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

Best Practices for Data Discovery

Data visualizations can support a variety of opinions, but often leave you with more questions than answers. Is the data accurate? Is the analytical method correct? Is there bias in the presentation of the data? Is the insight actionable for the business and not just analysts? Most importantly, can we base critical business decisions on this information in real-time?

Join us for this webcast to see the full potential of visual data discovery as part of your analytical platform. You’ll hear best practices for addressing different analytics needs with a fast, easy, and flexible business intelligence (BI) and analytics platform. We'll also cover the way data visualization fits into the broader objective of enabling self-service analytics in your organisation.
Recorded Feb 26 2015 45 mins
Your place is confirmed,
we'll send you email reminders
Presented by
Chris Banks, Director of BI and Performance Management, Information Builders
Presentation preview: Best Practices for Data Discovery

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
  • Enterprise Analytics Journey, the IBM point of view for IBM Z customers Dec 14 2017 1:00 pm UTC 45 mins
    Hélène Lyon, IBM, Distinguished Engineer, IBM Z Solutions Architect
    IT is a key player in the digital and cognitive transformation of business processes delivering solutions for improved business value with analytics. This session will step by step explain the journey to secure production while adopting new analytics technologies leveraging mainframe core business assets
  • Data Fabric: A New Paradigm For Self-Service Data & Data Scientists Dec 12 2017 5:00 pm UTC 45 mins
    Kelly Stirman, VP Strategy, Dremio
    Data Scientists are rare and highly valued individuals, and for good reason: making sense of data, and using the machine learning libraries requires an unusual blend of advanced skills. Why is it then that Data Scientists spend the majority of their time getting data ready for models, and a fraction actually doing the high value work?

    In this talk we introduce the concept of Data Fabric, a new way to provide a self-service model for data, where data scientists can easily discover, curate, share, and accelerate data analysis using Python, R, and visualization tools, no matter where the data is managed, no matter the structure, and no matter the size.

    We will talk through the role of Apache Arrow, the in-memory columnar data standard that is accelerating analytics for GPU-based processing, as well as the role of Pandas and Arrow in providing unprecedented speed in accessing datasets from Python.
  • Three Ways To Accelerate Your Data Lake Migration To Cloud Oct 25 2017 4:00 pm UTC 45 mins
    Kelly Stirman, VP Strategy, Dremio
    Public cloud deployments have become irresistible in terms of flexibility, low barriers to entry, security, and developer friendliness. But the sheer inertia of traditional data lakes make them difficult to transition to cloud. In this talk we'll look at examples of how leading companies have made the transition using open source technologies and hybrid strategies.

    Instead of following a "lift and shift" strategy for moving data lake workloads to the cloud, there are new considerations unique to cloud that should be considered alongside traditional approaches related to compute (eg, GPU, FPGA), storage (object store vs. file store), integrations, and security.

    Viewers will take away techniques they can immediately apply to their own projects.
  • Designing Data Lakes: Architecture options with open source tools Oct 25 2017 12:00 pm UTC 60 mins
    Maloy Manna, PM Engineering, AXA Data Innovation Lab, Paris
    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.
  • Virtual Data Lake: A Reality Oct 25 2017 10:00 am UTC 45 mins
    Hélène Lyon, IBM, Distinguished Engineer, IBM Z Solutions Architect
    As an Enterprise customer, you are potentially using IBM Z in a hybrid cloud implementation. Let's understand how to benefit from cloud access to mainframe data without moving it outside z; thereby improving security, reducing integration challenges and answering your GDPR auditor's needs.
  • Using Docker to realise a modular Big Data Platform & Leveraging SANSA Stack Oct 24 2017 12:00 pm UTC 45 mins
    Dr. Hajira Jabeen, Senior Researcher at the University of Bonn
    Join this webinar where senior researches will present:

    1) Big Data Integrator Platform
    - Use of Docker and Docker swarm to realize a modular Big Data Platform

    2) Semantic Analytics Stack
    - Use of Big data distributed processing engines to leverage Scalable
    processing for the Semantic Web (RDF data representation, Querying,
    Inference, and Machine Learning)

    3) Seven societal challenges in Big Data Europe
    -Combination of different Big Data tools to create Big Data Value Chain
    (Pipeline) for different Use cases representing the societal challenges
  • Advanced Analytics in the Cloud - the latest cloud analytics innovations Oct 24 2017 8:00 am UTC 60 mins
    Iver van de Zand
    Iver van de Zand will talk and demo on the latest SAP innovations for analytics in the cloud. Keywords are live connectivity and the closed loop of combined business intelligence, planning and predictive analytics all in one environment. Fully ready and prepared for big data.
  • Introduction to Blockchain: Bitcoin, Ethereum, Ledgers, and more Oct 9 2017 2:00 pm UTC 60 mins
    David Siegel, Blockchain, decentralization and business agility expert
    Still confused about this whole Blockchain thing? Interested in investing in digital currencies, but not sure where to start? Want to get a better idea of the threats and opportunities?

    David Siegel is a Blockchain, decentralization and business agility expert who has been a high-level management & strategy consultant to companies like Sony, Hewlett Packard, Amazon, NASA, Intel, and many start-ups. David has been praised for being able to explain Blockchain in the most simple and interesting way.

    What you will learn:
    -What is Bitcoin?
    -What is the blockchain?
    -What is Ethereum? What is Ether?
    -What is a distributed application?
    -What is a smart contract?
    -What is a triple ledger?
    -What about identity and security?
    -What business models are at risk?
    -What are the opportunities?
    -What should we do?
  • Deep Stupidity: Probing the limits of AI, Deep Learning and Analytics Sep 25 2017 1:00 pm UTC 60 mins
    Prof J. Mark Bishop, Director, TCIDA (The Centre for Intelligent Data Analytics)
    On December 2nd 2014, provoked by the impact of `Deep Learning’ and other recent scientific and technical advances in Artificial Intelligence, Professor Stephen Hawking released a terrifying warning about the coming technological singularity and the existential danger of AI: "The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re-design itself at an ever-increasing rate," Professor Hawking said. "Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded".

    Fashions in AI come and go - and the hype around so called ‘Deep Learning’ networks is not without precedence - so what is it about ‘nouveau AI’ that warrants such alarm?

    Conversely, in this talk, I will endeavour to situate recent technological landmarks more properly in their historical niche; concluding, by foregrounding the computationally-impregnatable barriers which remain between ‘Deep Minds’ and `Real Minds`.
  • The Cognitive Bank: Leveraging Advanced Analytics and Artificial Intelligence Sep 21 2017 12:00 pm UTC 60 mins
    Vivek Bajaj, Global VP of Solutions for IBM Financial Services
    Today the payments industry faces a rebirth by necessity. Financial institutions process massive volumes of customer and payments transaction data, much of it unstructured and untapped.

    Cognitive Systems have the ability to understand, reason and learn. In Financial Services applying cognitive capabilities to real world payments issues like safer and faster payments is yielding significant results. Furthermore Risk and Compliance and segment of one engagement are areas where ROI is tremendous when leveraging advanced analytics and artificial intelligence in cohesion.

    Learn from real world use cases of how financial institutions globally have gained significant competitive advantage by becoming a truly Cognitive Bank.
  • HDFS on Kubernetes: Lessons Learned Recorded: Sep 19 2017 46 mins
    Kimoon Kim, Pepperdata Software Engineer
    HDFS on Kubernetes: Lessons Learned is a webinar presentation intended for software engineers, developers, and technical leads who develop Spark applications and are interested in running Spark on Kubernetes. Pepperdata has been exploring Kubernetes as potential Big Data platform with several other companies as part of a joint open source project.

    In this webinar, Kimoon Kim will show you how to: 

    –Run Spark application natively on Kubernetes
    –Enable Spark on Kubernetes read and write data securely on HDFS protected by Kerberos
  • A Guide to Machine Learning Patterns and Data Visualizations Recorded: Aug 24 2017 55 mins
    Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs
    Data visualization must be intuitive in order for non-IT business leaders to see data patterns. Representing data in a graphical or pictorial format is easy, but constructing the data in the best and most logical way can be tricky.

    In this session, Umesh will talk about how to represent data simply to make quicker and better business decisions. He will walk through several data visualization techniques through business cases and examples. By the end of the session, you will not only know different data visualization techniques, but also have an understanding of circumstances under which each technique should be used and the best way to represent particular data sets for different business cases.
  • Data Visualization for Predictive Analytics Recorded: Aug 24 2017 58 mins
    Andy Kriebel, Eva Murray, Benedetta Tagliaferri
    What does the future hold?

    Predictive Analytics - everyone is talking about it and many organisations claim to be doing it. But are they? And what insights do they gain to then make tactical or strategic changes? How can analysts work with decision makers by sharing results in a visually effective and meaningful way while also informing them about possible courses of action?

    This webinar is presented by Andy Kriebel, Head Coach at the Data School and Eva Murray, Tableau Evangelist at Exasol. Our guest speaker on Predictive Analytics is Benedetta Tagliaferri, Consulting Analyst at The Information Lab.

    The webinar will look at some examples of predictive analysis and will show data visualization examples that are actionable and can drive further questions and discussions in an organisation.
  • Ask the Visualization Expert: Live Q&A on Advanced Data Visualization Recorded: Aug 24 2017 44 mins
    Carl Edwards, BI Consultant, Brett Churchill, BI Consultant
    Looking to take your graphs to the next level? Want to make sure you choose the right visualization? Plagued by the challenges of geospatial heat maps?

    Get your questions ready and join this session where data experts Carl and Brett will go over the common questions they get asked and answer all the data visualization issues you've been plagued with, including how to:

    -Use location-based data to put your visualization on the map
    -Uncover new relationships, patterns and opportunities
    -Identify emerging trends
    -Answering comparative business questions with set analysis
    -Understand best practices for creating an aesthetically-pleasing and useful visualization
  • How Many Analysts Does It Take To Change a Lightbulb? Recorded: Aug 24 2017 49 mins
    Steve Adams, Senior Consultant, Visual DJ, Ltd.
    When analysis needs to be used by decision makers that didn’t create it, the communication of the information and the message it conveys becomes critical. There is a plethora of ways to layout reports and dashboards, even within a single company.
    Enter the SUCCESS formula, that “lightbulb” moment.

    Introduced by the IBCS Association (International Business Communication Standards) the SUCCESS formula provides conceptual, perceptual and semantic rules that enable faster, better, and less-costly results in all stages of business communications and decision-making processes.

    This webinar will introduce the 7 Rules of SUCCESS that provides a toolkit to aid analysts in designing their visualisations for better reach and decisions in their target audience.

    The webinar will also introduce The Philips journey to implementing IBCS principles in their global "Accelerate!” Initiative.
  • Tensorflow machine learning library and sample of application Recorded: Aug 24 2017 46 mins
    Marwa Ayad Mohamed ( Founder of YourChildCode ,Team lead software Engineer, Women Techmakers Cairo Lead)
    Tensorflow is an open source software library for numerical computation and machine learning.

    Join this session where Marwa will discuss:

    -Introduction to Artificial intelligence, machine learning and deep learning
    -Sample of machine learning applications
    -Tensorflow Story, Model and windows installation steps with object recognition demo.
  • Artificial Intelligence: Methods, Applications and Impacts Recorded: Aug 23 2017 49 mins
    Arinze Akutekwe, PhD Data Scientist, BAS EMEIA – Intelligent Enterprise - Analytics at Fujitsu
    Artificial intelligence has greatly changed the way we live since the 20th century. It involves the science and engineering of making machines intelligent and autonomous using computer programs.

    The processing power of computers has been on the exponential increase with cost of processors and storage decreasing. This has made research and developments efforts in AI areas such as deep learning, once thought to be impossible possible.

    In this webinar, we will examine current methods, application domains of specific methods, their impacts on our daily lives and try to answer questions on ethics of applying these technologies.
  • Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback Recorded: Aug 23 2017 62 mins
    David Talby, CTO, Atigeo
    Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce.

    This talk will cover a code walk-through, the key lessons learned while building such real-world software systems over the past few years. We'll look for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing.

    David will iteratively build a machine-learned hybrid model – combining features from different data sources and algorithmic approaches, to catch diverse aspects of suspect behavior:

    - Natural language processing: finding keywords in relevant context within unstructured text
    - Statistical NLP: sentiment analysis via supervised machine learning
    - Time series analysis: understanding daily/weekly cycles and changes in habitual behavior
    - Graph analysis: finding actions outside the usual or expected network of people
    - Heuristic rules: finding suspect actions based on past schemes or external datasets
    - Topic modeling: highlighting use of keywords outside an expected context
    - Anomaly detection: Fully unsupervised ranking of unusual behavior

    Apache Spark is used to run these models at scale – in batch mode for model training and with Spark Streaming for production use. We’ll discuss the data model, computation, and feedback workflows, as well as some tools and libraries built on top of the open-source components to enable faster experimentation, optimization, and productization of the models.
  • Ask the Data Expert: Live Q&A on All Things Machine Learning & AI Recorded: Aug 23 2017 59 mins
    Wim Stoop, Cloudera
    Join this webinar where data expert Wim Stoop from Cloudera will answer all of your Machine Learning & AI questions live.

    Wim will also go over some frequently asked questions on Machine Learning and AI such as:

    - Why is machine learning and AI crucial to master for organisations?
    - What components are needed to take advantage of data science?
    - Which use cases drive the greatest value?
  • Putting AI into LeAdershIp Recorded: Aug 23 2017 45 mins
    Prof. Dr. Michael Feindt, Founder & Chief Scientific Officer, Blue Yonder
    Artificial Intelligence (AI) is not a technology for the future; it’s a huge business opportunity for today. But how can your organisation become a trailblazer for AI innovation, transforming the way you work to deliver immediate – and lasting – bottom line value?

    Former CERN scientist, Prof. Dr. Michael Feindt, is one of the brightest minds in Machine Learning. Join him for a 30-minute masterclass in how to apply AI to your business.

    You’ll learn how AI can:
    •Make sense of market and customer complexity, to deliver quick and effective decisions every single day
    •Increase workforce productivity to improve output and staff morale
    •Enhance decision-making and forecasting accuracy, for operational efficiency and improved productivity
    •Be implemented into your business quickly, easily, with minimal disruption

    Michael will also share real-life examples of how international businesses are using AI as a transformation tool, from his experience as founder of market-leading AI solution provider, Blue Yonder.
Big Data, Big Challenges, Big Gains
Everyone is talking about big data. But what is it? How do you use it? How will it affect your organization?

Subscribe to this channel to hear best practices and practical information on everything big data from infrastructure requirements to analysis and use cases.

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: Best Practices for Data Discovery
  • Live at: Feb 26 2015 2:00 pm
  • Presented by: Chris Banks, Director of BI and Performance Management, Information Builders
  • From:
Your email has been sent.
or close