Hi [[ session.user.profile.firstName ]]

Using the Cloud for Speed-of-Thought Analytics on All Your Data

1.5 TB of data per day? No problem! Learn how Ask.com turned to Snowflake’s cloud-native data warehouse combined with Tableau’s data visualization solution to address their challenges.

Ask.com and its parent family of premium websites operate in an extremely competitive environment. To stand out in the crowd, the huge amounts of data generated by these websites needs to be analyzed to understand and monetize a wide variety of site traffic.

Their challenges:
Ask.com’s previous solution of Hadoop + a traditional data warehouse was limiting their analysts’ ability to bring together and analyze their data.
- Significant amounts of custom processing to bring together data
- Performance issues for data users due to concurrency and contention challenges
- Several hours to incorporate new data into analytics.

Join Ask.com, Snowflake Computing, and Tableau for an informative webinar where you’ll learn:
- How Ask.com simplified their data infrastructure by eliminating the need for Hadoop + a traditional data warehouse
- Why Ask.com’s analysts are able to explore and analyze data without the frustration of poor, inconsistent performance
- How Ask.com’s widely distributed team of analysts can now access a single comprehensive view of data for better insights
Recorded Apr 28 2016 64 mins
Your place is confirmed,
we'll send you email reminders
Presented by
Snowflake Computing, Ask.com, Tableau
Presentation preview: Using the Cloud for Speed-of-Thought Analytics on All Your Data

Network with like-minded attendees

  • [[ session.user.profile.displayName ]]
    Add a photo
    • [[ session.user.profile.displayName ]]
    • [[ session.user.profile.jobTitle ]]
    • [[ session.user.profile.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(session.user.profile) ]]
  • [[ card.displayName ]]
    • [[ card.displayName ]]
    • [[ card.jobTitle ]]
    • [[ card.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(card) ]]
  • Channel
  • Channel profile
  • GDPR: How to Manage Risks and Reputation within Any Data-Driven Company Apr 3 2017 2:00 pm UTC 45 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.

    Data-driven companies need to apply proactive measures that will help in effectively managing their risks and reputation when client trust is at stake.

    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
  • Analyse, Visualize, Share Social Network Interactions w Apache Spark & Zeppelin Recorded: Mar 13 2017 49 mins
    Eric Charles, Founder at Datalayer
    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 Recorded: Feb 21 2017 59 mins
    Boris Evelson - Forrester Principal Analyst
    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 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.
  • Bridging the Data Silos Recorded: Feb 15 2017 48 mins
    Merav Yuravlivker, Chief Executive Officer, Data Society
    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 Recorded: Feb 15 2017 27 mins
    Lee Hermon, Sisense Engagement Manager and Adi Azaria, Sisense Chief Evangelist
    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 Recorded: Feb 15 2017 45 mins
    Kai Waehner, Technology Evangelist, TIBCO
    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 Recorded: Feb 14 2017 33 mins
    Raymond Rashid, Senior Consultant Business Intelligence, Unilytics Corporation
    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.
  • Data Science Apps: Beyond Notebooks with Apache Toree, Spark and Jupyter Gateway Recorded: Feb 14 2017 48 mins
    Natalino Busa, Head of Applied Data Science, Teradata
    Jupyter notebooks are transforming the way we look at computing, coding and problem solving. But is this the only “data scientist experience” that this technology can provide?

    In this webinar, Natalino will sketch how you could use Jupyter to create interactive and compelling data science web applications and provide new ways of data exploration and analysis. In the background, these apps are still powered by well understood and documented Jupyter notebooks.

    They will present an architecture which is composed of four parts: a jupyter server-only gateway, a Scala/Spark Jupyter kernel, a Spark cluster and a angular/bootstrap web application.
  • Visualization: A tool for knowledge Recorded: Feb 14 2017 49 mins
    Luis Melgar, Visual Reporter at Univision News
    During the last decades, concepts such as Big Data and Data Visualization have become more popular and present in our daily lives. But what is visualization?

    Visualization is an intellectual discipline that allows to generate knowledge through visual forms. And as in every other field, there are good and bad practices that can help consumers or mislead them.

    In this webinar, we will address:

    -What it’s Data Visualization and why it’s important
    -How to choose the right graphic forms in order to represent complex information
    -Interactivity and new narratives
    -What tools can be used
  • How to Setup and Manage a Corporate Self Service Analytics Environment Recorded: Feb 14 2017 48 mins
    Ronald van Loon, Top Big Data and IoT influencer and Ian Macdonald, Principal Technologist (Pyramid Analytics)
    As companies face the challenges arising from a surge in the number of customer interactions and data, it can be difficult to successfully manage the vast quantities of information and still provide a positive customer experience. It is incumbent upon businesses to create a consumer-centric experience that is powered by (predictive) analytics.

    Adopting a data-driven approach through a corporate self-service analytics (SSA) environment is integral to strengthening your data and analytics strategy.


    During the webinar, speakers Ronald van Loon & Ian Macdonald will:

    •Expand upon on the benefits of a corporate SSA environment
    •Define how your business can successfully manage a corporate SSA environment
    •Present supportive case studies
    •Demonstrate practical examples of analytic governance in an SSA environment using BI Office from Pyramid Analytics.
    •Discuss practical tips on how to get started
    •Cover how to avoid common pitfalls associated with a SSA environment

    Stay tuned for a Q&A with speaker Ronald van Loon and domain expert Ian Macdonald, Principal Technologist, Pyramid Analytics.
  • Data Virtualization: An Introduction (Packed Lunch Webinars) Recorded: Feb 10 2017 56 mins
    Paul Moxon, VP Data Architectures & Chief Evangelist, Denodo
    According to Gartner, “By 2018, organizations with data virtualization capabilities will spend 40% less on building and managing data integration processes for connecting distributed data assets.” This solidifies Data Virtualization as a critical piece of technology for any flexible and agile modern data architecture.

    This session will:

    Introduce data virtualization and explain how it differs from traditional data integration approaches
    Discuss key patterns and use cases of Data Virtualization
    Set the scene for subsequent sessions in the Packed Lunch Webinar Series, which will take a deeper dive into various challenges solved by data virtualization.
    Agenda:

    Introduction & benefits of DV
    Summary & Next Steps
    Q&A
  • HPE ALM Standardization as a Precursor for Data Warehousing Recorded: Feb 9 2017 59 mins
    Tuomas Leppilampi , Assure
    Agenda:
    Data warehousing at a glance
    Wild West vs Enterprise HPE ALM Template
    Planning and configuring the template
    Customer use case: Standardization project walkthrough
    How to maintain a standardized environment
    Next steps with HPE ALM
  • The Big Data Service Desk: The Saga so Far Recorded: Feb 1 2017 59 mins
    Thore Senneset, HEMIT Michael Pott, HPE Kevin Leslie, HPE
    HPE Service Manager and Smart Analytics leverage Big Data for the service desk for a better user experience and increased efficiency. It simplifies ticket submission, enables the automatic classification of tickets, as well as fast and proactive problem management.

    Hemit (Helse Midt-Norge IT) has been running HPE Service Manager with Smart Analytics for almost two years and will present experiences from this time frame along with thoughts and ideas for the future.

    Join this session to gain insights into Big Data Service Desk capabilities and values, and to learn about how Big Data can help you with your service desk.
  • Marketing Analytics: Using Analytics to Become a Data-Driven Marketer Recorded: Jan 26 2017 56 mins
    Susan Graeme - EMEA Marketing Director at Tableau
    Marketers deal with data every day in every channel. Need to segment leads by job title for an email campaign? We’ve got data for that. Want to prove which programs generate higher quality leads than others? Go ask the data.

    In this webinar, we’ll show you exactly how a data company uses analytics in its marketing efforts. Susan Graeme, Marketing Director at Tableau, will show you examples of real marketing dashboards that we at Tableau use internally to drive world class marketing programs.
  • AI in Finance: AI in regulatory compliance, risk management, and auditing Recorded: Jan 18 2017 49 mins
    Natalino Busa, Head of Applied Data Science at Teradata
    AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.

    Learn:
    Machine learning and cognitive computing for:
    -Regulatory Compliance
    -Process and Financial Audit
    -Data Management

    Recommendations:
    -Data computing systems
    -Tools and skills
Make smarter moves with your big data management
Make smarter moves with your big data management

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: Using the Cloud for Speed-of-Thought Analytics on All Your Data
  • Live at: Apr 28 2016 5:00 pm
  • Presented by: Snowflake Computing, Ask.com, Tableau
  • From:
Your email has been sent.
or close