Getting to Caprica - Making VR Really Usable

Why is a data-driven strategy essential for the success of your business? Data Science has come so far recently, but why? What industry trends are impacting the Data Science and AI industries? David explores common language to demystify data science, he reveals where companies are today in Data Maturity, and he articulates what data opportunities exist for companies to become data-driven.

About the speaker:
David Yakobovitch is a Principal Data Scientist at Galvanize, responsible for delivering Global Instruction, Scaled Training Programs, and Customer Success. He also partners with Engagement Managers and Account Executives with Pre-Sales and Product Marketing. David hosts the HumAIn Podcast, a Top 100 Technology Podcast on Artificial Intelligence, Data Science, Developer Education and Future of Work (www.humainpodcast.com). Prior to Galvanize, David led Enterprise programs at General Assembly (Bloomberg and Booz Allen Hamilton). Prior to GA, David worked in the financial services industry with banks, quantitative firms, and alternative data providers including Citigroup, Deutsche Bank, ADP, Aflac, Intel, and IBM Watson.

Stay up-to-date on the latest tools and best practices that industry experts recommend in order to get the most value out of your advanced analytics and data science strategy.

You'll come away with:
- A better knowledge of the technology on offer to help scale your organization's approach to advanced analytics and data science
- Key factors to consider when adopting an advanced analytics solution
- Best practices for implementing a data science program and advanced analytics strategy that works for you
- And more!

Moderator: Lishuai Jing, Senior Data Scientist at GRUNDFOS
Panelist: Dan Darnell, VP of Product Marketing at H2O.ai
Panelist: Pragyansmita Nayak, Chief Data Scientist at Hitachi Vantara Federal
Panelist: Paul Kowalczyk, Senior Data Scientist at Solvay

In an increasingly digitally connected world with abundant data sources, manufacturers and logistics companies need even more efficient ways to make effective supply chain decisions in real-time.

How do you overcome the delays in analyzing real-time data? How can you translate that to better understand supply chain and distribution risks? How do you reduce the time to decide the best course of action and act on it?

In order to truly empower your supply chain with data that you can trust, decipher in seconds, use “right then & there,” and act without further need for analysis, you need Artificial Intelligence (AI) to provide a “business-friendly view” of location and condition data that’s collected in the field.

Join this webinar to learn how AI will provide a business-friendly view of your goods and assets monitoring data, and also play a crucial part in assimilating and instantly validating important data points across multiple data streams for faster and better decision-making.

About Avkash Chauhan, Head of AI & Digital Intelligence at Roambee:

Avkash Chauhan is transforming logistics and supply chain by using Artificial Intelligence to help manufacturers and 3PLs make the most of goods and asset visibility. Since joining Roambee, he is leading a team of AI solution developers and solution delivery engineers to assist their customers using AI technology and solutions to transform their business and have an edge over the competition. Avkash's career as a data scientist spans 20 years+ as an engineer, entrepreneur and tech leader working with global enterprises and businesses.

Data Science has reached a point of maturity in large organizations. Having proven its value on fascinating niche use cases, it is now on every executive’s mind and ready to radically transform business-as-usual processes. But in order to pull off the transformation, the organization and method of delivery of analytics must change first. Data scientists should transform from isolated “unicorns” to being part of well-thought-out value delivery process.

We will discuss how we at TBC made the organizational change, the use cases which were made possible through it and what kind of results were achieved. We will talk about lessons learned by the example of two of our successful use cases: digital affluent value proposition, and next best offer.

Presented by:
Mikheil Nadareishvili, Deputy Head of BI, TBC Bank
Mariam Lelashvili, Analytical Transformation Project Leader, TBC Bank
Levan Borchkhadze, Senior Data Scientist, TBC Bank

The democratization of clinical trials is disruptive and that’s a good thing. Join this talk with MDGroup's Chief Data Scientist Richard Maguire as he discusses:

Managing the Big Data Wave

- Performant Infrastructure not only to manage the data size and types, structured & unstructured, but to manage Compute at Scale
- A Data Lake allows you to manage the wave of data by capturing all of it
- Next step is to store data in a proper data store for data type and ensure that the data is clean
- Then, you need to analyze the data for decision making

Managing the Big Textual Data that is Usually Forgotten

- Textual Data is Normally Siloed, not because it is unimportant, but because many do not know how to manage it
- Natural Language Processing (NLP) uses linguistic and semantic parsing to uncover patterns that can be deployed in predictive analytics

COVID-19 has Accelerated Move to Virtual Trials & Wearable Sensors: Disruptive Event & Technology

- Major Benefit is Continuous Patient Sensor Data, so NOT Episodic
- This means making correlations and predictions more accurate
- The challenge is the size of this data stream as it can become a tsunami
- Data Management and Data Cleaning are Paramount
- Virtual Clinical Trials: The Future is Here Now

Richard Maguire is presently the Head, Data Science, mdgroup and is tasked with bringing predictive analytics into their patient primary offering via their Primarius mobile app and patient portal. With a background in predictive analytics and Natural Language Processing, Rick has worked to bring wearable sensors into a Healthcare IoT for clinical trials. He has been a Subject Matter Expert at Oracle for Predictive Analytics and Predictive Genomic Medicine as well as Real World Data/Evidence for a global IT provider. Experience as a Director of Clinical Diagnostics in Pathology for a very large comprehensive cancer centre.

Join the conversation as industry experts discuss how businesses are taking advantage of predictive analytics technology to gain a competitive edge in the marketplace.

You'll discover:
- Use cases that show how AI and machine learning are helping companies be more proactive than ever
- How predictive modeling can lead to more informed business decisions
- What steps organizations can take to adopt an AI-enhanced analytics strategy that works for them
- And more!

Being historically a manufacturing company, Grundfos aims to bring digital transformation into its core business. This radical move not only bring challenges, but also energizes a wave of exploring/adopting state-of-the-art cloud computing, machine learning and AI technologies that impact different business domains. The new advances in computing platforms and machine learning services brings convenience to productionize industrial intelligent solutions. However, practical obstacles are hindering the scaling capability.

This talk will take you to peek into some of the challenges that are faced in researching and deploying AI solutions within Grundfos. I will give you a picture on Grundfos’s digital initiatives landscape and our hands-on experience on scaling AI solutions that accelerates enterprise level adoption. Two concrete use cases in sales and marketing and supply chain management can shed some light on how business value, agile development, DevOps, data science, and MLops together enable success in business value creation. In particular, I will address the data challenge and the statistical methods, machine learning/deep learning techniques that are adopted to solve some real life challenges.

About the speaker:
Currently, Lishuai Jing is working with advanced statistical inference methods, machine learning and deep learning techniques, and general AI in the largest pump manufacturing company. He has a PhD diploma in statistical signal processing and wireless communication from aalborg university, Denmark. Before joining Grundfos, he was a researcher on 4G/5G and IOT communication technologies. He advocates data and AI-driven approach to improve business intelligence, operating efficiency, productivity and customer satisfaction.

Elaborating on his article, "Data Governance and The Art of the Fugue," Randy will guide the audience through a layperson's explanation of what a fugue is, using J.S. Bach's The Art of the Fugue as an example, and draw parallels between its structure and that of data governance frameworks. Today’s data, in its near-infinite variety of sources and types, resembles polyphonic music such as fugues more so than homophonic music like popular songs. But combining independent melodic lines is just as difficult as understanding disparate data sets.

Without some guidelines, the result is cacophony, when sounds combine with no rhyme or reason, and the ear hears nothing but harshly clashing noise. In the data world, we have the same situation when data lakes become data swamps, filled with in-comprehensive information not fit for any use, because of the lack of data governance.

Randy will also explore these ideas:
- How the subject of the fugue provides the listener guideposts similar to how metadata and lineage help analysts navigate data
- How following the fugue "governance framework" allowed Bach to compose with enormous inventiveness while still providing his listeners a transparent structure even with many moving parts
- How a similar approach to creating a data governance can fuel innovation

In conclusion, Randy will demonstrate how this musical perspective can enable a rethinking of the very goal of data governance, a goal extremely relevant to the world of exploding, ever changing data we live in today.

About the speaker:
Randy Gordon is a data governance professional with over 10 years of experience in leadership roles in financial services. Most recently, at Moody’s, Randy established their first formal data governance program, building the data governance framework, principles, standards, and organization.

The melee of raw data in the various forms of structured, unstructured and semi-structured data has its own unique challenges. Extracting the hidden nuggets of information to meet the vision and aid the strategic and tactical goals of an organization is a resonating objective of every stakeholder today. This requires a number of systematic, both synchronous and asynchronous processes and techniques to rhythmically accomplish the objectives in a transparent manner. Data driven process enablement and the resulting effective decision making starts from scratch - identification of the most relevant and related data assets, posing the business problem, determining the analytics components.

The solution should ideally be as reusable as possible in order to aid related problems resolution down the road; aiding the knowledge growth, process automation and the effective business and data interplay. The overarching goal needs to be to complete this unique jigsaw puzzle specific to every individual organization; fitting as comfortably and seamlessly as one wants their hands to fit in a pair of gloves.

This talk will focus on how the DataOps mechanisms such as data catalogs, data lineage and, data fusion leading to near-transparent self-service analytics and MLOps enable and enhance advanced actionable analytics outcomes.

About the speaker:
Pragyansmita Nayak is Chief Data Scientist at Hitachi Vantara Federal (HVF), a wholly owned subsidiary of Hitachi Vantara. She has over 20+ years of experience in software development and data science-related research and development. She holds a Ph.D. in Computational Sciences and Informatics from George Mason University (GMU) and Bachelor's degree in Computer Science from Birla Institute of Technology and Science (BITS), Pilani, India. Her Ph.D. thesis focused on the application of Machine Learning techniques such as Bayesian Networks for redshift estimation.

A fundamental need in various industries is to find underlying causal relations and use them to improve the business outlook of companies. All companies bar none would like to understand why their sales are declining, what leads customers to buy, how to improve employee motivation, where to allocate resources and what is the best competitive strategy.

The knowledge of causal relations is essential in achieving these goals. However, in most analyses, scientists draw inferences from correlation. However, correlation is not causation, and such correlative inferences can often be disastrously incorrect.

This talk covers the following:

- An insight into the data science of Causal Discovery
- How to generate causal hypotheses to determine root causes of operational situations
- How to conduct “natural experiments” to validate these hypotheses from a historical dataset
- Real-world case studies of the successful application of causal discovery in industry

About the speaker:
Vijay Nadkarni is Vice President Artificial Intelligence at Tech Mahindra, responsible for AI technology
and customer engagements across multiple verticals including automotive, manufacturing, aerospace,
healthcare, retail, semiconductor and others. Previously, Vijay was VP and global head of AI at Visteon,
responsible for AI across its product lines, notably autonomous driving and infotainment. A veteran of
Silicon Valley, Vijay has co-founded multiple startups in AI and cloud computing of which Veraz
Networks, a VoIP company, had a NASDAQ IPO, and another, Wellcore, was acquired.

Distributed ML/AI design strategy serves as a potential solution as it solves two of the most notorious problems, performing near real time analytics on humongous high-speed data. Identify Machine Learning models and find means to integrate them in to existing Distributed computing architecture. First, since the data is large and fast paced it pushes ML closer to the source of data instead of bringing data from different sources at a central location and then applying ML. Second, it focuses on figuring out the right modelling approach combined with the right architectural design for effectively doing distributed ML processing.

Distributed ML design strategy utilises best of both the worlds, Distributed Computing and Machine Learning algorithms. It brings along the advanced analytical and mathematical reasoning capabilities of Machine Learning and combine it with efficient distributed processing in the form of a group of machines either in a Big Data cluster or over a GPU farm or steaming edge devices or over IoT devices. It is more of a design thought process that enterprises should focus on while planning to leverage Distributed ML architecture for their business problems.

About the speaker:
Kuldeep Jiwani: Researcher, Data Scientist, Data Science Architect, Performance specialist, Entrepreneur.
Currently working as a Data Science Researcher and Distinguished Architect in Thales (Guavus) handling various Data Science projects. Along with actively exploring new techniques of Machine Learning via various research projects with focus on Unsupervised ML. Thus ensuring that Data Science could be effectively applied to real world scenarios and able to solve important problems.