How AI is Transforming Data Migration Projects
According to Gartner, more than 80% of projects involving data migration exceed their budgets and timelines, while more than 50% of them fail. Costs run 30% higher than planned, and schedules are overrun by 41%.
Most company leaders we speak to face this issue. They would have a whole, well-planned strategy in place, their timelines would be set, timely decisions taken, and everything in place, but when the actual deployment happens, it all goes wrong. There are multiple disruptions, multiple failures that lead directly to much-dreaded downtimes, and yes, you guessed it, extra costs that their companies do not need.
Data migration is a resource-intensive process requiring intensive resources, cost, and time. But when done right with the use of AI, this can be done faster and higher ROI. AI enhances the data migration process; it adds a layer of flexibility and intelligence to the data migration process, making it highly effective and value-driven.
Syren has helped several enterprises eliminate this hurdle to get a full-scale data migration solution. Let’s talk about how AI has helped fast-track this process, leveraging data intelligence platforms like Databricks, and what role Syren plays in this process.
Data Migration Using AI
Every day, businesses are looking for ways to leverage AI to drive digital transformation and gain value.
With AI, businesses have greater visibility to understand their ecosystem. It draws them to figure out what their central source is, if there is bias, where they can find high-quality data, and which type of lower-quality data they can focus on without concern.
Whether in manufacturing, retail, or logistics, the ability to quickly summarize and get relevant information is where AI helps. At the end, actionable insights are what matter for all businesses.
What is the role of AI in data migration?
When AI assists in data migration, it can cleanse, validate, and understand the metadata. It also has the potential to maintain data privacy and compliance. It can help in streamlining workflows. It can elevate the data migration processes by data profiling, validation, mapping, and anomaly detection. The role of AI in data migration is ultimately to lead to a smoother, scalable, and efficient migration process.
Data Generation
AI can create synthetic data that applies to real-world data, enhance data migration, or generate data to fill gaps. This data is then used to train ML models for predictive analysis.
Data Quality & Cleansing
AI is capable of automating data cleansing. It can identify and rectify inconsistencies, flaws, and redundancies, thus accelerating migration and improving data quality.
According to McKinsey, Enterprises using AI for data cleansing report a 40-50% increase in post-migration accuracy
Data Understanding and Mapping
AI can understand semantics and relationships, and learn from data patterns, enabling accurate mapping between source and target schemas. This helps preserve data integrity and context, reducing the risks of data loss during data migration.
Data Privacy and Security
AI can help generate synthetic data while maintaining statistical properties. It can also safeguard sensitive information while ensuring compliance.
Continuous Learning
AI models can be trained to evolve data patterns, enabling dynamic adjustments during data migration. It ensures accuracy and builds resiliency against future changes.
Data Migration Challenges
Enterprise-level data migration is one of the most complicated processes that a business can undergo. Several challenges occur, and without proper assessment and planning, the migration process can be intimidating, expensive, and leave your transformation goals with a bitter taste.
Legacy System Complexity
Traditional systems with outdated functionalities make extraction and integration difficult, leading to custom solutions or extended manual effort.
Data Quality Issues
Unstructured or incomplete data can disrupt the migration process, leading to post-migration costs for data cleansing and quality implementation.
Schema & Format Mismatches
Different data models between source and target environments cause mapping errors, which can lead to data loss or misinterpretation if not properly handled.
Compliance & Security Risks
Moving sensitive or regulated data without strong encryption, masking, or governance frameworks can put businesses at risk of legal and reputational harm.
Downtime & Operational Disruption
Inadequately planned migrations can interfere with day-to-day operations, affecting employee productivity, customer experiences, and revenue generation.
Conclusion
The global AI market is worth a whopping $391 billion, an increase of around $119.5 billion since 2023.
Data migration is not child’s play and must not be treated as such. No matter what path you choose for your migration journey, you are bound to cross paths with AI. However, constantly rushing to use AI for data migration activities without proper consideration and guidance can jeopardize your business and can also lead to ethical concerns.
Using AI in data migration can be significant for a business, but venturing into it responsibly is the key and the challenge. Syren’s data migration solutions align with the right tools and partners for the most effortless, value-driven migration transformations.
Connect with us today to explore how Syren can help you in leveraging AI to turn your data challenges into transformation milestones. Let’s Talk!
