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Business Intelligence and Analytics

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  • Busting Data Lake Myths and Dragons
    Busting Data Lake Myths and Dragons
    Gary Richardson, MD, Emerging Technology, 6point6 Recorded: May 9 2019 34 mins
    Leveraging data lakes is no longer a technical challenge, AWS, Azure and GCP make it easy to provision and harness the technical infrastructure needed in order to leverage your data. The issues that prevent data exploitation are all of the non technical drivers that we have been talking about for years. In this talk we will run through the following provocations:

    1. We don't need governance, we have a data lake.
    2. We have all our data in one place so we know what data we have.
    3. We have a data lake so we now have a data strategy.
    4. We have new shiny tools we don't need to think about data engineers.
    5. We do agile so we will get results.

    Register for our webinar and get the inside track on how to leverage cloud data lakes.


    Speaker Bio-
    Gary Richardson, MD, Emerging Technology

    Gary is the Managing Director for Emerging Technology at 6point6. With over 17 years’ of consulting experience, Gary leads a team of data scientists and data engineers in the agile development of Blockchain, AI and Machine Learning solutions. The focus of the team is bringing a collaborative approach to analytics, underpinned by machine learning and data engineering. He believes mainstream business adoption of AI solutions are the key to accelerating innovation enabling businesses to compete, reduce cost and ensure compliance.

    Prior to joining 6point6, Gary was the Head of Data Engineering at a Big 4 consulting firm, focussing on blockchain and bringing sound data engineering to the world of AI.
  • Business Intelligence & Analytics Community Update
    Business Intelligence & Analytics Community Update
    Erin Junio, BI & Analytics Content Strategist, BrightTALK Recorded: Apr 24 2019 16 mins
    Find out what's trending in BrightTALK's BI & Analytics community and what BI audiences care about in 2019!

    Join Erin Junio, BI & Analytics Content Strategist at BrightTALK for a session to learn more about:

    - Topic Trends & Key Insights
    - What BI & Analytics Professionals Care About
    - Events in the Community
    - What to expect in Q2 2019 and beyond
  • Designing Data Lakes: Architecture Options with Open Source Tools
    Designing Data Lakes: Architecture Options with Open Source Tools
    Maloy Manna, PM Engineering, AXA Data Innovation Lab, Paris Recorded: Apr 17 2019 62 mins
    The concept of Data lakes evolved to address challenges and opportunities in managing big data.

    Organizations are investing massive amounts of time and money to upgrade existing data infrastructures and build data lakes whether on-premises or in the cloud.

    This talk will discuss architectures and design options to implement data lakes with open source tools. Also covered are challenges of upgrade & migration from existing data warehouses, metadata management, supporting self-service and managing production deployments.
  • Busting Data Lake Myths and Dragons
    Busting Data Lake Myths and Dragons
    Gary Richardson, MD, Emerging Technology, 6point6 Recorded: Apr 17 2019 35 mins
    Leveraging data lakes is no longer a technical challenge, AWS, Azure and GCP make it easy to provision and harness the technical infrastructure needed in order to leverage your data. The issues that prevent data exploitation are all of the non technical drivers that we have been talking about for years. In this talk we will run through the following provocations:

    1. We don't need governance, we have a data lake.
    2. We have all our data in one place so we know what data we have.
    3. We have a data lake so we now have a data strategy.
    4. We have new shiny tools we don't need to think about data engineers.
    5. We do agile so we will get results.

    Register for our webinar and get the inside track on how to leverage cloud data lakes.


    Speaker Bio-
    Gary Richardson, MD, Emerging Technology

    Gary is the Managing Director for Emerging Technology at 6point6. With over 17 years’ of consulting experience, Gary leads a team of data scientists and data engineers in the agile development of Blockchain, AI and Machine Learning solutions. The focus of the team is bringing a collaborative approach to analytics, underpinned by machine learning and data engineering. He believes mainstream business adoption of AI solutions are the key to accelerating innovation enabling businesses to compete, reduce cost and ensure compliance.

    Prior to joining 6point6, Gary was the Head of Data Engineering at a Big 4 consulting firm, focussing on blockchain and bringing sound data engineering to the world of AI.
  • Social, Mobile, Cloud: Today’s Information Governance Challenge
    Social, Mobile, Cloud: Today’s Information Governance Challenge
    Robert Cruz, Senior Director, Information Governance Practice, Smarsh Recorded: Apr 17 2019 39 mins
    During this presentation, Robert Cruz, Senior Director/Information Governance Practice at Smarsh will discuss the governance challenges of today’s data – namely, the growth of social, mobile, and rich, dynamic content. Cruz will also look at the process of governing your data in the cloud and provide tips and best practices for qualifying cloud services providers for data availability, performance and extraction.
  • 5 Key Ingredients for Self-Service Success
    5 Key Ingredients for Self-Service Success
    Carl Allchin & Andy Kriebel, Asst. Head Coach & Head Coach (The Information Lab Data School); Eva Murray, Head of BI (Exasol) Recorded: Apr 16 2019 62 mins
    Carl Allchin (The Information Lab Data School) shares practical advices and real-life scenarios for developing a successful and sustainable self-service analytics environment.

    Carl has several years of experience as an analytics and BI consultant, coach and trainer. He focuses on operational improvement and customer experience measurement, two key components for any self-service strategy.

    Join us for this webinar and takeaway a number of tips, ideas and the pitfalls to avoid when setting up self-service BI in your organization.
  • Insights in the Cloud: Guiding Your Cloud Analytics and BI Strategy
    Insights in the Cloud: Guiding Your Cloud Analytics and BI Strategy
    Isabelle Nuage (Talend), Brajesh Goyal (Cavirin), Vincent Lam (Talend), Erin Junio (BrightTALK) Recorded: Apr 16 2019 61 mins
    Whether you're just starting your cloud analytics journey, or you're a seasoned veteran, the question remains: Is your cloud analytics strategy delivering the insights you need? This panel of experts will help you discover the right way to harness your cloud data to give your big data team the resources they need to make informed business decisions. From preventing your cloud data lakes from becoming data swamps, to scaling your cloud environment to accommodate all your data sources, learn how to activate your cloud BI strategy & achieve powerful analytics results

    About the Presenters

    Brajesh Goyal is an industry veteran in the hybrid cloud space, bringing to Cavirin more than 20 years of high tech engineering experience. He was the founder & CEO for ITAPP, a disruptive cloud management company that was purchased by ServiceNow in 2016. At Oracle, he defined the term “enterprise grid computing” (former name for cloud) and wrote books on the topic. He continued to lead initiatives for enterprise grid computing, virtualization, and cloud at NetApp. BG holds a Master's degree, in Computer Science and Engineering from University of Minnesota, and a Bachelor of Technology Degree in CS from the Indian Institute of Technology

    Isabelle Nuage is Director of Product Marketing at Talend. Her field of expertise include Data Integration, Big Data and Analytics. Isabelle brings 20+ years of experience in the software industry holding various leadership positions in product marketing at SAP & Business Objects. She holds a post graduate degree in computer science applied to GIS from the Pierre & Marie University in Paris, France

    Vincent Lam is Head of Cloud Product Marketing at Talend. Throughout his career he has held leadership roles in marketing, product management & product development involving innovative technology solutions to complex problems. Mr. Lam is author of several patents and his background includes innovation across technology firms, Wall St, & entrepreneurship
  • Panel Discussion: The Road to a Data-Driven Business
    Panel Discussion: The Road to a Data-Driven Business
    Jen Stirrup, Gordon Tredgold, Joanna Schloss, & Lyndsay Wise Recorded: Apr 16 2019 61 mins
    Join Jenn Stirrup (Director, DataRelish), Gordon Tredgold (CEO & Founder, Leadership Principles LLC), Joanna Schloss (Data Expert) and Lyndsay Wise (Solution Director, Information Builders) as they discuss what it takes to take a business from needing analytics to leveraging analytics successfully.
  • Machine Learning Challenges - Data Integration and Transformation
    Machine Learning Challenges - Data Integration and Transformation
    Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs Recorded: Apr 10 2019 50 mins
    AI Machine Learning model accuracy depends on the quality of data. In data science, when we say quality of data, it means data consistency, data completeness and data correctness which are all part of data integrity. In this session we will talk about how machine learning models can be adopted for data integration. Also, in case of some of the machine learning models, we assume data is normally distributed or data elements are appropriately scaled. However, it is not always true. Hence, data has to be transformed by normalizing data without losing its integrity. This is a big challenge in data science. Data integrity is maintained with the help of integrity constraints or the rules that are designed to keep data consistent and correct. In this session we will discuss some of the techniques and methods used for data integration, data transformation and normalization while ensuring data integrity. We will walk you through the steps involved with the help of examples.
  • What You Must Know to Build AI Systems That Understand Natural Language
    What You Must Know to Build AI Systems That Understand Natural Language
    David Talby, CTO at Pacific AI Recorded: Apr 9 2019 58 mins
    New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past six years. David also highlights some of the differences between language understanding and other machine learning and deep learning applications.

    Topics include:

    - What gave natural language understanding its reputation as an “AI complete” problem
    - The many languages we speak every day and the resulting need to train domain-specific NLP models for most systems
    - The evolution from “traditional” machine learning and information retrieval techniques to current state-of-the-art systems, covering both the “simple” parts of common NLP Q&A or bot solutions and where “advanced” AI fits in
    - Guidelines to architecting a system that trains and serves large, current, accurate domain-specific NLP models using open source software

    David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

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