rules and business logic.
Moving to Alteryx gives you a more visual development interface that speeds up building and modifying workflows. Licensing costs typically drop because Alteryx uses a simpler pricing model. Your team can prototype faster using drag and drop tools. The platform handles modern data sources better and integrates smoothly with cloud storage . You also get stronger self-service analytics capabilities that reduce dependency on IT teams.
FIRE connects securely to your Informatica PowerCenter repository through standard protocols. You preview and select the mappings and workflows you want to move. Everything gets packaged into a structured format with complete dependencies. Then you upload this package to FLIP, choose your Databricks workspace, and select either Python Spark or Scala Spark. The platform analyzes your objects and converts them into Databricks notebooks while preserving business logic and optimizing for distributed processing.
Databricks eliminates your on-premises infrastructure costs and removes expensive PowerCenter licensing fees. Processing speeds increase significantly through distributed computing across clusters. You gain real-time stream processing that wasn’t available before. Development cycles become faster because teams can collaborate using Git integration. The unified workspace combines data engineering with machine learning capabilities. Scaling happens automatically based on workload demands instead of requiring manual capacity planning.
The accelerator connects to your Informatica repository and extracts workflows, mappings, and business logic into a structured export package. You upload this to FLIP and select Microsoft Fabric as your target platform . The tool analyzes your ETL workflows and converts them into Fabric data pipelines and dataflows. Complex transformations become optimized for cloud-native execution. All business rules, data quality checks, and orchestration logic transfer over while adapting to Fabric’s architecture.
Microsoft Fabric gives you a unified analytics ecosystem that combines data integration with warehousing and business intelligence. Infrastructure management becomes Microsoft’s responsibility instead of yours. You get OneLake storage that works across all Fabric services without moving data. Licensing simplifies because everything runs on capacity units rather than separate product licenses. Performance improves through automatic query optimization and intelligent caching. Your teams gain access to built-in AI capabilities and real-time processing that modern business demands require.
Moving from legacy systems to modern platforms shouldn’t drain your resources or slow down operations. At Kanerika, we’ve built proprietary FLIP migration accelerators that handle 70-80% of the migration work automatically.
Manual migrations eat up months of team time. They’re expensive and mistakes happen when you’re moving thousands of reports, dashboards, or data pipelines by hand.
Our accelerators cut that timeline drastically. What used to take 6 months now takes 6-8 weeks. You save on consulting hours, reduce downtime, and get your teams working on the new platform faster. Moreover, We’ve handled complex data models, custom calculations, and intricate permission structures across hundreds of projects. That experience is built into every accelerator we create.
Move Beyond Legacy Systems and Embrace Data Modernization for Better Insights!
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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.
