Value Creation is at the Core of Our Existence
We help companies unlock and transform data into useful analysis, valuable insights, and actionable strategies. We do this by helping companies utilize the data that they have been collecting in order to improve customer experience, optimize operations, and make better products and services.
DataCrunch Lab, LLC was started by industry veterans Dr. Zeydy Ortiz and Rob Montalvo to bring the transformational power of data and technology to business and society. DataCrunch Lab is accelerating the adoption of artificial intelligence and machine learning in the enterprise using the power of open source & commercial software and cloud computing resources. They create value by uncovering needs, co-developing the best solution, supporting customers through implementation, and ensuring the solution is successful. They serve customers across many industries including, IT, legal, retail, financial, and industrial/manufacturing sectors.
With 20+ years of professional technology development experience at the intersection of software and hardware, some of the business issues they had addressed include:
Dr. Zeydy Ortiz is the co-founder & CEO of DataCrunch Lab, LLC. She has been helping teams and organizations transform data into value across many industries including IT, financial, retail, and the manufacturing sectors. She started her career as a Performance Engineer at IBM building predictive models to inform business strategy. She developed capacity planning tools to right-size server infrastructure to customers’ workloads. She worked on multiple projects focused on improving performance & efficiency. Dr. Ortiz earned her bachelor's degree in Computer Engineering from the University of Puerto Rico, master's from Texas A&M University, and Ph. D. in Computer Science from North Carolina State University.
Rob Montalvo is co-founder and President of DataCrunch Lab, LLC. He is a Software Engineer turned entrepreneur. Rob has been in the software development industry for over 20 years building enterprise-grade software for many large organizations including IBM, VMware, Research in Motion (Blackberry), Qualcomm and Cisco. He has deep expertise in the integration of software and hardware including low-level programming, networking and virtualization. Throughout his career, he has held various technical and leadership positions. He earned a Bachelor of Science in Computer Engineering from the University of Puerto Rico. In his spare time, Rob enjoys working on house remodeling projects, traveling, and spending time with family.
Our innovative, award-winning digital assistant was recognized as “Highest Potential Value to Manufacturers” for increasing visibility of real-time production and plant operations.
Algorithms are used in many high-stakes decisions that affect our daily lives. As we develop AI systems that automate decisions in healthcare, how do we ensure trust and fairness? In this talk, we will discuss how to address algorithmic bias in building trusted AI systems.
Diversity and Inclusion are a key part of creating an environment that drives innovation and improves the bottom line. It provides companies a wide range of differing viewpoints, experiences and perspectives that is only possible by having a unique and varied employee base. Be part of this very important conversation by joining us in a panel discussion.
Many organizations are deploying AI and deriving measurable benefits from its adoption. In manufacturing, the most significant impact is reported as cost savings due to optimizing yield, energy, and throughput. However, starting a new project may introduce risk and uncertainty. How might we mitigate the risk of adopting AI technology? In this interactive hands-on session we will guide you from idea to roadmap focused on business outcomes.
Azure ML Studio provides the tools to easily build and deploy machine learning models. In this session we will discuss the process of building a model with Azure ML using a case study. We will also share practical tips including pitfalls to avoid when using customer data, how deployment affects the machine learning model, and how to build trust in the predictions.
In the insurance and banking industries, the track record of contributions made by women continues to grow. This is helping pave the way for future female scientists and analytics leaders. Predictive analytics and machine learning are no exception. At this panel session, learn from women in these fields what they've learned along the way, their wins and losses, and how they are helping others do the same.
Many businesses determine customer lifetime value (CLTV) in order to plan how to attract and retain customers. Traditionally, they use descriptive analytics to determine the average CLTV. However, with the expectation of receiving personalized services, these methods are inadequate.