Ocient, the leading hyperscale data solutions company serving organizations that derive value from analyzing trillions of data records in interactive time, today announced that its next-generation campaign forecasting solution was selected by MediaMath, a leading technology pioneer which helps more than 3,500 advertisers solve complex marketing problems.

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“Ocient accelerates the impact and transformational value of hyperscale data analysis in interactive time, and we couldn’t be more pleased to see our campaign forecasting solution powering an incredible new business opportunity for MediaMath.”

MediaMath handles more than six million bid opportunities per second with 10-12 petabytes of new records per day. The Ocient campaign forecasting solution enables advertising technology (AdTech) customers like MediaMath to analyze higher-resolution data sets and deliver faster, more accurate insights on demand. Prior to working with Ocient, it was technically infeasible for MediaMath to query raw bidder log data at high enough resolution and in interactive time. This led to complex pre-aggregation and estimating pipelines that added cost and introduced technical limitations. After working with Ocient, MediaMath now returns rapid, more granular insights to improve campaign effectiveness and return on investment (ROI) for its customers.

“Our customers rely on MediaMath to innovate continuously and bring more competitive insights across more screens and platforms faster than ever,” said Anudit Vikram, chief product officer at MediaMath. “Working with Ocient enabled us to get to market months faster. We left the customization of our solution to Ocient, meaning we didn’t need 10 or more of our own engineers dedicated to making it work. We can now grow our market share and pursue massive new business opportunities while reducing costs and eliminating the resource-intensive process of customizing our own solution internally.”

Ocient’s campaign forecasting solution streamlines the data path across rapid and continuous transformation, loading, storage and analysis at petabyte scale, enabling AdTech firms to sample more data faster while cutting costs by 50% or more.

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Unique features of Ocient’s next-generation campaign forecasting solution include:

• Low latency analytics optimized for highly parallelized processing leveraging NVMe solid state hard drives with compute adjacent to storage
• Continuous loading and transformation of raw, semi-structured bidder log data at terabits per second with streaming data available for query within seconds
• Support for geospatial data types and complex data formats, including arrays, tuples and matrices, to handle deep arrays of audience data and bid opportunity contextual data sets
• ANSI SQL queries with powerful windowing, aggregation and machine learning models that process thousands of unique campaign targeting criteria
• Zero Copy Reliability™ leveraging parity coding to ensure data availability and reliability with 60%-80% less storage footprint than copy-based data storage and analytics
• GDPR and CCPA compliant
• Deployed as a fully managed service in OcientCloud™ for ultimate flexibility and low operational overhead

A key value for organizations working with Ocient is the deployment of a complete hyperscale data solution, from ingest to insights, without the resource-intensive customization required to engineer a solution internally. Leveraging Ocient’s deep industry expertise, MediaMath deployed a comprehensive solution that would have otherwise taken multiple engineers 9-12 months, further adding to the cost and complexity of enhancing their campaign forecasting product offering.

“Working with MediaMath showed us the power of partnering closely with our customers to tackle their toughest hyperscale data challenges,” said Chris Gladwin, Ocient co-founder and CEO. “Ocient accelerates the impact and transformational value of hyperscale data analysis in interactive time, and we couldn’t be more pleased to see our campaign forecasting solution powering an incredible new business opportunity for MediaMath.”

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