Top 10: Data Cleaning Tools for AI

Data quality platforms address the infrastructure gap causing AI project failures, with tools like visual no-code preparation for hybrid cloud environments

Enterprises are discovering that the success of AI deployments hinges less on model sophistication and more on the quality of data feeding those models. 

An estimated 60% of AI projects fail before reaching production due to data quality issues, according to industry research. 

As organisations move from pilot programmes to production-scale machine learning (ML) systems, the infrastructure required to profile, cleanse and govern data has become critical. 

The platforms below are some of the top tools addressing this challenge, handling everything from basic data validation to complex governance frameworks across hybrid cloud environments. 

10. Zoho DataPrep

Mani Vembu, CEO of Zoho | Credit: The Hindu Business Line

Company: Zoho

CEO: Mani Vembu

Specialisation: AI-assisted visual preparation and cleanup for consistent data

Zoho operates as a privately owned SaaS provider, which is increasingly rare in an industry dominated by venture-backed giants. 

DataPrep provides a visual interface for data cleaning that incorporates OpenAI integration, allowing users to execute fixes using natural language commands rather than code. 

SaaS reports customers have reduced time spent on data migration and import processes by 75% to 80%, which translates to tangible productivity gains. 

The platform works particularly well for organisations already using Zoho’s integrated suite, where consistent data quality directly affects internal reporting and customer tracking.

9. SAS Viya Data Management

James Goodnight, CEO of SAS | Credit: SAS

Company: SAS

CEO: Dr. James Goodnight

Specialisation: Trusted data management and governance for enterprise AI models

SAS positions data management as a prerequisite for trustworthy AI, particularly in sectors where getting it wrong carries serious consequences. 

The Viya platform operates on hybrid architecture, allowing organisations to work with data residing in systems like Snowflake without the complexity and risk of moving it. 

Dr. James Goodnight founded SAS decades ago and still leads the company, which has carved out a niche in industries where data quality carries regulatory and ethical weight. 

The platform tackles what the company describes as infrastructure complexity that often slows AI adoption in sectors requiring real-time decisioning.

8. Informatica Intelligent Data Management Cloud

Amit Walia, CEO of Informatica

Company: Informatica

CEO: Amit Walia

Specialisation: Cloud-native, AI-powered platform for trusted data and analytics

Informatica has built its business as a platform-agnostic data management specialist, which matters in a world where enterprises rarely commit to a single cloud provider. 

The Intelligent Data Management Cloud uses an AI engine called CLAIRE to automate the data lifecycle, handling the repetitive work that typically consumes data engineering resources. 

Amit Walia as CEO has pushed what he calls customer-centricity in product development, though what that means in practice is delivering data that organisations can actually trust. 

The independence from specific cloud vendors represents a genuine differentiator for enterprises managing sprawling, multi-cloud infrastructure.

7. Databricks Unity Catalog and Delta Live Tables

Ali Ghodsi, Co-Founder and CEO of Databricks

Company: Databricks

CEO: Ali Ghodsi

Specialisation: Unified governance and monitoring for quality data across Lakehouse

The Lakehouse Platform, built by Databricks, merges data lake and warehouse functionality into something that doesn’t quite fit the old categories. 

Unity Catalog handles governance through automated lineage tracking and access controls, while Delta Live Tables takes a different approach by enforcing quality rules directly within ETL processes. 

This means poor data never reaches production tables in the first place. 

The platform includes monitoring for anomalies and data freshness, with alerts when things drift. 

Lakehouse Federation extends this governance across external systems including Snowflake and Amazon Redshift without requiring data migration, which addresses a real pain point for enterprises operating across multiple platforms.

6. Salesforce Data Cloud

Salesforce CEO’s Marc Benioff

Company: Salesforce

CEO: Marc Benioff

Specialisation: Unifies data into Customer 360 profile for AI-driven CRM

Salesforce developed Data Cloud to solve a problem that plagues most organisations: customer data scattered across dozens of systems, none of which agree on basic facts. 

The platform unifies this into what the company calls a Customer 360 Truth Profile. 

Salesforce tested it internally first, reducing lead assignment time from 20 minutes to under one minute, a 98% improvement that would be remarkable if it weren’t so clearly necessary. 

CEO Marc Benioff has positioned the platform as essential for AI-driven CRM and the company points to research showing lead conversion probability increases by 75% when businesses respond within five minutes. 

That makes data quality not just a technical issue but a revenue driver.

5. Oracle Enterprise Data Quality

Clay Magouyrk and Mike Sicilia, CEO’s of Oracle

Company: Oracle

CEO: Clay Magouyrk and Mike Sicilia

Specialisation: Profile, audit, cleanse and match complex enterprise data

Oracle provides Enterprise Data Quality as a platform for the unglamorous but essential work of profiling, auditing and cleansing data across Master Data Management and compliance initiatives. 

The system includes global address verification and operates through a browser-based dashboard that lets organisations monitor quality trends over time. 

Oracle has the advantage of deep integration within the core enterprise systems – CRM, ERP – where bad data causes the most damage. 

The platform connects with Oracle Enterprise Metadata Manager and supports Machine Learning services within Oracle Analytics Cloud, targeting the high-volume enterprise scenarios where data reliability isn’t optional.

4. AWS Glue DataBrew

AWS’s CEO, Matt Garman

Company: Amazon Web Services (AWS)

CEO: Matt Garman

Specialisation: Visual, no-code data preparation with 250+ built-in transformations

DataBrew addresses a straightforward problem: data preparation takes too long and requires skills most organisations don’t have enough of. 

The platform offers over 250 built-in transformations through a visual interface and AWS claims it reduces data preparation time by up to 80% compared to writing custom code. 

Matt Garman leads AWS, which designed DataBrew specifically to let domain experts – people who understand what the data actually means – handle quality work without needing to programme. 

That’s a practical response to the talent shortages affecting AI deployment across enterprises.

3. IBM watsonx Data Quality Suite

Arvind Krishna, IBM’s CEO

Company: IBM

CEO: Arvind Krishna

Specialisation: Comprehensive DataOps framework for AI governance and hybrid cloud

IBM consolidated its data services under the watsonx umbrella after Arvind Krishna took over as CEO and made enterprise hybrid cloud and AI the company’s focus. 

The suite brings together DataStage for ETL, Manta for lineage tracking and Databand for observability into a complete DataOps pipeline. 

IBM earned recognition as a Leader in the 2024 Gartner Magic Quadrant for Augmented Data Quality Solutions, which carries weight in enterprise purchasing decisions. 

The platform uses AI to generate quality checks based on detected relationships and historical patterns – and IBM reports that Sixt achieved a 70% reduction in problem detection and resolution time using it. 

Arvind’s tenure has been marked by big bets, including the US$34bn acquisition of Red Hat and watsonx is the continuation of that strategy with end-to-end visibility for managing compliance in hybrid environments.

2. Google Vertex AI Data Preparation

Thomas Kurain, CEO of Google Cloud

Company: Google Cloud

CEO: Thomas Kurain

Specialisation: AI-driven data preparation for model training in BigQuery

Google Cloud integrates data preparation directly within BigQuery, connecting it to Vertex AI, which provides access to over 200 Gen AI models including Gemini. 

The company operates under CEO Thomas Kurain and has positioned data preparation as the bottleneck preventing faster analytics adoption. 

Data Fusion handles hybrid and multi-cloud integration, built on the open-source CDAP project, which Google emphasises as a hedge against vendor lock-in concerns. 

Wayfair achieved a four times faster update rate for product attributes using Vertex AI, according to Google’s case studies. 

The placement of cleaning tools within BigQuery aims to collapse the time between data preparation and model training, which in practice means getting models into production faster.

1. Microsoft Fabric with Purview Unified Catalog

Youtube Placeholder

Company: Microsoft

CEO: Satya Nadella

Specialisation: Unified analytics, AI governance and end-to-end data quality

Microsoft introduced Fabric as a unified analytics platform that integrates Azure Data Factory and Synapse Analytics, aiming to solve an era defined by AI. 

Data quality management operates through Microsoft Purview Unified Catalog, which provides no-code and low-code rules, including AI-generated rules and AI-powered profiling. 

Microsoft’s pitch is straightforward: 60% of AI projects fail due to insufficient data governance and Fabric addresses that by eliminating the data silos that affect services like Azure OpenAI. 

Partners including Celebal use the platform, while Midcontinent Independent System Operator employs Purview as a data dictionary for AI initiatives. 

The integration of Purview within Fabric applies sensitivity labels and enforces access controls across data assets, tackling what Microsoft describes as the industry fragmentation where customers previously had to assemble disconnected services themselves. 

It’s a strategy built around unification, betting that enterprises are tired of stitching together tools that were never designed to work together.