The global market for tools that move, sync, or connect data is growing fast. For example, the data integration market was worth over $13 billion in 2025 and is expected to keep rising as companies bring together data from many systems to support analytics and reporting. At the same time, services that help businesses move data to new platforms (like cloud systems) are also booming, with the cloud migration market projected to be valued at more than $148 billion in 2025 as firms switch to more scalable and flexible infrastructures. These numbers show that businesses don’t just want data. They want it in the right system and in the right shape for use.
That is where data migration and data integration come in. While both deal with moving and handling data, they serve different purposes and are used at different stages of a company’s tech setup. Choosing the wrong one can lead to delays, broken reports, or data gaps .
Key Takeaways Data migration moves data from old or on-premise systems to modern platforms, keeping history accurate and complete. Data integration connects multiple systems on a continuous basis, so data stays in sync across tools. Migration is usually a one-time effort during system upgrades or consolidation. Integration runs all the time and supports shared workflows, reports, and automation. Many organizations migrate their data first, then integrate systems for ongoing access. Migration protects historical data for future analysis, while integration supports real-time insights. Best practices include clear goals, clean data , automation where possible, regular checks, and strong security.
What is Data Migration? Data migration is the structured process of moving data from one system, format, or storage setup to another. It is most often used during system upgrades, cloud moves, or when replacing older software. The goal is to move data without breaking business processes, while keeping records complete, accurate, and usable in the new system.
Features of Data MigrationOne-time, project-based task: Data migration is usually planned as a single project with a clear start and end, rather than an ongoing process. Triggered by system change: It happens when an organization upgrades platforms, adopts cloud services, or retires legacy systems. Focus on accuracy and completeness: Strong checks ensure all data is moved correctly, with no missing or altered records. Data transformation and mapping: Data often needs cleanup, reformatting, or field matching to fit the structure of the new system. Tool-supported automation: Migration tools are commonly used to speed up the process, reduce manual work, and lower the risk of errors.
When businesses change their cloud provider, combine databases, switch outdated systems, or launch new company tools, they often migrate data . Likely, older systems cannot work with modern analytics and struggle with scale and speed, making a migration inevitable. The other type of data migration occurs in mergers and acquisitions, where information from various systems must be combined.
Typical Scenarios Moving from on-premise to cloud platforms: Organizations shift data from local servers to cloud storage or cloud apps to improve scale, access, and cost control. Replacing legacy business systems: Older ERP, CRM, or HR tools are retired and replaced with modern platforms that support current business needs. Database consolidation: Data from multiple databases is merged into a single system to reduce duplication and simplify reporting. Adopting modern analytics platforms:
What Challenges Are Commonly Faced During Data Migration? Data migration can be complex and requires careful planning. A bad implementation can result in downtime, loss of data, or compatibility. Lack of uniform data structure , omission of fields, and broken links may cause disruption in operation, and the security and compliance issues are very critical to sensitive data.
Common Issues of Data MigrationData corruption, duplication, or missing records: During migration, data can be altered, duplicated, or lost if validation rules are weak or skipped. This often happens when older systems contain inconsistent or poorly maintained data. System downtime and business disruption: Many migrations require systems to be paused or run in limited mode. If downtime is not planned properly, it can interrupt daily operations, delay transactions, or block access to critical information. Data format and structure mismatches: Older systems often store data in formats that do not align with modern platforms. Differences in date formats, field lengths, naming rules, or data types can cause errors during transfer. Incomplete data mapping and hidden dependencies: When fields, tables, or relationships are not fully mapped, important links between data can break. For example, reports may fail, workflows may stop working, or records may lose their context. Security, privacy, and compliance risks: Data is most vulnerable while being moved. If encryption, access controls, or audit logs are not applied correctly, sensitive information can be exposed.
Data Conversion vs Data Migration: Which Approach Suits Your Project? Explore the differences between data conversion and migration, and how Kanerika handles both.
Learn More
What is Data Integration? Data integration is the process of connecting multiple systems so their data can be combined and used together. It creates a unified, consistent view of data from various sources, enabling teams to access updated information for analytics, reporting, and workflow automation . Integration supports real-time synchronization and cross-platform operations.
Features of Data IntegrationContinuous and ongoing process: Unlike data migration, data integration runs continuously. Data is synced at regular intervals or in real time to ensure systems stay aligned as new information is created or updated. Unifies data across systems and teams: Integration brings together data from departments such as sales, marketing, finance, and operations. This creates a single, consistent view of the business, even though the data lives in different tools. Supports real-time or near real-time updates: Many integration setups allow data to update instantly or within minutes. This is critical for use cases like live dashboards, customer activity tracking, or automated alerts. Enables analytics, reporting, and automation: Integrated data feeds, analytics tools, reports, and automated workflows. This reduces manual work, improves accuracy, and helps teams act on current information instead of outdated snapshots. Uses ETL, ELT, or API-based connections: Data integration relies on technical methods such as ETL, ELT, or APIs to move and sync data between systems. These methods help handle different data formats, structures, and update rules across platforms.
How Does Data Integration Benefit Organizations? The data integration is a means to break silos and improve enterprise visibility. It endorses dashboards, analytics, automated processes , and one hundred percent observation of customers, operations or finances. Organizations use integration to connect with CRMs, ERPs, and marketing tools as well as business intelligence platforms.
Benefits of Data IntegrationBetter data accuracy: When data is integrated, all systems pull from the same source. This reduces the mismatch between reports and dashboards. Teams no longer waste time checking which number is correct. Fewer errors also mean fewer last-minute fixes before reviews or meetings. Faster access to information: Integrated data flows automatically instead of moving by hand. Reports that once took days can be ready in minutes. Managers get updates on time, which helps them act before small issues grow into big ones. Complete business view: Data from sales, marketing, finance, and operations come together in one place. This helps teams see links between actions and results. For example, marketing spend can be matched with sales results without extra effort. Lower cost and effort: Manual data work takes staff time and adds risk of mistakes. Integration reduces repeated tasks like copying files or cleaning sheets. Over time, this cuts tool overlaps and lowers overall system costs. Stronger team alignment: When everyone works from the same data, meetings are smoother. Teams trust the numbers and focus on solutions instead of debates. This improves planning, goal setting, and day-to-day teamwork.
What Are The Common Challenges In Data Integration? Incompatible systems can be difficult to integrate and those with limited API support. There should be a robust infrastructure in real-time synchronization. The mistakes can be either due to bad quality of data or lack of governance, and this can produce inaccurate insights.
Common Issues Data silos and inconsistent data formats: Systems often store the same information in different formats or structures. When these differences are not appropriately handled, data becomes hard to combine and compare. Latency in syncing large volumes of data: As data size grows, keeping systems in sync can slow down. Delays in updates can affect dashboards, alerts, and time-sensitive decisions. API limits and connection failures: Many integrations depend on APIs that have usage limits or stability issues. Changes to an API or unexpected downtime can break data flows. Lack of shared standards across systems: When teams use different naming rules, definitions, or update logic, integration becomes harder to maintain and scale over time. Errors caused by poor data quality: Missing values, duplicates, or unclear business rules can lead to incorrect results once data is combined across systems.
Data Ingestion vs Data Integration: Which One Do You Need? Understand data ingestion vs integration: key differences & Kanerika’s approach to seamless data handling.
Learn More
Data Migration vs Data Integration : Key Differences ExplainedFeature/Aspect Data Migration Data Integration Definition Moving data from one system, format, or storage to another Combining data from multiple sources into a unified view Purpose System upgrades, cloud adoption, legacy system replacement Real-time analytics , reporting, workflow automation Process One-time planned activity with validation Continuous process using ETL, ELT, or APIs Scope Limited to the source and target systems Across multiple systems, departments, and formats Frequency Occasional/one-time Ongoing/continuous Impact on Business Ensures smooth system transition, data continuity Provides unified insights, better decision-making Challenges Data loss, downtime, format mismatch, security risks Data consistency, quality, API limitations, latency Tools Migration software, ETL tools, validation tools Integration platforms, APIs, ETL/ELT frameworks
When Should a Business Choose Data Migration and Data Integration? Choosing between data migration and data integration depends on what a business is trying to achieve, how its systems are set up, and how data is used day to day. While both deal with data movement, they solve different problems. Data migration is best when systems are being replaced or retired. Data integration works better when multiple systems must continue working together.
Understanding the goal first helps avoid costly mistakes, such as migrating data when ongoing connections are actually needed, or trying to integrate systems that are about to be shut down.
Ideal Scenarios for Data Migration When old systems can’t keep up anymore: Your legacy systems stop meeting business needs because they can’t handle the volume, run too slowly, or lack features you need. That’s why you migrate data to newer platforms that actually work for your business. Moving to the cloud: Companies shift databases from their own servers to cloud platforms all the time. Data migration transfers both historical records and active data to the cloud environment. Bringing systems together after big changes: Mergers and acquisitions create data chaos. Different teams use different systems with different information. Migration consolidates everything into one system, so everyone works with the same data. Starting fresh with new business software: New ERP or CRM implementations need your existing data. Customer records, transaction history, and current workflows all move to the new system through migration, and nothing gets left behind. Keeping your data intact: Migration preserves both historical records and operational data while reports still run. Audits have what they need, and daily work continues after the system change because all the data made the move successfully.
Ideal Scenarios for Data Integration Running multiple systems that must work together : Data integration fits well when a business uses several tools at the same time. Systems like CRM, ERP, marketing tools, and support platforms can share data without replacing anything. Each tool keeps doing its job while the information stays aligned. Needing real-time or frequent data updates: Integration works best when teams depend on current data. Dashboards, alerts, and automated workflows need updates to happen often. This is common in sales tracking, inventory updates, and customer activity tracking. Supporting analytics and business intelligence: Teams that rely on reports and analytics use data integration to pull data from many sources into one place. It removes manual exports and helps keep reports consistent across teams. Automating cross-system business processes: Data integration helps when a process touches more than one tool. Customer records can be updated automatically. Orders can sync with finance systems. Actions can trigger based on user activity without manual steps. Keeping systems flexible and future-ready: Integration makes it easier to add or replace tools over time. New systems can connect without large data moves. This supports growth while existing data flows keep working.
When to Use Data Migration and Integration Together Many businesses need both data migration and data integration. They work together during major system changes, when companies replace old platforms while keeping operations running.
Consider a company moving from legacy systems to cloud platforms. They migrate historical data to the new system first. Then they use data integration to sync the old and new systems while teams transition their work processes. This combined approach has clear benefits. It minimizes risk because teams can test the new system while the old one continues to run.
The two methods work better when used together. Migration moves data to a new system. Integration keeps systems connected while that move is happening. Used side by side, they help teams switch systems without breaking daily work.
Phased system replacements: Some systems cannot be shut down at all at once. In those cases, core data moves to the new platform first. Integration keeps data flowing between the old and new systems until the switch is done. Large-scale cloud modernization projects: During cloud adoption, older data is usually moved in bulk. At the same time, integration connects cloud tools with on-premises or third-party systems that are still in use. Mergers and acquisitions with active systems: When companies merge, historical data often move into one central platform. Integration then keeps data sharing active between systems that teams still rely on day to day. Enterprise-wide data platforms: When businesses build shared data warehouses or analytics platforms, large datasets are migrated first. Integration then feeds new data from source systems on an ongoing basis. Minimizing risk and business downtime: Using both methods reduces risk. Teams can test systems, check data accuracy, and move users in stages. Daily operations continue without major interruptions.
Simplify Your Migration Journey with Experts You Trust! Partner with Kanerika for smooth, error-free execution.
Book a Meeting
How Do Data Migration vs Data Integration Affect Analytics? Migration and integration are important factors in how businesses use data to generate analytics. Migration provides access to historical and operational data in the appropriate system. On the other hand, Integration makes sure that data from various sources is linked together and is updated in real-time.
How Data Migration Improves Analytics Getting old data into new systems: Migration takes data stuck in outdated systems and puts it where modern tools can use it. Your old records become available in cloud warehouses and BI platforms, and now you can actually analyze information that was locked away before. Seeing the bigger picture over time: When you migrate years of data, you can spot patterns you missed and see how sales changed over the seasons. Track performance across decades, which helps with forecasting and understanding how your business has grown. Cleaning up messy data: Migration forces you to fix problems. Duplicate records are removed, dates are formatted consistently, and field names are standardized. Your analytics work better when the data underneath is clean. Making cloud analytics possible: After moving data to the cloud, you get access to powerful tools. Run complex queries faster, build better visualizations, and your old servers no longer hold you back.
How Data Integration Improves Analytics Bringing all your data together: Integration connects sales numbers with financial data, marketing metrics with operational data. Instead of jumping between systems, teams see everything in one report. Watching changes as they happen: Connected systems feed live data to dashboards. Managers see metrics updates throughout the day and alerts fire when something needs attention. Therefore, no more waiting for weekly reports. Getting everyone on the same page: Integration makes sure all systems use the same definitions. Revenue means the same thing in every report, and numbers match across departments.Making smart predictions possible: AI and forecasting tools need complete, current data. Integration provides a steady stream of information that these tools require. Better data means better predictions.
Without proper migration, analytics may miss key historical data. Without integration, insights may be delayed or incomplete due to disconnected systems.
Case Study: Modernizing Reporting With SSRS To Power BI with FLIP Client Overview The client is a mid-sized enterprise that uses SQL Server Reporting Services (SSRS) for operational and management reporting. While SSRS dealt with standard reporting needs, it didn’t have the flexibility to do interactive analysis. Business teams wanted to have dashboards that were easier to use, faster to update, and more suitable for day-to-day decision making.
Business Challenges Over time, the existing reporting setup began to limit the organization’s ability to work with data effectively.
Reports were mostly static and offered little room for user-driven analysis Complex SQL queries and stored procedures made report updates slow and risky Business users relied heavily on IT for even small report changes Managing performance and consistency across many reports required ongoing effort These issues slowed reporting cycles, increased IT workload, and reduced the overall value teams could gain from their data.
Solution Delivered Kanerika applied a structured SSRS-to-Power BI migration approach using FLIP, with a focus on accuracy, speed, and usability.
Report metadata, queries, and data sources were extracted automatically to reduce manual work SSRS reports were converted into interactive Power BI dashboards Existing business logic was mapped carefully to preserve calculations and metrics Validation checks ensured data accuracy and stable performance Self-service reporting was enabled so business users could explore data without IT support Outcome The migration helped the client modernize its reporting environment and improve data usage across teams.
Case Study: Enhancing Operational Efficiency through Data Integration The client is a large enterprise operating across multiple business units, using a mix of SAP and non-SAP systems to manage finance, HR, and operational data. Due to the distributed nature of their systems, teams struggled to get a unified view of business performance and operational metrics.
Business Challenges Over time, disconnected systems and manual data handling began to impact efficiency and decision-making.
Data existed in silos across SAP and non-SAP platforms Manual data consolidation caused delays and frequent errors Reports were not updated in a timely manner Business teams lacked a single, reliable view of operational and financial data These challenges increased manual effort, reduced data trust, and slowed operational planning.
Solution Delivered Kanerika implemented a structured data integration solution using Power BI and Azure, focusing on automation, consistency, and visibility.
Integrated data from SAP and non-SAP systems into a centralized reporting layer Automated data pipelines to reduce manual intervention Standardized data models to ensure consistency across reports Enabled near real-time data availability for reporting and analysis Provided business users with reliable, self-serve access to insights Outcome The data integration initiative improved operational efficiency and reporting accuracy across the organization.
Faster and more reliable access to integrated data Reduced manual effort and operational overhead Improved consistency in financial and operational reports Better visibility for leadership to support planning and decision-making
Kanerika’s End-to-End Support for Data Migration and Integration Projects At Kanerika, we specialize in helping businesses move from legacy systems to modern platforms with minimal disruption. Our FLIP migration accelerators make it faster and easier to transition from tools like Informatica, SSIS, Tableau, and SSRS to platforms such as Talend, Microsoft Fabric, and Power BI. We handle the entire process—from assessment to execution—so your data stays accurate, secure, and ready for use.
We also help link systems within your organization to ensure data flows freely and without security concerns . You can use cloud-based or on-premise, or a combination of both, and we ensure that information flows continuously. We are also working on real-time data sync , API automation, and cloud-ready solutions that minimize system gaps. This provides the teams with good information, improved reports, and the day-to-day workings.
What makes our approach different is how closely we work with your team. We take the time to understand your goals, systems, and real challenges. Based on that, we build migration and integration plans that fit how your business actually runs, not just how the technology works. Our work spans various industries, including banking, retail, logistics, healthcare, and manufacturing, and we’ve helped clients reduce costs, enhance data security , and gain better insights.
With Kanerika, you get more than just a service provider; you get a trusted partner in data migration vs data integration. Whether you’re upgrading platforms, connecting systems, or preparing for AI and advanced analytics, we’re committed to making your data work smarter. Let us help you turn your data into a real business advantage.
FAQs 1. What is the difference between data migration and data integration? Data migration is the process of moving data from one system or storage to another, often during system upgrades, cloud adoption, or replacing legacy systems. Its main focus is accuracy and minimal downtime. Data integration, however, combines data from multiple sources into a unified view, enabling easier access, analysis, and reporting. Migration is usually a one-time process, while integration is ongoing.
2. When should a business choose data migration over data integration? Data migration is needed when transitioning to a new system, consolidating databases, or moving to the cloud. Data integration is preferred when a business wants to unify data from different systems like CRM, ERP, or third-party apps for reporting, analytics, and real-time decision-making.
3. Are data migration and data integration used together? Yes, they often work hand in hand. Organizations may first migrate data from old systems to new platforms and then integrate data from multiple sources to maintain continuous flow, up-to-date analytics, and consistent reporting. This ensures smooth operations and better decision-making.
4. What are the main challenges in data migration vs data integration? Challenges in data migration include downtime, data loss, and system incompatibility. For data integration, difficulties often arise with data quality, consistency, real-time updates, and handling multiple formats. Choosing the right tools and planning carefully can reduce these risks.
5. Which is more important for analytics: data migration or data integration? Data integration usually has a bigger impact on analytics because it provides a unified, accurate, and real-time view of data from multiple sources. Data migration supports analytics by ensuring historical and operational data is available in the new system, but integration enables continuous insights and better decision-making.
