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Big Data Management

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  • Analyse, Visualize, Share Social Network Interactions w Apache Spark & Zeppelin
    Analyse, Visualize, Share Social Network Interactions w Apache Spark & Zeppelin Eric Charles, Founder at Datalayer Recorded: Mar 13 2017 49 mins
    Apache Spark for Big Data Analysis combined with Apache Zeppelin for Visualization is a powerful tandem that eases the day to day job of Data Scientists.

    In this webinar, you will learn how to:

    + Collect streaming data from the Twitter API and store it in a efficient way
    + Analyse and Display the user interactions with graph-based algorithms wi.
    + Share and collaborate on the same note with peers and business stakeholders to get their buy-in.
  • Systems of Insight - The Next Generation of Business Intelligence
    Systems of Insight - The Next Generation of Business Intelligence Boris Evelson - Forrester Principal Analyst Recorded: Feb 21 2017 59 mins
    Business intelligence has gone through multiple iterations in the past few decades. While BI's evolution has addressed some of the technology and process shortcomings of the earlier management information systems, BI teams still have a ways to go.

    From the laggards failing to transform enough of their structured and data into information and business insights, to the old guard of spreadsheet-based applications for key business decisions, companies have hit a rut. How do we solve this? With Systems of Insight.


    In this webinar, guest speaker, Boris Evelson, Vice President and Principal Analyst at Forrester, will show you how to get with the BI program by creating systems of insight. Doing so will allow you to:


    - Merge existing IT capabilities with demanding business requirements

    - Harness valuable customer and company data

    - Make decisions faster, and more pointed, to serve customers in the Age of the Customer
  • Long-term Data Retention: Challenges, Standards and Best Practices
    Long-term Data Retention: Challenges, Standards and Best Practices Simona Rabinovici-Cohen, IBM, Phillip Viana, IBM, Sam Fineberg Recorded: Feb 16 2017 61 mins
    The demand for digital data preservation has increased drastically in recent years. Maintaining a large amount of data for long periods of time (months, years, decades, or even forever) becomes even more important given government regulations such as HIPAA, Sarbanes-Oxley, OSHA, and many others that define specific preservation periods for critical records.

    While the move from paper to digital information over the past decades has greatly improved information access, it complicates information preservation. This is due to many factors including digital format changes, media obsolescence, media failure, and loss of contextual metadata. The Self-contained Information Retention Format (SIRF) was created by SNIA to facilitate long-term data storage and preservation. SIRF can be used with disk, tape, and cloud based storage containers, and is extensible to any new storage technologies. It provides an effective and efficient way to preserve and secure digital information for many decades, even with the ever-changing technology landscape.
Join this webcast to learn:
    •Key challenges of long-term data retention
    •How the SIRF format works and its key elements
    •How SIRF supports different storage containers - disks, tapes, CDMI and the cloud
    •Availability of Open SIRF

    SNIA experts that developed the SIRF standard will be on hand to answer your questions.
  • Logistics Analytics: Predicting Supply-Chain Disruptions
    Logistics Analytics: Predicting Supply-Chain Disruptions Dmitri Adler, Chief Data Scientist, Data Society Recorded: Feb 16 2017 47 mins
    If a volcano erupts in Iceland, why is Hong Kong your first supply chain casualty? And how do you figure out the most efficient route for bike share replacements?

    In this presentation, Chief Data Scientist Dmitri Adler will walk you through some of the most successful use cases of supply-chain management, the best practices for evaluating your supply chain, and how you can implement these strategies in your business.
  • Unlock real-time predictive insights from the Internet of Things
    Unlock real-time predictive insights from the Internet of Things Sam Chandrashekar, Program Manager, Microsoft Recorded: Feb 16 2017 60 mins
    Continuous streams of data are generated in every industry from sensors, IoT devices, business transactions, social media, network devices, clickstream logs etc. Within these streams of data lie insights that are waiting to be unlocked.

    This session with several live demonstrations will detail the build out of an end-to-end solution for the Internet of Things to transform data into insight, prediction, and action using cloud services. These cloud services enable you to quickly and easily build solutions to unlock insights, predict future trends, and take actions in near real-time.

    Samartha (Sam) Chandrashekar is a Program Manager at Microsoft. He works on cloud services to enable machine learning and advanced analytics on streaming data.
  • Machine Learning towards Precision Medicine
    Machine Learning towards Precision Medicine Paul Hellwig Director, Research & Development, at Elsevier Health Analytics Recorded: Feb 16 2017 47 mins
    Medicine is complex. Correlations between diseases, medications, symptoms, lab data and genomics are of a complexity that cannot be fully comprehended by humans anymore. Machine learning methods are required that help mining these correlations. But a pure technological or algorithm-driven approach will not suffice. We need to get physicians and other domain experts on board, we need to gain their trust in the predictive models we develop.

    Elsevier Health Analytics has developed a first version of the Medical Knowledge Graph, which identifies correlations (ideally: causations) between diseases, and between diseases and treatments. On a dataset comprising 6 million patient lives we have calculated 2000+ models predicting the development of diseases. Every model adjusts for ~3000 covariates. Models are based on linear algorithms. This allows a graphical visualization of correlations that medical personnel can work with.
  • Bridging the Data Silos
    Bridging the Data Silos Merav Yuravlivker, Chief Executive Officer, Data Society Recorded: Feb 15 2017 48 mins
    If a database is filled automatically, but it's not analyzed, can it make an impact? And how do you combine disparate data sources to give you a real-time look at your environment?

    Chief Executive Officer Merav Yuravlivker discusses how companies are missing out on some of their biggest profits (and how some companies are making billions) by aggregating disparate data sources. You'll learn about data sources available to you, how you can start automating this data collection, and the many insights that are at your fingertips.
  • Data Visualization IS NOT Self Service BI - The Case for IoT in BI
    Data Visualization IS NOT Self Service BI - The Case for IoT in BI Lee Hermon, Sisense Engagement Manager and Adi Azaria, Sisense Chief Evangelist Recorded: Feb 15 2017 27 mins
    Businesses today already know that visualization in business intelligence is an essential part of competitive success. Yet, too many organizations are falling behind because of the inability to keep up with demand for information. One mistake is thinking that self-serve data visualization is all they need when setting up a self-service BI environment.

    Debunking the common myth, we will explore why data visualization IS NOT self-service BI. The only way for Information workers to become more self-sufficient is by having a BI environment that is more usable but also more consumable. It is these two themes—usability and consumability - that play crucial roles in a fully functioning self-service BI environment. Using modern IoT technologies, the modern business can expand access and consumability of data by engaging the human senses of sight, sound, and touch.

    Join Lee Hermon, Sisense Engagement Manager, as he explores the limitations of current Self Service Visualization models and Adi Azaria, Sisense co-founder & Chief Evangelist as he introduces how IoT in Business Intelligence is changing the game.
  • Comparison of ETL v  Streaming Ingestion,Data Wrangling in Machine/Deep Learning
    Comparison of ETL v Streaming Ingestion,Data Wrangling in Machine/Deep Learning Kai Waehner, Technology Evangelist, TIBCO Recorded: Feb 15 2017 45 mins
    A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 50% of the whole project.

    This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming analytics ingestion, and data wrangling within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Spark, Talend or KNIME. The session also discusses how this is related to visual analytics, and best practices for how the data scientist and business user should work together to build good analytic models.

    Key takeaways for the audience:
    - Learn various option for preparing data sets to build analytic models
    - Understand the pros and cons and the targeted persona for each option
    - See different technologies and open source frameworks for data preparation
    - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
  • Strategies for Successful Data Preparation
    Strategies for Successful Data Preparation Raymond Rashid, Senior Consultant Business Intelligence, Unilytics Corporation Recorded: Feb 14 2017 33 mins
    Data scientists know, the visualization of data doesn't materialize out of thin air, unfortunately. One of the most vital preparation tactics and dangerous moments happens in the ETL process.

    Join Ray to learn the best strategies that lead to successful ETL and data visualization. He'll cover the following and what it means for visualization:

    1. Data at Different Levels of Detail
    2. Dirty Data
    3. Restartability
    4. Processing Considerations
    5. Incremental Loading

    Ray Rashid is a Senior Business Intelligence Consultant at Unilytics, specializing in ETL, data warehousing, data optimization, and data visualization. He has expertise in the financial, manufacturing and pharmaceutical industries.

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