Improving AI Performance: How Anomalo and AWS Are Tackling Unstructured Data Challenges

AI and Data Quality

As generative AI continues to evolve, the focus is shifting from building ever-larger models to ensuring the quality of the data that feeds them. This change reflects a broader realization across industries: no matter how advanced an AI model is, its output will only be as good as the data it learns from. In this context, a recent collaboration between Anomalo and Amazon Web Services (AWS) offers a timely solution for one of the most persistent challenges in enterprise AI—managing unstructured data.

Understanding Unstructured Data

Unstructured data refers to information that doesn’t fit neatly into tables or databases. Think of scanned documents, emails, PDFs, and even social media posts. These are often stored in company systems but are difficult to analyze at scale. According to research from MIT Sloan, over 80% of enterprise data falls into this category. Yet many companies still rely on manual processes to handle it—methods that are slow, error-prone, and expensive.

Anomalo’s Automated Solution

Anomalo’s new approach aims to change that by offering an automated system for cleaning and monitoring unstructured data using AWS’s cloud infrastructure. The system can automatically extract text from various file types like PDFs and Word documents, identify problems such as missing or duplicated content, and flag sensitive information like personal addresses or proprietary designs. It also helps ensure compliance with regulations such as GDPR in Europe or CCPA in California by identifying and managing personal data more effectively.

Continuous Monitoring Benefits

One key feature is continuous monitoring. Instead of checking data quality only once during setup, Anomalo watches every batch of incoming documents for signs of trouble—such as unexpected changes in format or content size. This allows companies to catch issues early before they affect AI applications. The platform also integrates with AWS services like Amazon Bedrock and AWS Glue, making it easier for companies already using these tools to adopt Anomalo’s solution without starting from scratch.

Advantages of Automation

There are clear advantages here. Automating these tasks reduces the time engineers spend reviewing documents manually and cuts down on the risk of human error. It also lowers costs by preventing low-quality data from being used in training AI models—a process that can be both expensive and inefficient if not managed carefully. On the other hand, implementing such a system still requires some upfront effort and coordination between IT teams and business units to define what “good” data looks like for their specific needs.

Trends in AI Implementation

This announcement fits well within broader trends we’ve seen over the past couple of years. As large language models become more accessible thanks to lower training costs and cloud-based platforms like AWS Bedrock, companies are turning their attention toward practical implementation challenges—especially around data quality and governance. Anomalo has been active in this space for some time, previously focusing on structured data monitoring. This latest move into unstructured content shows a natural expansion rather than a sudden shift in direction.

Industry Comparisons

In fact, this development echoes similar efforts by other tech firms aiming to make generative AI more reliable in real-world settings. For example, Google Cloud has emphasized secure data handling in its Vertex AI platform, while Microsoft has introduced tools within Azure OpenAI Service focused on responsible AI practices. What sets Anomalo apart is its focus on making unstructured enterprise content usable at scale—a challenge many organizations are just beginning to tackle seriously.

Conclusion: Moving Forward

In summary, the partnership between Anomalo and AWS highlights a growing recognition that high-quality input is essential for meaningful AI output. By offering a way to clean up messy enterprise documents quickly and securely, this solution could help more businesses move their generative AI projects from experimentation into everyday use. While no tool can solve every problem automatically, having systems like this in place makes it easier for teams to build trustworthy applications without getting bogged down by hidden issues in their data lakes.

The Future of Data Technology

For companies exploring how best to use their existing information assets in AI projects, this kind of technology may become an important part of the foundation—not just an optional add-on later down the line.