AI and data migration: A winning combination for business | HCLTech

AI and data migration: A winning combination for business

Explore AI-driven data migration trends and the role of Generative AI in optimizing data quality, cleansing, privacy and strategic business growth.
9 minutes read
Andy Packham


Andy Packham
Chief Architect & SVP , Microsoft Business Unit, HCLTech
9 minutes read
AI and data migration: A winning combination for business

In today's fast-paced world, data has become the lifeblood of businesses, driving decision-making, innovation and overall success. As technologies like AI continue to advance, businesses find themselves at a crucial juncture where the ability to harness the full potential of data is not just a competitive advantage but an absolute necessity. Here, we explore the emerging trends in AI-driven data migration and how Generative AI fits into this picture.

But before we dive in, it is essential to understand the current state of the AI market. Especially in the past year, there has been plenty of hype, followed by massive investments in startups, Large Language Models (LLM) and the like. The global AI market is worth $196.63 billion. That's an increase of around $60 billion since 2022. In the aftermath, everyone realizes it is much more complicated than it looks. The complexities and challenges have become apparent. And so, rushing into AI deployment without proper consideration can lead to costly failures and ethical issues.

This is where systems integrators like HCLTech step in, helping organizations move from the valley of disillusionment to the plateau of productivity in AI adoption. One of the first steps is to focus on the quality, source, and structure of the data available. Without quality data, AI cannot deliver the expected benefits. Data migration is an integral component of this journey.

Understanding AI-driven data migration

Data migration, once a behind-the-scenes process, has now taken center stage in the era of AI. Businesses are increasingly leveraging AI to unlock organizational value and drive transformation at scale. The global data migration market is projected to reach nearly $11 billion by 2025, indicating the growing significance of AI in this domain.

One of the key drivers of AI-driven data migration is the need to ground applications in legitimate data to unlock the full potential of generative AI. Businesses realize that data quality is paramount for AI to provide meaningful insights and drive actionable outcomes. They must think about the data they want the model to draw from. This necessitates careful curation and data protection, ensuring that it is accurate and secure.

AI also presents businesses with the ability to have a much greater understanding of the available data landscape. It gives them a better look at:

  • Where the central source is
  • Where they can find high-quality data
  • Which lower-quality data can they depend on
  • Where the bias is

Whether in a specialized industry or general knowledge data, the ability to summarize it quickly and get relevant information and value out of it is where AI, especially GenAI, comes in. The end game is to gain actionable insights.

Impact of GenAI on data migration

According to analysts at Gartner and Bloor, more than 80% of database migrations don't meet their goals, failing to meet their objectives and overrun budgets and timelines. However, AI capabilities could mitigate this by covering various aspects of data migration, extending the ability to cleanse, validate and understand the metadata. It also has the potential to play a massive role in maintaining data privacy in compliance, especially with regulations like GDPR. This requirement stems from understanding the data and its exact sources, tagging it clearly and maintaining those tags.

Some of how these uses can be implemented include:

  • Data generation and augmentation: GenAI can create synthetic data that closely mimics real-world data. This capability is invaluable for data migration, enabling businesses to augment existing datasets or generate new data to supplement incomplete or inadequate datasets. GenAI helps ensure that the migrated dataset is comprehensive and representative by synthesizing realistic data.
  • Data cleansing and transformation: GenAI algorithms can analyze and understand the structure and semantics of data, allowing for automated data cleansing and transformation. This capability is crucial during the data migration process, as it helps identify and rectify inconsistencies, errors and redundancies in the dataset. GenAI accelerates the migration process and improves data quality by automating data cleansing and transformation tasks.
  • Semantic understanding and mapping: GenAI models can understand the underlying semantics of data attributes and relationships, facilitating accurate mapping between source and target data schemas. This capability is essential for ensuring data integrity and preserving the meaning and context of data during migration. By automating the mapping process, GenAI reduces the risk of data loss or corruption and streamlines the migration workflow.
  • Data privacy and security: GenAI can generate privacy-preserving synthetic data that preserves the statistical properties of sensitive datasets while protecting individual privacy. This capability is particularly relevant for organizations migrating sensitive or regulated data, such as personally identifiable information (PII) or financial data. GenAI enables organizations to comply with data privacy regulations without compromising data utility by generating synthetic substitutes for sensitive data.
  • Continuous learning and adaptation: GenAI models can continuously learn and adapt to evolving data patterns and distributions, allowing for dynamic adjustments during migration. This capability is invaluable in scenarios where data sources or requirements change over time, ensuring that the migrated dataset remains accurate and up-to-date. By leveraging adaptive learning algorithms, GenAI enables organizations to future-proof their data migration strategies and accommodate changing business needs.

Strategic approaches to AI in data migration

Businesses must adopt a strategic approach to leverage AI in data migration fully. This involves fostering a data-driven culture, treating data as valuable assets, and aligning AI initiatives with strategic business goals. By focusing on the following pillars, organizations can maximize the benefits of AI in data migration:

  1. Top and bottom line growth
  2. CX transformations
  3. Data monetization
  4. Increased productivity
  5. Cost management
  6. Risk mitigation

The benefits of adequately leveraging AI in data migration are immense. From increased productivity to better decision-making, AI-driven data migration can have a transformative impact on businesses. By empowering employees with AI-powered tools and insights, organizations can drive innovation, streamline operations and stay ahead in today's competitive landscape.

As AI evolves, its role in data migration will only become more significant. By embracing , businesses can unlock the full potential of their data assets and drive growth and innovation. However, it's essential to approach AI adoption responsibly, ensuring that data privacy and security are prioritized at every step. With the right strategies and technologies in place, businesses can harness the power of AI to thrive in the data-driven economy of the future.

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