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Welcome to the Battle for the Consumer

In this modern world of marketing the continued diversification and proliferation of channels and information available to the consumer have led to a change in the attitude of business as they compete for customers. Retailers, telecommunications, financial services and technology companies are all changing the way in which they approach the customer and in turn the customer has changed their expectations of the Organisation. In this webinar we look at the drivers behind this and share some of the changes that are taking place as a result of it. Most importantly we will share our view of what will successful organisations need to change to win the battle for consumers
Recorded Nov 25 2015 41 mins
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Presented by
Mike Turner, Global Practice - Customer Intelligence
Presentation preview: Welcome to the Battle for the Consumer

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  • Unsupervised learning to uncover advanced cyber attacks Aug 22 2017 10:00 am UTC 45 mins
    Rafael San Miguel Carrasco, Senior Specialist, British Telecom EMEA
    This case study is framed in a multinational company with 300k+ employees, present in 100+ countries, that is adding one extra layer of security based on big data analytics capabilities, in order to provide net-new value to their ongoing SOC-related investments.

    Having billions of events being generated on a weekly basis, real-time monitoring must be complemented with deep analysis to hunt targeted and advanced attacks.

    By leveraging a cloud-based Spark cluster, ElasticSearch, R, Scala and PowerBI, a security analytics platform based on anomaly detection is being progressively implemented.

    Anomalies are spotted by applying well-known analytics techniques, from data transformation and mining to clustering, graph analysis, topic modeling, classification and dimensionality reduction.
  • Fog Computing in Mobile Network Jun 22 2017 8:00 am UTC 45 mins
    Adnyesh Dalpati, Director Solutions Architect at Alef Mobitech
    Fog computing has the potential to resolve the issues with network latency since the media rich content can be delivered through such nodes directly.

    Fog Computing inside the mobile network providers opens up a window of revenue opportunities for MNO's and creates a innovative space in content & application delivery platform.

    Join this webinar to learn how to tackle the different challenges with fog computing and its role in the IoT cycle.
  • Apache Zeppelin in the Enterprise: Build, Secure & Reuse Data Pipelines w Spark May 15 2017 2:00 pm UTC 45 mins
    Eric Charles, Founder at Datalayer
    Apache Zeppelin is a great entry point for Data Scientist to explore and model Data.

    In an enterprise environment, this exploration tool can be used to assemble pipeline of notes and deploy them in a production system.

    In this webinar, you will learn how to:

    + Create functional notes corresponding to each step of the analysis.
    + Call a note from another note.
    + Pipe multiple notes together.
    + Create a deployable unit and run this unit on a remote cluster
  • Power BI Data Analytics and Visualization May 8 2017 8:00 am UTC 45 mins
    Priyanka Mane, Technology Consultant at Saviant Consulting
    Join this webinar to learn:

    1. What is Power BI?
    a. More power to Business Inteligence

    2. Why Power BI?
    a. Analytics
    b. Visualization

    3. How?
    a. Which data sources we can connect and analyse with Power BI?
    b. How to connect those data sources to Power BI?
    c. What is the role of R as a data source?
    d. How to visualise and Analyse?
    i. Design skills
    ii. Tips and tricks
    iii. Analytics patterns
    1. Custom visuals
    2. R Custom visuals (Predictive Analytics)

    4. What about Backend Management?
    a. Excel / Azure SQL / Azure Storage or Other
    b. Custom Tables / Columns / Measures
    i. What / Why / How?

    5. Power BI Updates
    a.Monthly Updates
    b. Power BI Community for queries and request/issues updates
    i. How to use this?
  • Tensorflow: Architecture and use case Recorded: Apr 21 2017 49 mins
    Gema Parreño Piqueras. AI product developer
    The webinar drives into the introduction of the architecture of Tensorflow and the designing of use case.

    You will learn:
    -What is an artificial neuron?
    -What is Tensorflow? What are its advantages? What's it used for?
    -Designing graphs in Tensorflow
    -Tips & tricks for designing neural nets
    -Use case
  • The Ways Machine Learning and AI Can Fail Recorded: Apr 13 2017 48 mins
    Brian Lange, Partner and Data Scientist, Datascope
    Good applications of machine learning and AI can be difficult to pull off. Join Brian Lange, Partner and Data Scientist at data science firm Datascope, as he discusses a variety of ways machine learning and AI can fail (from technical to human factors) so that you can avoid repeating them yourself.
  • Different Strategies of Scaling H2O Machine Learning on Apache Spark Recorded: Apr 13 2017 46 mins
    Jakub Hava, Software Engineer at H2O.ai
    Sparkling Water integrates H2O, open source distributed machine learning platform, with the capabilities of Apache Spark. It allows users to leverage H2O’s machine learning algorithms with Apache Spark applications via Scala, Python, R or H2O’s Flow GUI which makes Sparkling Water a great enterprise solution.

    Sparkling Water 2.0 was built to coincide with the release of Apache Spark 2.0 and introduces several new features. One of the latest and largest features is the ability to configure Sparkling Water for different workloads, scale and optimize the platform according to your data and needs.

    In this talk we will introduce the basic architecture of Sparkling Water, go over different scaling strategies and explain the pros and cons of each solution. We will also compare the approaches with regards to the specific use cases and provide the rationale why or why not each solution may be a good fit for the desired use case.

    This talk will finish with a live demo demonstrating the mentioned approaches and should give you a real time experience of configuring and running Sparkling Water for your use case(s)!
  • Data Science in Modern Banking Recorded: Apr 13 2017 45 mins
    Charlie Leahy, Head of Software Architecture and Data Science (Hufsy)
    Banks have a vast wealth of mineable data available to them, but traditionally have provided their customers with little feedback beyond a balance and list of transactions.

    In this talk Charles Leahy, Tech Lead at Hufsy, looks at ways in which tools such as visualisation and machine learning can be employed to give users meaningful insights, helping them make the most of their money.
  • Applied Data Science Recorded: Apr 13 2017 49 mins
    Giovanni Lanzani, Chief Science Officer at GoDataDriven
    Now that the Data Science hype is levelling out, many companies are wondering what went wrong as they could not extract values from their data science efforts.

    In this webinar we will explore what does it take to apply data science and machine learning in the real world.

    Key takeaways include:
    - How can you go beyond the traditional data warehouse when doing machine learning
    - How should you adapt your processes to keep monetizing on your data
    - How to close the feedback loop between your customers and your machine learning models
    - What kind of profiles are essential to successfully become a data driven organization
  • How Machine Learning Helps Predict Equipment Failure Recorded: Apr 13 2017 23 mins
    Yaroslav Nedashkovskyi, System Architect at SoftElegance
    We are going to discuss a case study on a unified data lake for the oil industry -- it is a software architecture and a set of microservices that are used to get business values from the data that are generated during the oil production. Math models were developed to make failure prediction of rod pumps during the oil artificial lifting.

    We used modern capabilities of Big Data Architecture, based on Apache Spark set of technologies, machine learning, archived data, and streaming data from wells to build a unified math model to predict failure of that kind of industrial equipment.

    Join this webinar to learn:
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    -- Architecture to handle near real-time data-flow from oil wells
  • How to Build Chat-Bots Using Machine Learning and NLP Recorded: Apr 12 2017 64 mins
    Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs
    Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. How Machine Learning frameworks have been applied in the real world continues to evolve and affect our daily lives, especially with chat bots.

    In this session Dr. Hodeghatta Rao will explain the fundamental concepts of Natural Language Processing (NLP), what are the practical applications of NLP, how machine learning is adopted to process the natural language and finally end with an overview of QnA (chat) system.
  • Panel: AI - The Backbone of the Modern Startup Recorded: Apr 11 2017 54 mins
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    Vijay Nadadur, Co-Founder & CEO, Stride.ai, Inc.
    Joshua Montgomery, CEO, Mycroft AI
    Brett Kuprel, Ph.D. Student, Standford AI Lab
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    Ronald van Loon, Top Big Data and IoT influencer and Dr. Markus Noga, VP Machine Learning, Innovation Center Network, SAP SE
    Machine learning and artificial intelligence enable customers to learn from data without being explicitly programmed. This changes hundreds of applications in the enterprises as machines can now work with unstructured data like natural language, text, images and videos. Machine Learning enables enterprises to re-imagine business processes with digital intelligence. Learn how you can realize the intelligent enterprise for your business.

    In this webinar, speaker and Big Data influencer Ronald van Loon and Dr. Markus Noga, Vice President Machine Learning, Innovation Center Network, SAP SE will discuss the following:


    •What is machine learning and artificial intelligence?

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    •Benefits of machine learning

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  • GDPR: How to Manage Risks and Reputation within Any Data-Driven Company Recorded: Apr 3 2017 48 mins
    Ronald van Loon, Director Business Development, Adversitement
    With the new GDPR taking effect in 2018 in the European Union, clients and consumers will have more control over their data, allowing them to decide which companies can use and store their information, which will have a substantial impact on data driven businesses. This includes all data analytics, and all applications, including Big data, Business Intelligence, data warehouses, data lakes, analytics, marketing applications, and all other applications where data is used. Client consent will be at the forefront of a business’s concerns, and organizations must manage this process to be compliant.

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    In this webinar, speaker Ronald van Loon will discuss the following:

    •Maintain client trust with appropriate data management
    •Taking steps to reduce risks and protect your reputation
    •Adopting a Protection by Design approach to data
    •How to implement technical infrastructures to protect and govern client data
    •Utilizing a Data Protection Officer to define how data is collected and stored
    •How to handle the various data streams

    Stay Tuned for a Q&A at the conclusion of the webinar with speaker Ronald van Loon
  • IT Relevance in the Self-Service Analytics Era Recorded: Mar 28 2017 60 mins
    Kevin McFaul and Roberta Wakerell (IBM Cognos Analytics)
    There’s no denying the impact of self-service. IT professionals must cope with the explosive demand for analytics while ensuring a trusted data foundation for their organization. Business users want freedom to blend data, and create their own dashboards and stories with complete confidence. Join IBM in this session and see how IT can lead the creation of an analytics environment where everyone is empowered and equipped to use data more effectively.

    Join this webinar to learn how to:


    · Support the analytic requirements of all types of users from casual users to power users
    · Deliver visual data discovery and managed reporting in one unified environment
    · Operationalize insights and share them instantly across your team, department or entire organization
    · Ensure the delivery of insights that are based on trusted data
    · Provide a range of deployment options on cloud or on premises while maintaining data security
  • Long-term Data Retention: Challenges, Standards and Best Practices Recorded: Feb 16 2017 61 mins
    Simona Rabinovici-Cohen, IBM, Phillip Viana, IBM, Sam Fineberg
    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 Recorded: Feb 16 2017 47 mins
    Dmitri Adler, Chief Data Scientist, Data Society
    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 Recorded: Feb 16 2017 60 mins
    Sam Chandrashekar, Program Manager, Microsoft
    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 Recorded: Feb 16 2017 47 mins
    Paul Hellwig Director, Research & Development, at Elsevier Health Analytics
    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.
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  • Title: Welcome to the Battle for the Consumer
  • Live at: Nov 25 2015 2:00 pm
  • Presented by: Mike Turner, Global Practice - Customer Intelligence
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