Atlanta and Tel Aviv — February 14, 2023 – Intelligent monitoring platform maker Mona has announced an automated exploratory data analysis tool to identify the
root-cause of anomalies in multivariate data sets.
“As organizations increasingly rely on data to inform their decision making, they need more efficient and accurate data analysis tools,” said Yotam Oren, CEO and Co-founder at Mona. “We’re excited to make Mona’s cutting-edge analytical capabilities more accessible to more data practitioners.”
Multivariate data analysis has long been a challenge in today’s data-driven world, but Mona’s new solution is designed to change that. Mona is the first tool to automatically identify segments in multivariate datasets that exhibit anomalous behaviors while avoiding noise and false positives. By correlating key findings with other relevant metrics in the dataset, Mona is able to provide a comprehensive understanding of underlying patterns to determine the true cause of the issue.
“In many industries, a significant part of analysts’ work is to find the specific segments in which metrics underperform, and then understand the reason behind it,” said Itai Bar-Sinai, CPO & Co-founder at Mona. “We created the first-ever algorithm to automate this process on any multivariate dataset.”
Mona is designed to significantly reduce manual work hours required to sift through large datasets, conduct exploratory analysis, and generate insights that will drive
decision-making. With just a simple configuration, Mona has the ability to take any tabular dataset and automatically surface specific outlier segments. Mona is completely free and easy to use, making it accessible for organizations of all sizes and across any vertical. Simply upload a CSV file and let Mona do the rest of the work. Get started using Mona’s automated exploratory data analysis tool today by signing up on the website.
