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Calling all Data Scientists: Simplify Data Science and Advanced Analytics using Logical Data Fabric

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Virtual Solution Spotlight: Calling all Data Scientists - Simplify Data Science and Advanced Analytics using Logical Data Fabric

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Data scientists have three requirements for efficiently performing data science and advanced analytics: (1) access to wide-ranging enterprise data, (2) flexible modeling, and (3) easy data preparation. It is easier if all the data is normalized and stored in a single repository, but, in reality, data is stored across multiple systems and applications on premises or in the cloud, in diverse formats—structured, unstructured, and semistructured, and in different latencies—at rest or in motion.

Logical data fabric powered by data virtualization promises to provide access to all enterprise data in real time, in a normalized format, and with the ability to create multiple logical data models, all in a fraction of the time it usually takes with physical architectures such as data lakes. In a recent ROI study, data virtualization reduced the modeling time for data scientists from three months to one week, an 83 percent reduction in time to value.

Don’t believe it? Sign up for this free Virtual Solution Spotlight and learn how a customer company enabled predictive analytics for its data scientists in a shorter amount of time.

Join Dr. Sathyan Munirathinam, expertise manager at ASML, David Loshin, president of Knowledge Integrity, and Ravi Shankar, senior vice president and chief marketing officer at Denodo, to learn about the role of logical data fabric and data virtualization to aid advanced analytics and data science.

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Agenda

  • Using Logical Data Fabric to Enable Integrated Advanced Analytics by David Loshin – 20 min
  • Use Case Example: How Our Data Scientists Use Data Virtualization to Enable Predictive Analytics by Dr. Sathyan Munirathinam – 30 min
  • Why Are Logical Architectures Better for Data Science and Advanced Analytics? by Ravi Shankar – 10 min
  • Q&A – 30 min
  • David Loshin

    President of Knowledge Integrity

    Using Logical Data Fabric to Enable Integrated Advanced Analytics

    Our world is increasingly connected, with massive volumes of data streaming in real time from a variety of both machine-generated and human-generated sources. Innovative businesses have recognized the potential of ingesting streaming information to develop predictive and prescriptive models that can be directly embedded into business processes to automatically inform time-sensitive decision making.

    Powerful tools for transforming streams of information into analytical models have lowered the barrier to entry for organizations looking to transform their businesses with predictive and prescriptive analytics. Yet without a strategy for balancing real-time streaming with accessing data at rest, businesses risk becoming overwhelmed by the volumes of data being generated, the speeds at which these data volumes are communicated, capturing and managing data, and the ability to provide self-service access to analysts and data scientists who can develop analytical models.

    This discussion focuses on analyst enablement for integrated analytics. We consider the data strategy and data architecture challenges to support data availability, accessibility, and rapid data provisioning, enabling the development and testing of analytical models. In turn, we examine how developing and integrating advanced analytics models can be simplified using a logical data fabric.

  • Dr. Sathyan Munirathinam

    Expertise Manager, ASML

    Use Case Example: How Our Data Scientists Use Data Virtualization to Enable Predictive Analytics

    ASML customers are the world’s largest semiconductor manufacturers. We continuously improve our lithography machines to enable our customers to produce chips smaller, faster, and greener. Our data science team uses the data generated from our machines at the customers’ sites to perform predictive maintenance to ensure that those machines are operating at the highest availability and productivity that can lead to increased fabrication output. Any downtime of those machines could severely impact customers’ revenue.

    In this session, I will discuss how our data scientists use data virtualization to integrate the machine data collected from the customer locations with the rest of the enterprise data and provide important information for our technicians to perform timely preventative maintenance ensuring high customer satisfaction. Specifically, I’ll discuss:

    • ASML background and business
    • Helping our data scientists with all machine and enterprise data on an integrated data model
    • Simplifying their data preparation effort
    • The use of data virtualization to provide faster results to technical support teams
    • Do’s and don’ts for effective data science and advanced analytics

    Speaker Bio: Sathyan Munirathinam is working as an expertise manager at ASML Corporation. He is responsible for developing an enterprise data management and cloud data strategy and road map that addresses the dynamic data needs for operational analytics and advanced analytics.

    Sathyan Munirathinam has a Ph.D. in data mining, with over 20 years in business intelligence and 15 years in manufacturing. He has authored many papers and been involved in numerous international artificial intelligence and data mining research activities and conferences. His research interests include artificial intelligence, equipment health monitoring, IoT and big data analysis, statistical machine learning and data mining, ubiquitous computing, and human-computer interaction.

    He holds a master’s degree from Illinois Institute of Technology, Chicago. He is a Certified Business Intelligence Professional. Prior to joining ASML, Sathyan worked at Micron Technology and IBM, where he played different roles in architecting data lakes for manufacturing and enabling business models to support real-time analytics.

  • Ravi Shankar

    Senior Vice President and Chief Marketing Officer, Denodo

    Why Are Logical Architectures Better for Data Science and Advanced Analytics?

    IT teams have been supporting data science projects with physical repositories by dumping all enterprise data into data lakes, but that data is duplicated, out of sync with the source, in raw format, and creating additional headaches for data preparation and modeling. Preparing this data for advanced analytics takes an enormous amount of time for data scientists. In addition, they are hamstrung by the inability to create multiple models because of the physical nature.

    Many companies are successfully using logical data fabric enabled by data virtualization to easily support their data scientists. It provides all enterprise data in real time, reduces the data preparation effort, and enables data scientists to create multiple models and easily share with their teams.

    In this presentation, the author will provide insights into why logical architectures are better suited for data science and advanced analytics, citing specific examples. Specifically, he will address the following points:

    • The limitations of physical architectures
    • The capabilities within data virtualization that simplify data science
    • Time-based examples of how data scientists accelerate the time to analytics
    • Reference architecture of a logical data fabric

    Speaker Bio: Ravi Shankar is the senior vice president and chief marketing officer at Denodo. He is responsible for Denodo’s global marketing efforts, including product marketing, demand generation, field marketing, communications, social marketing, customer advocacy, partner marketing, branding, and solutions marketing.

    Ravi brings to his role more than 25 years of proven marketing leadership and product management, business development, and software development expertise within both large and emerging enterprise software leaders such as Oracle, Informatica, and Siperian. His deep expertise in data-related technologies facilitates increased global awareness of the Denodo Platform and accelerates its growth.

    Ravi holds an MBA from the Haas School of Business at the University of California, Berkeley, and a master’s degree in computer science. He is a published author and a frequent speaker on data management and governance. Prior to joining Denodo, Ravi was the vice president of product marketing at Informatica and was instrumental in positioning the company as a leader in the master data management (MDM) market. He helped accelerate MDM revenue and customer acquisition and helped propel Informatica into a $1B company.

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Presented By

denodo