Dr. Michael Zeller Co-founder & CEOThroughout history, many organizations have risen to fame or plummeted to abyss, and decision making has always played a pivotal role in either cases. Decision-makers in the business need data fast and it must be accurate, accessible and in a user-friendly format, facilitating rapid understandings. When predictive models are generating critical data for the business decision making, there can often be a breakdown in the process that converts the data science team’s work into a form that business users can utilize. However, a predictive model is a complex chain of processes—from acquiring huge volumes of data to translating it into meaningful information through analytic tools like SAS, and then consigning it to appropriate teams who make decisions and act upon them. Data is generated too quickly and is changing too rapidly for manual processes to keep pace. With its powerful intermediary solutions for data managed through SAS, Zementis, enables organizations to harness the power of their data to rapidly deliver insights and support informed business decisions based on predictive analytics. “We help organizations reduce the cost and complexity of predictive analytics and accelerate the time-to-market for insight-driven business decisions, thereby improving the quality of business outcomes,” says Dr. Michael Zeller, CEO and Co-Founder, Zementis.
Zementis’s solutions are based on PMML (Predictive Model Markup Language), which eliminates the complex myriad of model-to-model translation and SAS-to-IT operationalization. Zementis offers two PMML-based solutions— ADAPA (Adaptive Decision and Predictive Analytics), a decision engine for real-time scoring and UPPI (Universal PMML Plug-in), a tool for big data scoring for industrial analytics and data warehouse platforms. ADAPA, the flagship of Zementis, supports a broad range of predictive modeling techniques, including association rules, decision tree, linear and logistic regression, scorecards, and distributed clustering models. A stand-alone product, ADAPA is a decision engine, configured to meet industry-specific standards for predictive analysis. It features an intuitive user interface, simplifying the user experience when running predictive analytics queries.
UPPI has the functionalities of ADAPA embedded in portable plug-in tools and integrates with the core analytics or data warehouse program, offering seamless capabilities for integrating predictive analytics into analytics workflows.
We help organizations reduce the cost and complexity of predictive analytics and accelerate the time-to-market for insight-driven business decisions
UPPI has three variants based on the data storage it is implemented on—In-database, Hadoop and Datameer. Addressing a spectrum of applicative uses, UPPI is optimized for fraud and risk scoring, sensor and device data processing, and marketing and sales.
Both solutions are complimentary to SAS and tend to bring the modeling environment within the confines of the IT operational domain. ADAPA and UPPI are compatible with SAS, as PMML is the de facto standard for representing predictive models. PMML allows for models to be developed in one application and deployed within another, as long as both systems are PMML-compliant.
Encompassing a broad spectrum of devices involved in data transaction for analytic purposes, including remote servers, desktop computers, laptops, mobile devices and other BYOD (Bring Your Own Devices), puts sensitive data at a vulnerable risk. Deriving intellectual insights from customer behavior and market dynamics, Zementis caters to the security concerns revolving around it. “Everything is online now, so security becomes even more important for the Internet of Things, surrounding customer analytics,” explains Zeller.
True to its name, Zementis—zement meaning concrete and mentis meaning— the German and Latin interpretation respectively—the predictive analysis expert looks forward to extending its proclivity in machine learning and artificial intelligence.