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Business Intelligence and Analytics

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  • 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
  • How to apply real-time Cloud analytics in just an hour How to apply real-time Cloud analytics in just an hour Iver van de Zand, SAP Analytics Leader Recorded: Feb 17 2017 60 mins
    Cloud analytics has great momentum and that is for a reason: it allows for real-time and live analytics without needing to prepare an environment. In this webinar you will learn how to apply SAP Cloud analytics using BusinessObjects Cloud and the Digital Boardroom. Be amazed by the easiness’ of use and the great visualization capabilities.

    Iver van de Zand – SAP Analytics Leader – will provide a deep dive session on the modeling and visualization capabilities of this stunning product
  • 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.
  • Hype vs. reality: the truth of self-service BI Hype vs. reality: the truth of self-service BI Glen Rabie, CEO, Yellowfin Recorded: Feb 15 2017 41 mins
    The desire to balance the needs of business users with the governance requirements of IT will kill the current concept of self-service BI.

    Discover how to drive success with analytics and better separate the self-service hype from reality with Yellowfin's CEO, Glen Rabie.

    Glen will discuss:
    - The impact of the self-service pipe dream
    - Why changing BI tools won't help
    - What self-service BI should actually look like
  • 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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