Introducing our Snowflake Data Cloud Native Application: AI-Driven Data Quality built into SQL statements! Learn More
AI-Powered Data Quality and Data Matching

Data Quality Challenges - Does Your Data Look Like This?

Here are some typical examples of trouble-causing, inconsistent, similar, and likely redundant data.

Examples of inconsistent data quality and data consistency issues

How do we address these data quality issues?

We've leveraged decades of experience through generations of product development in the data quality space to design, develop, and deploy our SmartMatchAI technology behind-the-scenes that consists of:

Cutting-edge AI models on a global scale

Ongoing Machine Learning processes

Specialized language model algorithms

Extensive knowledge bases

SmartMatchAI discovers issues in your important data quickly and easily. It's at the core of all our product offerings.

What We Offer

Easy-to-integrate REST/JSON APIs

Identify, match, and resolve inconsistent and redundant data with our AI-driven powerful APIs that can plug into anything anywhere.

Applications for Datasets and Databases

Use our APIs with entire datasets, files, and database tables for quick discovery and resolution of data quality issues, including at large scale.

Snowflake Native Application

Quickly and easily leverage our data quality capabilities as SQL on the Snowflake Data Cloud and dramatically increase your Snowflake ROI.

Why Data Quality Matters: The Impact You Can't Ignore

Costly Errors and Rework

Poor data quality leads to errors that require time-consuming and expensive rework. These mistakes can inflate operational costs and reduce overall efficiency, ultimately impacting the bottom line.

Inefficient Decision-Making

High-quality decisions rely on high-quality data. Poor data quality impairs decision-making processes, leading to suboptimal business outcomes. Leaders need accurate, timely, and reliable data to make informed decisions that drive success.

Missed Business Opportunities

Inaccurate or incomplete data can result in missed opportunities, as faulty data may lead to incorrect market analysis, poor customer insights, and misguided business strategies, costing your business potential growth and revenue.

Damaged Reputation

Delivering products or services based on faulty data can harm your reputation. Clients and stakeholders expect accuracy and reliability, and repeated data issues can erode trust, leading to poor communication, customer churn, and negative brand perception.

Regulatory Non-Compliance

With stringent data protection regulations like GDPR, poor data quality can lead to non-compliance issues. This not only results in hefty fines but also lengthy legal battles and additional corporate risk.

$15 million

Average cost per organization due to poor data quality

$3 trillion

Annual cost to US economy alone from poor data quality

billions+

Data elements analyzed to build our specialized AI models

27

Number of pre-integrated file types and database platforms

AI-Powered Data Quality Solutions

Revolutionize your data management with cutting-edge AI technology

Get Started Today