Power your data transformation for AI with AI—and the combination of Snowflake and Matillion’s many capabilities.
Jan. 28, 2025 | By Shishir Shrivastava
AI thrives on clean, high-quality data, but to get that data, we first need to transform it. In other words, the journey to building AI begins with data transformation—it’s the foundation for all data-driven platforms.
But here’s the ironic twist: What if we use AI itself to transform the data it needs? A perfect loop of dependency, with AI as the creator and consumer of its own success.
In my previous article, I discussed the preparations organizations need to make to enjoy the full benefits of AI. Now let’s dive a little deeper into data availability and transformation and how both play key roles in your organization’s AI journey.
Why Data Quality Matters for Your AI Journey
Data transformation is a critical step in an enterprise’s AI journey because raw data is often messy, unstructured and inconsistent, making it unsuitable for AI models. Transforming data ensures it is clean and organized and in a format that AI systems can effectively process and analyze. Data transformation helps remove hidden patterns, uncover biases and improve the accuracy and reliability of AI predictions.
Without proper data transformation, even the most advanced AI algorithms will fail to deliver meaningful insights. In essence, data transformation lays the foundation for building robust, high-performing AI solutions.
Challenges of Quality Data Transformation
Enterprises often face several challenges to obtain quality data for AI use because doing so is a complex and resource-intensive process. Some key challenges include:
- Data silos: Data is often scattered across multiple departments, systems or platforms, making it difficult to consolidate and transform into a consistent, unified format.
- Poor data quality: Raw data may contain errors, inconsistencies, duplicates or missing values, requiring significant effort to clean and standardize.
- Unstructured data: A large portion of enterprise data (e.g., text, images, videos) is unstructured, and transforming it into a usable format for AI models can present technical challenges.
- Volume and complexity of data: Enterprises often deal with massive amounts of data, and processing such large data sets for transformation can be time-consuming and computationally costly.
- Integration challenges: Combing data from different sources with varying formats, structures and standards can lead to compatibility issues during transformation.
The Power of Matillion and Snowflake
Snowflake is a powerhouse capable of holding a multitude of data—whether structured, semi-structured or unstructured—and can offer Python, Java and Scala language integration within Snowflake. Nevertheless, these capabilities can be augmented by integration tools like Matillion, which helps address several data transformation challenges on Snowflake Data Cloud, offering solutions to common problems enterprises face.
Matillion’s Data Productivity Cloud provides a centralized platform to connect to various data sources, regardless of their location, whether on-premises databases, SaaS applications or cloud storage. Its prebuilt connectors, as well as the ability to create custom connectors, can simplify data ingestion from disparate systems into Snowflake, breaking down data silos.
Matillion also offers a range of data quality and transformation components within its visual, low-code/no-code interface. These components enable data cleaning, standardization, deduplication and validation, addressing data quality issues before AI models use the data. Functions such as data type conversion, string manipulation and regular expression matching help cleanse and prepare data for AI use.
Take Advantage of the Matillion/Snowflake Combination
Matillion offers built-in components that leverage the power of large language models to provide intelligent responses to user prompts. To boost accuracy, users can optimize the outputs using retrieval-augmented generation (RAG) workflows.
In addition, Matillion and Snowflake offer other benefits to help your enterprise on its AI journey:
- Matillion can handle semi-structured data like JSON and nested data. Components like “Extract Nested Data” flatten and rationalize semi-structured data, making it usable for AI algorithms.
- Matillion leverages Snowflake’s inherent scalability and processing power. Its pushdown ELT architectures push data transformation tasks into Snowflake, allowing Matillion to utilize Snowflake’s compute resources for faster processing of larger data sets.
- The low-code/no-code environment empowers data analysts and business users to perform data transformations without extensive coding skills. The visual interface simplifies pipeline building and data manipulation, reducing the need for specialized data engineering expertise.
- Matillion’s prebuilt connectors simplify integration with various data sources. Users can even create their own connectors to handle different data formats and structures, streamlining the process of combining data from diverse sources within Snowflake.
- Snowflake’s security features allow enterprises to maintain data governance and compliance during the transformation process. Snowflake’s data masking and encryption capabilities can be leveraged within Matillion pipelines to protect sensitive data.
- Snowflake aligns costs with usage. By leveraging Snowflake’s processing power, Matillion optimizes resource utilization and potentially reduces overall infrastructure costs.
Explore the Power of Snowflake and Matillion in Your AI Journey
Snowflake empowers AI data transformation with features like Snowpark for in-database coding, stored procedures for reusable logic and tasks for automation. Paired with Matillion, Snowflake offers even more robust capabilities to enterprises on their AI journeys, helping your organization prepare efficiently and effectively for analytics and AI.

Shishir Shrivastava
Practice Director, TEKsystems Global Services
Shishir spearheads data initiatives, leveraging an in-depth understanding of diverse industries and a strong proficiency in Snowflake’s Data Cloud. He has more than two decades of expertise in data architecture, governance, business intelligence, data warehousing, cloud computing, machine learning and AI. He specializes in leading high-performing teams, driving innovation and delivering scalable solutions that transform data into actionable insights.
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Shishir Shrivastava
Practice Director, TEKsystems Global Services
Shishir spearheads data initiatives, leveraging an in-depth understanding of diverse industries and a strong proficiency in Snowflake’s Data Cloud. He has more than two decades of expertise in data architecture, governance, business intelligence, data warehousing, cloud computing, machine learning and AI. He specializes in leading high-performing teams, driving innovation and delivering scalable solutions that transform data into actionable insights.