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    • From Data with Love: How the data economy is impacting the insurance sector
      From Data with Love: How the data economy is impacting the insurance sector JS Gourevitch, Luca Schnettler, Petra Wildermann, Anil Celik, Thomas Lethenborg Recorded: Nov 20 2017 3:00 pm UTC 60 mins
    • The data economy and digital technologies are deeply transforming almost all areas of our lives. One of the most heavily transformed revolve around insurance and healthcare with a number of really interesting development possibly redefining the way we take care of ourselves and the way we consumer and use insurance as well.

      From harnessing the power of data to better help mental health patients, carers and medical personnel with their treatments to assessing the risk of developing broad range of illnesses and engaging better with users to propose them personalised healthy life plans to using big data and analytics to track down and prepare for epidemics to using data to better cover cars and drivers with car insurances and finally using social media data for insurers to better engage with customers, this webinar will propose a fascinating exploration of the opportunities, risks, new models supporting the digital transformation in banking.

      Moderated by Jean-Stéphane Gourévitch
      Luca Schnettler, CEO and Founder, HealthyHealth (UK)
      Petra Wildermann, Business Development Director, Metabiota (Switzerland)
      Anil Celik, Co-founder and CEO Urbanstats (US)
      Thomas Lethenborg, CEO, Monsenso (Denmark)

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    • GDPR Best Practice: Using a Data Hub to Protect Personal Data
      GDPR Best Practice: Using a Data Hub to Protect Personal Data Remi Forest, MapR Technologies & Jean-Michel Franco, Talend Recorded: Jan 16 2018 1:55 pm UTC 52 mins
    • Is Your Data Ready for GDPR?

      As the deadline for GDPR approaches, it is time to get practical about protecting personal data.

      We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.

      A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.

      Watch, as experts from MAPR and Talend show you how to:

      Diagnose the maturity of your GDPR compliance;
      Set up milestones and priorities to reach compliance;
      Create a foundation to manage personal data through a data lake;
      Master compliance operations - from data inventory to data transfers to individual rights management.

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    • Converging Your Data Landscape
      Converging Your Data Landscape Jack Norris, MapR Technologies & John Myers, Enterprise Management Associates (EMA) Recorded: Jan 11 2018 12:00 pm UTC 59 mins
    • How Data-Driven Approaches are Changing Your Data Management Strategies

      Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.

      In this webinar, John L. Myers of Enterprise Management Associates (EMA) and Jack Norris of MapR will discuss how the new business advancements require data-rich applications that enable global, real-time data integration, microservices support, and in-place and continuous machine learning/AI and SQL capabilities.

      Watch this video to learn:

      Examples of disruptive business models
      Drivers of changes to the management landscape
      Best practices associated with meeting requirements for data-driven applications

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    • Data Fabric @ Scale: Breaking through legacy data architectures
      Data Fabric @ Scale: Breaking through legacy data architectures Jack Norris - Senior Vice President, Data and Applications, MapR Recorded: Oct 25 2017 1:00 pm UTC 49 mins
    • The use of an emerging data fabric, offers enterprises a number of benefits and advantages including the ability to break through the gravitational pull of legacy data architectures and capture the full potential of all your data.

      This webinar will detail how the deployment of a data fabric can enable enterprises to more quickly and easily scale across data volumes, data types and locations. The session will also provide an overview on how a data fabric reduces storage costs and increases application agility and reliability – with the underpinning to support the successful pursuit of:

      * IoT through a data fabric’s capability of handling data flows from the edge to the cloud, centralizing learning, and distributing intelligence back to the edge for real-time responsiveness.

      * Machine Learning/AI with the fabric able to handle the complex data flows and logistics to support the rapid deployment and coordination across machine learning models, algorithms and analytic tools

      * Microservices and containers with the underlying data fabric able to support intelligent streams and support the mobility and flexibility for elastic stateful applications and analytic processes relying on shared data.

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    • Enabling Real-Time Business with Change Data Capture
      Enabling Real-Time Business with Change Data Capture Audrey Egan, MapR Technologies & Rupal Shah, StreamSets Recorded: Nov 14 2017 2:25 pm UTC 55 mins
    • Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.

      Running a business in real-time means being able to react to important business events as they happen. Applications that support day-to-day operations, however, are often scattered across the organization making it difficult to enable real-time movement of data.

      In this session, MapR and StreamSets discussed how change data capture (CDC) can be used to enable real-time workloads to drive success with ML and AI. You’ll see demonstrations of technologies that enable CDC, and specifically learn how to:

      Utilize change data capture (CDC) for efficient real-time data movement & processing
      Connect your databases, data warehouses, and data lakes without code
      Use MapR-DB as both source and destination for change data capture

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    • Data Warehouse Modernization: Accelerating Time-to-Action
      Data Warehouse Modernization: Accelerating Time-to-Action Clarke Patterson, StreamSets & Ankur Desai, MapR Recorded: Jun 6 2017 3:20 pm UTC 60 mins
    • Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions.

      Listen to our MapR and StreamSets experts to learn how to:

      Connect your databases, data warehouse, and data lake without code: StreamSets can help you connect your existing databases and data warehouse with the MapR Converged Data Platform to create a modern data lake. You can offload/move data without having to write a single line of code as well as continually monitor and visualize your dataflows.
      Utilize change data capture (CDC) for efficient real-time data movement: No more analyzing stale data or reliance on batch database updates. Leverage CDC to continually update the MapR data lake and ensure your insights and action are based on complete and current data.
      Effectively analyze frequently changing data: MapR Converged Data Platform is built on a read-write file system unlike other big data platforms. Land frequently changing data into MapR-DB and analyze it using modern distributed computing technologies.

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    • Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
      Powering Real-Time Big Data Analytics with a Next-Gen GPU Database Matt Aslett, Research Director, Data Platforms & Analytics at 451 Research, Dipti Borkar, VP Product Marketing at Kinetica Recorded: Nov 1 2017 5:00 pm UTC 52 mins
    • Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.

      Join 451 Research and Kinetica to learn:
      •An overview of the business and technical trends driving widespread interest in real-time analytics
      •Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing
      •How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
      •Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.

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    • 2018 Trends in Datacenters
      2018 Trends in Datacenters Andy Lawrence & Daniel Bizo Recorded: Feb 13 2018 4:00 pm UTC 56 mins
    • Datacenters of all types are facing a wave of disruption. Owners and operators of mission-critical facilities, and the suppliers that serve them, are aligning their strategies to several converging trends. These include the application of cloud computing and analytics to facility management and resiliency; new approaches to automation, networking and industrialization, driven in part by next-generation edge computing; and, critically, a growing need to increase agility and efficiencies without compromising availability.

      Join Andy Lawrence, Research VP, and Daniel Bizo, Senior Analyst, for a live webinar on February 13 as she reviews the trends expected to shape the datacenter landscape in 2018, and the level of impact those trends will have. Please come armed with questions, as there will be a live Q&A session at the end of the webinar.

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