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Business Intelligence and Analytics

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  • The End of Proprietary Software The End of Proprietary Software Merav Yuravlivker, Co-founder and CEO, Data Society Recorded: Dec 8 2016 49 mins
    Is it worth it for companies to spend millions of dollars a year on software that can't keep up with constantly evolving open source software? What are the advantages and disadvantages to keeping enterprise licenses and how secure is open source software really?

    Join Data Society CEO, Merav Yuravlivker, as she goes over the software trends in the data science space and where big companies are headed in 2017 and beyond.

    About the speaker: Merav Yuravlivker is the Co-founder and Chief Executive Officer of Data Society. She has over 10 years of experience in instructional design, training, and teaching. Merav has helped bring new insights to businesses and move their organizations forward through implementing data analytics strategies and training. Merav manages all product development and instructional design for Data Society and heads all consulting projects related to the education sector. She is passionate about increasing data science knowledge from the executive level to the analyst level.
  • A Practical Guide: Building your BI Business Case for 2017 A Practical Guide: Building your BI Business Case for 2017 Ani Manian, Head of Product Strategy, Sisense and Philip Lima, Chief Development Officer, Mashey Recorded: Dec 8 2016 45 mins
    So you’ve decided you want to jump on the data analytics bandwagon and propel your company into the 21st century with better analytics, reporting and data visualization. But to get a BI project rolling you usually need the entire organization, or at the very least the entire department, to get on board. Since embarking on a BI initiative requires an investment of time and resources, convincing the relevant people in the company to take the leap is imperative. You’ll need to construct a solid business case, defend your budget request and prove the value BI can bring to your organization.

    In this webinar you’ll discover:

    - Why organizations need to invest in BI to begin with
    - How are organization deriving value from BI
    - How to build an internal business case for investing in BI
    - What are the intricacies of how to build a budget
    - How to drive your company to a purchasing decision
    - How to start realizing value from BI now
  • Predictive APIs: What about Banking? Predictive APIs: What about Banking? Natalino Busa, Head of Applied Data Science at Teradata Recorded: Dec 8 2016 44 mins
    The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services.

    According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions.

    In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.

    The presentation will unfold in three parts:

    1) Define banking services: Maslow's law, modern vs traditional banking
    2) Examples predictive and personalized banking experiences
    3) Examples of data science and data engineering pipelines for banking and financial services
  • Big data and Machine Learning in Healthcare – Actual experience, actual results Big data and Machine Learning in Healthcare – Actual experience, actual results Lonny Northrup, Sr. Medical Informaticist – Office of Chief Data Officer, Intermountain Healthcare Recorded: Dec 7 2016 63 mins
    Hear first hand from one of the nation’s leading healthcare providers, Intermountain Healthcare, on what is actually being accomplished with big data and machine learning (cognitive computing, artificial intelligence, deep learning, etc.) by leading healthcare providers.

    Intermountain has evaluated between 300 and 400 big data and analytic solutions and actively collaborates with the other leading healthcare providers in the United States to implement the solutions that are delivering improved healthcare outcomes and cost reductions.
  • From the intelligence driven datacenter to an intelligence driven business From the intelligence driven datacenter to an intelligence driven business Matt Davies, Head of Marketing EMEA, Splunk, & Sebastian Darrington, EMEA Director, Big Data & Analytics Solutions, Dell EMC Recorded: Dec 7 2016 49 mins
    Leveraging Big Data and Analytics to create actionable insights.

    Splunk & Dell EMC will share insights into the challenges & opportunities customers are seeing in the market – with the ‘needs to’; reduce costs and improve efficiency within IT (operational analytics), improve Compliance (security analytics) & implement Shadow IT due to the business not receiving the right service from IT. CIO Priority is keeping the lights on and so on…

    Dell EMC & Splunk combined strengths are helping numerous organizations to ‘leverage Big Data and Analytics to create actionable insights’.
  • Analytics in the Cloud Analytics in the Cloud Natalino Busa, Head of Applied Data Science at Teradata Recorded: Dec 7 2016 45 mins
    Today, data is everywhere. As more data streams into cloud-based systems, the combination of data and computing resources gives us today the unprecedented opportunity to perform very sophisticated data analysis and to explore advanced machine learning methods such as deep learning.

    Clouds pack very large amount of computing and storage resources, which can be dynamically allocated to create powerful analytical environments. By accessing those analytics clusters of machines, data analysts and data scientists can quickly evaluate more hypotheses and scenarios in parallel and cost-effectively.

    The number of analytical tools which is supported on various clouds is increasing by the day. The list of analytical tools spans from traditional rdms databases as provided by vendors to analytics open sources projects such as Hadoop Hive, Spark, H2O. Next to provisioning tools and solutions on the cloud, managed services for Data Science, Big Data and Analytics are becoming a popular offering of many clouds.

    Analytics in the cloud provides whole new ways for data analysts, data scientists and business developer to interact with each other, share data and experiments and develop relevant insight towards improved business processes and results. In this talk, I will describe a number of data analytics solutions for the cloud and how they can be added to your current cloud and on-premise landscape.
  • The Big BI Dilemma - Bimodal Logical Data Warehouse to the Rescue! The Big BI Dilemma - Bimodal Logical Data Warehouse to the Rescue! Rick van der Lans, Independent Industry analyst, Lakshmi Randall, Head of Product Marketing for Denodo Recorded: Dec 6 2016 59 mins
    The classic unimodal data warehouse architecture has expired because it is restricted to primarily supporting structured data but not the newer data types such as social, streaming, and IoT data. New BI architecture, such as “logical data warehouse”, is required to augment the traditional and rigid unimodal data warehouse systems with a new bimodal data warehouse architecture to support requirements that are experimental, flexible, explorative, and self-service oriented.

    Learn from the Logical Data Warehousing expert, Rick van der Lans, about how you can implement an agile data strategy using a bimodal Logical Data Warehouse architecture.
    In this webinar, you will learn:

    · Why unimodal data warehouse architectures are not suitable for newer data types
    · Why an agile data strategy is necessary to support a bimodal architecture
    · The concept of Bimodal Logical Data Warehouse architecture and why it is the future
    · How Data Virtualization enables the Bimodal Logical Data Warehouse
    · Customer case study depicting successful implementation of this architecture
  • A World Full of Insights – Mapping & Geospatial Visualization with Your Data A World Full of Insights – Mapping & Geospatial Visualization with Your Data David Clement & Rick Blackwell, IBM Watson Recorded: Dec 6 2016 56 mins
    High performance and scalable data mapping offers unlimited opportunities for quickly categorizing and identifying key insights for retail, defense, insurance, utilities, natural resources, social sciences, medicine, public safety and more.

    Organizations, already awash in customer data, know geospatial capabilities can put a new “lens”on existing reports. Data from smartphones, GPS devices and social media has organizations anxious to factor in customer location, origin or destination, with time or day.

    Join IBM Product Marketing Manager David Clement and IBM Senior Product Manager Rick Blackwell and explore the new, world-class mapping and geospatial capabilities for IBM Cognos Analytics and Watson Analytics. Discover how you can add geographic dimension to visualizing critical business information in reports and dashboards in Cognos Analytics.

    Keywords:
    analytics, data, big, watson, ibm, visualization, mapping, geospatial
  • IT Powered Enterprise Analytics IT Powered Enterprise Analytics Andy Cooper, Enterprise IT Consultant, Tableau Recorded: Dec 6 2016 48 mins
    Traditional report factories are rapidly becoming obsolete. Enterprise organizations are shifting to self-service analytics and looking for a sustainable, yet long-term approach to governance that satisfies the needs of both the business and IT.

    The Business needs real-time access to data to drive critical decisions. IT needs to audit and manage data to ensure it’s accurate, secure, and governed to scale.

    With only eight percent of people in traditional organizations able to both ask and answer their own questions, it’s time to take a closer look at your analytics strategy.

    Join this webinar to take a closer look at enterprise analytics and learn how:
    · Visual data analysis brings speed, value, accuracy, collaboration and leads to culture of analytics

    · Modern enterprises are eliminating boundaries between IT and the business

    · Shifting to enterprise self-service analytic tools empowers both the business and IT
  • Video interview: How to Capitalise on Big Data & Data Science Skills Video interview: How to Capitalise on Big Data & Data Science Skills Jez Clark, Co-founder and Director at Eden Smith Recorded: Dec 2 2016 7 mins
    Listen to our interview at Big Data LDN with Jez Clark, Co-founder and Director at Eden Smith.

    Jez will share insights focused on the importance of Big Data & Data Science skill both for employers searching for talent and professionals seeking opportunities.

    For those looking for these skills:
    -How big is the talent pool? Which skills are in the pool? What are the common job titles and themes?
    -How do you attract the talent and what are the challenges?

    For those looking for opportunity:
    -How can you manage expectations -- opportunities at Facebook/Google vs smaller SMEs
    -The majority of those in data science are not commercially aware, how do we fix this?
    -Data scientists don't know how to behave for a client to secure opportunity -- what are some tips to help?
    -How do you position yourself and showcase transferable skills?