How Does AI Solve Data Migration Challenges

In technology transformation programs, data migration is one of the key success factors. But simply transferring data from a legacy system to a new one does not guarantee a healthy migration.

Addressing potential roadblocks requires time, resources, and money. So can AI overcome this trifecta and even introduce unforeseen data advancements?

To answer this question, let’s break down the data migration process into five phases and see how AI acts in each.

Along the way, watch out for some real-world use cases.

1. Data cleansing

Messy data is one of the biggest obstacles in migration. Duplicates and outdated records can clog up the process and cause unreliability issues in the new system. AI helps tackle this by improving the quality of data before the migration begins.  

Throughout the data cleansing phase, AI-powered tools run data profiling that detects and flags errors and redundancies that could potentially create bottlenecks in data migration.

As a result, only clean, structured, and accurate data makes its way into the new system.

AI in data cleansing:

• AI can catch outliers and anomalies that traditional data checks might miss, such as mismatched customer profiles or missing transaction records.
  
• AI clusters and recognizes patterns in data, making it easy to standardize things like phone numbers, addresses, or product SKUs across massive datasets.
  
• AI models can analyze both structured (database tables) and unstructured (documents, emails) data and automate what used to be a slow, manual process.

2. Data quality

Poor data quality is often characterized by inconsistent formats, typographical errors, or incorrect values.

By analyzing current data patterns against historical benchmarks or established data patterns, AI can make intelligent adjustments and fix these issues.

AI in data quality

• Natural Language Processing (NLP) can interpret text fields and suggest corrections for misspellings, inconsistent terminology, or formatting issues.
  
• AI can identify and correct errors in numerical datasets, such as discrepancies in accounting records or incorrect product quantities.
  
• AI can analyze similarities in fields (name, address, phone number) and intelligently merge or remove duplicate records while preserving data integrity.

Use case: LLM-based data enrichment for insurance geographic analysis

A major player in the insurance sector needed an intuitive solution to analyze their geographic data. Their main goal was to drill down through different geographical levels and gain deeper insights into customer retention and geographic risk profiles.

To fulfill their request, we implemented an LLM system powered by the OpenAI API, that automatically enriched the existing data with detailed geographic attributes, such as population density, demographic statistics, and relevant economic indicators.

3. Data mapping

Data mapping, the process of matching fields from a source system to a target system, uses AI to detect complex relationships between data fields in both systems.

Within this framework, AI can identify correlations in data that human analysts may skim over at first glance, such as inconsistencies in naming conventions, variations in data formats, and unexpected connections between seemingly unrelated data points.

So, not only does AI make data mapping faster and more accurate, but it also makes it capable of easily handling the complexity of modern data environments.

AI in data mapping

• AI can generate mapping recommendations by analyzing the schema descriptions of both source and target systems, alongside natural language instructions and business process definitions.
  
• NLP-powered models can identify missing or mismatched data when consolidating local databases into a unified structure. This is particularly useful for large multinational enterprises undertaking SAP transformations or standardized global data models.
  
• AI can flag irrelevant or outdated records that often cause restrictions, such as corrupt entries, superseded records, or unused data.

Use case: AI-powered data mapping for accurate reporting

A group of dental practices with over 24 locations ran into a big data problem. Their associate dentists, who often worked at multiple locations, logged operational data into a system that had no defined list of practice names.

The lack of standardization resulted in inconsistent naming and misallocated data, which made it difficult to track dentists’ work locations and revenues accurately. This led to inaccurate reporting and financial losses.

To solve this, we applied database normalization, using Large Language Models (LLMs), during the transformation step of the ETL pipeline, which included:

1. Collect name variants: Aggregating all name variations from multiple sources, capturing misspellings, abbreviations, and other inconsistencies.


2. Build canonical mappings: Creating a reference table of official “canonical” practice names, each linked to a unique identifier to serve as the standard.


3. Apply LLM-powered mapping: Using a few-shot learning method, the LLM was trained on a small set of known name mappings and then used to match new, unseen variants to their correct canonical forms.

With this process, manual mapping goes out of the picture, making room for an accurate and smart transformation in our ETL system and, eventually, much more accurate reporting.

4. Data validation

In the post-migration phase, AI can help with reconciliation by comparing migrated data with the source data without requiring extensive manual checks. It does that by automating end-to-end validation of data flows and verifying that transformations, aggregations, and calculations deliver successful results in the target system.

To top it off, AI can also cross-reference historical data to detect anomalies and validate accuracy through automated regression testing.

AI in data validation

• AI can automatically verify that all data dependencies are satisfied, detecting errors beyond missing records or foreign key mismatches. It can even suggest corrective actions for issues like misclassified locations or employees being linked to the wrong business unit.
  
• AI doesn’t just check if data was moved correctly; it goes a step further to confirm that migrated data makes sense. NLP and GenAI models can validate that sales orders, transactions, and inventory levels align with expected business logic. If an unusual transaction spike appears post-migration, AI can determine whether it’s a duplication issue or a genuine business trend.

5. Compliance checks

In highly regulated industries such as healthcare, banking services, and insurance, ensuring compliance with data governance policies, industry regulations, and internal controls is a key aspect of data migration. AI can automate compliance assessments, verifying that sensitive information is correctly handled, anonymized, or masked before and after migration.

AI in compliance checks

• AI can categorize data based on sensitivity, regulatory status, or proprietary value.
  
• AI can create a detailed log of the entire migration process, serving as a reference for future migrations, regulatory audits, and internal reviews. For instance, a government agency migrating citizen records could use AI to document every action taken, including who accessed the data, what transformations were applied, and what modifications were made, providing a comprehensive compliance trail.

Wrapping up

To sum things up, yes AI is making data migration a lot more efficient and accurate. Yes, it’s automating numerous processes such as analyzing, cleaning, mapping, and validating data at scale. But the payoff is in the way it’s reshaping how organizations future-proof their data infrastructure.

It’s time to let loose the power of your data and turn migration from a challenge to a competitive edge.