Today, Anomalo announced that it has closed a $10M Series B extension round led by Smith Point Capital to accelerate investment in growth initiatives, including R&D for unstructured data monitoring and to deliver the future of data quality for Generative AI applications.
Anomalo’s extended funding round comes at a time where the company is seeing record enterprise demand for data quality as Generative AI surges alongside growing lack of trust from executives in data. In the last year, Anomalo has more than doubled its customers in the Fortune 500. Enterprise and high-growth companies like Discover, Atlassian, and Notion understand the urgency behind solving for data quality and rely on Anomalo’s AI-powered platform and capabilities to ensure the data implemented into their AI workflows is high quality and compliant. We’ve heard firsthand from data leaders over the past several months that data quality has emerged as a critical investment area within IT budgets.
The complexity in managing the enterprise data estate is increasing rapidly, driven by the surge in structured and unstructured data. In fact, 80 to 90 percent of new enterprise data is unstructured data – comprising files such as documents, call transcripts and order forms. These files are often cluttered with duplicates, errors, sensitive information and even inappropriate language, creating significant challenges for enterprises. This issue is particularly acute in industries like healthcare, which are poised to capitalize on the benefits presented by AI, but rely heavily on unstructured data in the form of patient records, clinical notes, and medical literature. This underscores the immense potential of data quality platforms to address these challenges and unlock the immediate and transformative value that Large Language Models (LLMs) bring to the enterprise.
Anomalo is rewriting the enterprise playbook for data quality in the AI era
In order for enterprise organizations to leverage their structured and unstructured data, they need to be able to identify and resolve quality issues with such data before they incorporate it into Generative AI workflows. Without proper handling of unstructured data, AI models may fail to generate accurate and meaningful insights (or miss out on hidden insights altogether) – hindering decision-making capabilities, and significantly slowing the overall time and effort required to prepare unstructured data required for AI development projects.
Anomalo addresses a mission-critical challenge for enterprises by enabling them to fully leverage AI platforms through improved structured and unstructured data quality. Their complete data quality platform provides rapid detection, root cause analysis, and resolution of unstructured data quality issues, ensuring swift mitigation before these issues impact business operations. This Series B extension round will also allow further innovation across Anomalo’s new features for enterprise customers including customized criteria within issue detection and the ability to run models within their own cloud environment hosted by Google Vertex, AWS Bedrock and Azure AI.
We’re extremely proud to support an outstanding team led by Elliot Shmukler and Jeremy Stanley. They are exceptional product-oriented leaders with a deep commitment to solving enterprise challenges through data. By assembling a world-class team and building a powerful distribution engine, they are driving the continued advancement of the market-leading Anomalo platform.
Congratulations again to the team at Anomalo. Maximizing the quality of data in the enterprise has become mission-critical and an important area of investment for Fortune 500 executives, and we are pleased to lead their Series B extension as they emerge as the leading platform in this space.