What makes one visualisation more impactful than another? Why do some chart types struggle to tell a story? In this session we explore how the Zen of Analysis is impacted by the types of visualisation we use, and the means in which we can use human perception to better tell our data story. Here, we explore the design principles that any analyst should be aware of when designing their worksheets and dashboards, as well as tie in the concepts of visual best practice to issues of speed and performance.
RecordedFeb 25 201543 mins
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Kirk Borne, Principal Data Scientist, Booz Allen Hamilton & Andreas Blumauer, CEO, Managing Partner Semantic Web Company
Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."
This webinar covers:
- An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
- How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
- How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
- Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.
In this webinar, Metadata.io CEO Gil Allouche will talk about the different ways AI is being used by marketers. From analyzing data to orchestrating new marketing campaigns, AI is powering marketing activities in new and exciting ways and affecting interactions throughout the entire customer lifecycle. As an example of how AI can have a tremendous impact on marketing practices, Gil will focus on its role in lead generation. Webinar attendees will learn:
- What Machine Learning is in relation to AI and how it connects your data to find patterns
- Examples of how machine learning can identify target audiences, including the 20 percent that creates 80 percent of your revenue
- How AI technology can help marketers prioritize their budgets to focus on the most effective programs
- Starting with small, iterative uses of AI in marketing can be the most effective way to understand what will yield the most ROI
Gil Allouche founded Metadata.io to make demand generation easy for non-technical marketers. The Metadata.io platform and AI Operator evolved from Gil's experiences hacking various marketing and CRM systems to get the solutions he needed.
AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Geordie Kaytes will share a framework for building customer-centered AI products. You’ll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.
Learn a framework for creating and communicating a vision that describes the overall direction of your AI product, a defined product strategy, a cross-functional roadmap aligned with the strategy, and a list of metrics that track progress towards the strategy
About the Speaker: Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic (https://www.heroicteam.com), a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.
With the General Data Protection Regulation (GDPR) becoming enforceable in the EU on May 25, 2018, many data scientists are worried about the impact that this regulation and similar initiatives in other countries that give consumers a "right to explanation" of decisions made by algorithms will have on the field of predictive and prescriptive analytics.
In this session, Beau will discuss the role of interpretable algorithms in data science as well as explore tools and methods for explaining high-performing algorithms.
Beau Walker has a Juris Doctorate (law degree) and BS and MS Degrees in Biology and Ecology and Evolution. Beau has worked in many domains including academia, pharma, healthcare, life sciences, insurance, legal, financial services, marketing, and IoT.
As we move to the conversational UI and take advantage of NLP and AI in general, we change the way we interact with technology dramatically. The standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.
Dennis will talk through where x.ai has encountered error perception issues as we seek to develop frictionless software, how we thought about the problem and the communication strategies we’re exploring to resolve it.
Ruturaj Pathak, Senior Product Manager, Networking BU, Inventec
We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.
In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.
Tariq Ali Asghar, CEO, Emerging Star investment Group
This Webinar explains how Big Data, Artificial Intelligence, and Machine Learning is going to transform the future Banking Industry. Banks which can manage this Big Data evolution successfully will survive and thrive, and give a more holistic and personalized customer service, thereby increasing their revenues tremendously.
The key takeaway from this Webinar is that “Right information at the right place and the right time is going to be the real money and will shape the future of Banking Industry.”
Tariq is a Fintech Expert, writer, and thinker based in Toronto Canada and is currently working on an initiative to disrupt the conventional Banking Industry with “Big Data Predictive Analytics Model” of his startup.
In this webinar, Mustafa Kabul, Principal Data Scientist, SAS, will provide an introduction to deep learning and its applications.
Mustafa is a data scientist in the Artificial Intelligence and Machine Learning R&D at SAS, where he leads innovative projects for SAS’s next-generation AI-enabled analytics products, including applications of deep learning. His current focus is on applying deep reinforcement learning to operational problems in the CRM and IoT spaces. An operations research expert working at the interface of machine learning and optimization, previously, he developed distributed, large-scale integer optimization algorithms for marketing optimization problems. Ever the optimization enthusiast, Mustafa always looks into ways to improve the algorithms. Nowadays his favorites are the distributed stochastic gradient and online learning methods. Mustafa holds a PhD from the University of North Carolina at Chapel Hill, where his research focused on game theory models of supply chains selling to strategic customers.
Andy Kriebel, Head Coach and Tableau Zen Master at The Data School & Eva Murray, Head of BI and Tableau Zen Master at Exasol
This webinar is part of BrightTALK's Founders Spotlight series, featuring fearless entrepreneurs and inspiring founders.
In this episode, Eva Murray & Andy Kriebel, Founders of Makeover Monday, will share their story of how they started the social data project, Makeover Monday, the challenges and successes they encountered along the way and how they overcame them.
This will be an interactive Q&A session and an excellent opportunity for entrepreneurs or professionals to have their questions answered.
This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.
The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.
You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.
Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.
Jean-Frederic Clere, Manager, Software Engineering, Red Hat
You can do a lot with a Raspberry and ASF projects. From a tiny object
connected to the internet to a small server application. The presentation
will explain and demo the following:
- Raspberry as small server and captive portal using httpd/tomcat.
- Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
- Raspberry as a Modbus supervisor controlling an Industruino
(Industrial Arduino) and connected to ActiveMQ.
Denis Magda, Director of Product Management, GridGain Systems
The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional analytical applications (HTAP).
Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. This session will cover:
- The requirements for real-time, high volume HTAP applications
- Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
- A detailed comparison of Apache Ignite and GridGain® for HTAP applications
About the speaker: Denis Magda is the Director of Product Management at GridGain Systems, and Vice President of the Apache Ignite PMC. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies and individuals deploy it for mission-critical applications. You can be sure to come across Denis at conferences, workshop and other events sharing his knowledge about use case, best practices, and implementation tips and tricks on how to build efficient applications with in-memory data grids, distributed databases and in-memory computing platforms including Apache Ignite and GridGain.
Before joining GridGain and becoming a part of Apache Ignite community, Denis worked for Oracle where he led the Java ME Embedded Porting Team -- helping bring Java to IoT.
Subscription businesses can lose the happiest of subscribers because of involuntary churn—that deadly form of attrition that comes from card declines and invoice failures.
Even slight variations in a subscription business’ churn rate can have significant impact on revenues, so it’s critical to address involuntary churn -- and easier than ever. The latest subscription technology leverages machine learning, which can improve transaction success rates and billing continuity, helping automatically reduce involuntary churn and boost monthly recurring revenue by an average of 9 percent.
Want to know more about how subscription businesses are making a positive impact on revenue? How can you optimize decline management and revenue recovery strategies based on your own unique business needs? Join our latest VB Live event and you’ll learn how to start and where, plus get a first look at the latest Revenue Recovery Benchmarks, which reveal the powerful impact of machine learning.
Don’t miss out!
Registration is free.
In this webinar, you’ll learn...
* The power of dynamic retry logic, optimized for each individual invoice
* The incremental lift that a well-designed dunning strategy can have on revenue
* The key metrics every subscription business should understand to prevent churn
* How to develop a comprehensive decline management and revenue recovery plan using proven strategies for successful transactions.
* Emma Clark, Director of Product, Recurly
* Devin Brady, Data Scientist, Recurly
* Stewart Rogers, Analyst-at-Large, VentureBeat
* Rachael Brownell, Moderator, VentureBeat
Akmal Chaudhri, Technology Evangelist, GridGain Systems
Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.
Akmal Chaudhri has over 25 years experience in IT and has previously held roles as a developer, consultant, product strategist and technical trainer. He has worked for several blue-chip companies such as Reuters and IBM, and also the Big Data startups Hortonworks (Hadoop) and DataStax (Cassandra NoSQL Database). He holds a BSc (1st Class Hons.) in Computing and Information Systems, MSc in Business Systems Analysis and Design and a PhD in Computer Science. He is a Member of the British Computer Society (MBCS) and a Chartered IT Professional (CITP).
When monitoring an increasing number of machines, the infrastructure and tools need to be rethinked. A new tool, ExDeMon, for detecting anomalies and raising actions, has been developed to perform well on this growing infrastructure. Considerations of the development and implementation will be shared.
Daniel has been working at CERN for more than 3 years as Big Data developer, he has been implementing different tools for monitoring the computing infrastructure in the organisation.
Kirk Borne, Principal Data Scientist, Booz Allen Hamilton
As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.
Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.
The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.
Another huge advantage of the data lake approach is the ability to annotate data sets and data granules with intelligent, searchable, reusable, flexible, user-generated, semantic, and contextual metatags. This tag layer makes your data "smart" -- and that makes your agile big data environment smart also!
James Serra, Data Platform Solution Architect, Microsoft
With new technologies such as Hive LLAP or Spark SQL, do you still need a data warehouse or can you just put everything in a data lake and report off of that? No! In the presentation, James will discuss why you still need a relational data warehouse and how to use a data lake and an RDBMS data warehouse to get the best of both worlds.
James will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. He'll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution, and he will put it all together by showing common big data architectures.
Robin Marcenac, Sr. Managing Consultant, IBM, Ross Ackerman, Dir. Digital Support Strategy, NetApp, Alex McDonald, SNIA CSI
Watson is a computer system capable of answering questions posed in natural language. Watson was named after IBM's first CEO, Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! (where it beat its human competitors) and was then used in commercial applications, the first of which was helping with lung cancer treatment.
NetApp is now using IBM Watson in Elio, a virtual support assistant that responds to queries in natural language. Elio is built using Watson’s cognitive computing capabilities. These enable Elio to analyze unstructured data by using natural language processing to understand grammar and context, understand complex questions, and evaluate all possible meanings to determine what is being asked. Elio then reasons and identifies the best answers to questions with help from experts who monitor the quality of answers and continue to train Elio on more subjects.
Elio and Watson represent an innovative and novel use of large quantities of unstructured data to help solve problems, on average, four times faster than traditional methods. Join us at this webcast, where we’ll discuss:
•The challenges of utilizing large quantities of valuable yet unstructured data
•How Watson and Elio continuously learn as more data arrives, and navigates an ever growing volume of technical information
•How Watson understands customer language and provides understandable responses
Learn how these new and exciting technologies are changing the way we look at and interact with large volumes of traditionally hard-to-analyze data.
After the webcast, check-out the Q&A blog http://www.sniacloud.com/?p=296
Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs
AI is changing the way organizations do businesses and how they interact with customers. AI continues to drive the change. Deep Learning and Natural Language Processing will become standards in AI solutions. Deep Learning is based on brain simulations and uses deep neural networks. AlphaGo is the first AI system to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. Baidu improved speech recognition from 89% to 99% using Deep Learning. Every AI and Machine learning scientist is required to know Deep Learning tools in his / her current job scenario.
In this session, we will be discussing what is Deep Learning and why it is gaining popularity. We will explain AI solutions using Deep Learning with a practical example. Deep Learning has an edge over other machine learning techniques as with the increased volume of data, performance increases with Deep Learning. Further, Deep Learning enables Hierarchical Feature Learning i.e. learning feature hierarchies.
Jen Stirrup, Gordon Tredgold, Joanna Schloss, & Lyndsay Wise
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.
Managing and analyzing data to inform business decisions
Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.
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