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INTERNET | Internet Software & Services / Customer Relationship Management

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Founded Year



Series A | Alive

Total Raised




Last Raised

$10M | 4 yrs ago

About UneeQ

UneeQ provides an intelligent digital human platform that makes digital conversations more human. UneeQ enables companies to reimagine the customer experience through a conversational platform designed around leveraging the 'human touch' to create better customer experiences.

UneeQ Headquarter Location

22 Pollen Street Grey Lyn

Auckland, 1021,

New Zealand

+64 9 974 4865

Latest UneeQ News

O’Reilly Announces First-Ever Book on Data Quality by Monte Carlo’s Barr Moses and Lior Gavish to Help Data Teams Achieve Reliability at Scale

Oct 13, 2021

Available today, the pre-release chapters dive into how some of the best teams are architecting for data observability. Download Available today, O'Reilly Data Quality Fundamentals' early release chapters dive into how some of the best teams are architecting for data observability. (Graphic: Business Wire) Available today, O'Reilly Data Quality Fundamentals' early release chapters dive into how some of the best teams are architecting for data observability. (Graphic: Business Wire) October 13, 2021 09:00 AM Eastern Daylight Time SAN FRANCISCO--( BUSINESS WIRE )-- Monte Carlo , the data reliability company, announced the launch of Data Quality Fundamentals: A Practitioner's Guide to Building More Trustworthy Data Pipelines, published by O’Reilly Media and available for free on the Monte Carlo website . This is the first book released by O’Reilly to educate the market on how best-in-class data teams design and architect technical systems to achieve trustworthy and reliable data at scale. For decades, teams have struggled to measure, maintain, improve, and predict data quality, and over the past few years, the speed and scale at which we ingest, process, transform, and analyze data have made these challenges even harder. This lack of visibility into the health of our data leads to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail. O’Reilly’s Data Quality Fundamentals is the only guide of its kind to help data engineers and analysts understand the key factors that contribute to unreliable data pipelines and poor data quality, leverage new and novel processes and technologies to solve these problems, and design resilient, observable systems to prevent data downtime from happening in the first place. In this book, readers will learn: Why data quality is critical for deriving true value from your analytics How to achieve high data quality across the organization with data observability How to architect a scalable, end-to-end data observability platform The people, processes, and frameworks necessary for a robust data quality strategy How to ensure data quality at scale with DevOps principles Best practices for building data trust gleaned from real-world examples The book is co-authored by Barr Moses, CEO and co-founder of Monte Carlo, Lior Gavish, CTO and co-founder of Monte Carlo, and Molly Vorwerck, Head of Content at Monte Carlo and former lead editor of the Uber Engineering Blog. “As data pipelines grow increasingly complex, the requirements around data quality and trust are higher than ever, and yet, many data teams don’t have the necessary resources to execute on this vision,” said Moses. “Lior, Molly, and I were inspired to write this book after our experiences building reliable data systems and working with some of the best data leaders to help them accelerate the adoption of data at their organizations and eliminate data downtime through data observability. We’re honored to partner with O’Reilly on this landmark contribution to the data engineering canon.” Get Free Access Today The first two chapters of Data Quality Fundamentals (a $67 value) are available for free from Monte Carlo. Visit today to get early access to the book. About the Authors Barr Moses is CEO & co-founder of Monte Carlo, a data reliability company and creator of the industry leading Data Observability platform, backed by Accel, GGV, Redpoint, ICONIQ Growth, Salesforce Ventures, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and built the data/analytics team from scratch. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford University with a B.Sc. in Mathematical and Computational Science. Lior Gavish is CTO and co-founder of Monte Carlo. Prior to Monte Carlo, Lior co-founded cybersecurity startup Sookasa, which was acquired by Barracuda in 2016. At Barracuda, Lior was SVP of Engineering, launching award-winning ML products for fraud prevention. Lior holds an MBA from Stanford and an M.Sc. in Computer Science from Tel-Aviv University. Molly Vorwerck is Head of Content and Communications at Monte Carlo. Prior to joining Monte Carlo, Molly led the Tech Brand and Content team at Uber, where she served as lead editor for the Uber Engineering Blog and Uber Research Program. She graduated from Stanford University with a B.A. in American Studies and wrote her honors thesis on Elvis Presley. About Monte Carlo As businesses increasingly rely on data to drive better decision making and power digital products, it’s mission-critical that this data is trustworthy and reliable. Monte Carlo , the data reliability company and creator of the Data Observability category, solves the costly problem of broken data through their fully automated, SOC-2 certified Data Observability platform. Billed by Forbes as the New Relic for data teams and backed by Accel, Redpoint Ventures, GGV Capital, and ICONIQ Growth, Monte Carlo empowers companies to trust their data. Contacts

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Research containing UneeQ

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CB Insights Intelligence Analysts have mentioned UneeQ in 2 CB Insights research briefs, most recently on Jul 21, 2021.

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UneeQ is included in 2 Expert Collections, including Customer Service Tech.


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