Name: Supply chain planning with AI
Start: 2022-09-20T18:00:00Z
End: 2022-09-20T18:00:39.000Z
Location: BrightTALK
Rating: 4.4

The increasing adoption of multicloud is leading to business-critical data getting more siloed. This makes it difficult and time-consuming for data engineers, architects, and analysts to quickly gain value from data. BigQuery Omni connects to various cloud data sources and provides a single pane of glass to access the data using SQL – like queries without leaving the interface. Looker allows you to explore, share, and visualize your company’s data to make better business decisions. In this session, you’ll see a demo of how to break down data silos using BigQuery Omni and Looker to securely and cost effectively analyze and visualize data across clouds.

Cross-cloud analytics and visualization with BigQuery Omni and Looker

The past two years have taught us that "business as usual" is a thing of the past, exposing sharp differences between companies prepared for the digital economy and those that were not. Organizations are looking for ways to leverage their digital assets to best position themselves to succeed through disruption. In this session you will hear stories of different organizations that used business intelligence and analytics to successfully navigate their digital transformation journeys.

The Future of Business Intelligence

There’s no questioning the massive impact advanced analytics in AI and ML have had on our lives, and from a business perspective, these technologies have made available an unprecedented wealth of information for decision-making.  

This information has evolved our ability to understand what can happen in the future. What hasn’t evolved is our approach to making decisions.  Business decisions are more complex than ever, yet a critical element of intelligent decision-making is overlooked – mathematical optimization (MO) 

This talk will address several key topics on incorporating MO with AI and ML, including: 

Defining MO 
Describing how MO and ML can work together 
MO case studies and success stories 
Facts about MO (e.g. scalability, guaranteed optimality) 
Learning MO and getting started

The most critical and FORGOTTEN element in advanced analytics: ML, AI and MO

AI/ML-driven data applications are often considered as the final data application -- the target of all data preparation and metadata management activities performed upfront. However, AI/ML can also effectively aid the data aggregation and categorization for search, analytics, and discovery. Data tagging and metadata management can be automated and thereby, enhance the overall data experience for all entities involved in the data process – business or technical. In this session, we'll cover certain examples of AI/ML in data engineering.

About the Speaker
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" engaging with several challenging Data Engineering and Data Science projects in the US Federal Government domain. She has over 22+ years of experience in software development and data science. She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA. She has presented as part of various BrightTalk and other similar technical events in the past. She lives in DC Metro with her husband and enjoys cooking, travel, and photography. For more information on Pragyan's professional experience, please visit her LinkedIn profile at https://www.linkedin.com/in/pragyansmita and Twitter profile at https://twitter.com/SorishaPragyan.

AI and Machine Learning in Data Engineering

Enterprises increasingly deploy machine learning models to analyze corporate data that informs vital business issues. Identifying and gathering high quality data to develop these models is hugely challenging, however, as some information is protected by regulatory or proprietary reasons. Quality data collection is also expensive and time-consuming, leaving huge gaps in AI and machine learning model development. The development of synthetic data and explainable AI is helping fill these gaps by helping train machine learning models or develop new algorithms with insights that are readily available for data scientists.

In this fireside chat with machine learning scientist Vincent Granville, he'll discuss:
o Synthetic data design techniques, and how to identify business processes where most useful;
o How to test the quality of synthetic data;
o The benefits, and potential detriments, of explainable AI;
o Common modern enterprise data issues, such as managing unbalanced, inconsistent, small, outdated, and unstructured data;
o Ways to address data leakage, as well as small, wide and unobserved data;
o How synthetic data, explainable AI and other modern technologies are used to overcome these issues.

The Business Analysis Benefits -- and Limitation -- Of AI and Synthetic Data

Tune into this webinar to hear University of Washington's Deveeshree Nayak discuss the role of AI in cyber security and the how AI can be applied to address present cyber attacks.

Key Takeaways:
- How AI can be used in cybersecurity;
- Does cyber security have components of AI?
- Examples of cybersecurity in AI.

The Power of AI Cybersecurity

Why do customers churn? Why is revenue down? What are my top channels to acquire new customers? Why is my business losing more orders in rural areas?

The answer is in your data.

As we move forward in modernizing the traditional data stack, companies are collecting more and more data.  Getting through all the data is a growing challenge.  Operationalized ML powered analytics can make analysts more efficient and eliminate rote data science work. Data teams need the ability to rapidly process cloud-scale data, separating signal from noise with pre-built and operationalized AI/ML tools.

Join this webinar to learn the basics of how to:

-Rapidly sift through data (in seconds)
-Determine key drivers of metric change
-Leverage artificial intelligence and machine learning in your data stack

Analysts vs. Machines: AI/ML for cloud-scale data

For over 40 plus years, organizations around the world have used the language of SAS to build assets such as revenue forecast models, customer retention models, analyze risk at an enterprise leveland perform compound discovery.

As analytical technology has continued to evolve and develop, organizations are increasingly seeking to include modern programing languages such as Python and R into their model building and to understand how they can gain value from and utilize the speed and agility of The Cloud.

Today, organizations have choices with respect to the language of SAS deployment models.Join us for this 30-minute webinar where we will be discussing:

•What a transition away from legacy systems looks like and the benefits it can offer in terms of resource elasticity and scaling up the numbers of models produced compared to traditional models.
•How you can easily include modern programming languages into your models for swift deployment, removing frustrating devops blocks.
•How to leverage the decades of investment in legacy systems whilst incorporating the best of what modern analytics systems can offer and continue utilize the resources ideally suited for your organizations modeling building process.

Making the Transition from Legacy Systems to Modern Analytics Platforms

Tune into this webinar to hear AI security issues and the best approaches to apply while using AI-powered products in daily life. 

Key Takeaways:
- Security challenges in AI;
- Best practices to use AI products;
- Best practices to implement security practices.

Cyber Security Issues and Challenges in AI

Sometimes people manage machine learning projects as if they were software development projects. Sometimes they use approaches that have evolved and "just work for them." And sometimes they don't manage them at all! But the community "in the know" has come up with approaches and techniques that work to remove risk from ML projects and create high quality outcomes without burning out valuable teams in the process. 

I've written this all up in the book Managing Machine Learning Projects: From Design to Deployment (Manning, 2022). In this talk, I'll give an overview of the methodology and the motivations behind it. Attendees will learn:
- How to get a project started; 
- What the team needs to focus on up front;
- Where the risks come from; 
- How to handle the model development process; 
- Why evaluation is fundamental to success; 
- How and why to integrate ethics into the process;
- What needs to be considered when integrating ML into applications.

Managing Machine Learning Projects

Data is widely considered the most valuable asset in the world; that data is no longer just the exhaust of the business, but more and more is becoming the business. If data is the new oil in driving the economic growth of the 21st century, then how effective is your organization at leveraging data and analytics to derive and drive new sources of business and operational value? 

Data and analytics are unique economic assets -- assets not only never wear out, but actually can appreciate, not depreciate, in value the more valuable they are used. Give that, tune into this webinar session to discover how your organization can embrace a systematic approach to build out your data and analytic economic assets.

The Economics of Data and Analytics

Intelligence should make sense across the general, the specific and the in-between. Instead, we've got general rule-based thinking in one set of silos, and narrow probabilistic thinking in another.

A knowledge graph foundation for analytics and collaboration provides the means for solving two problems:

1) How to eliminate siloing, and
2) How to ensure quality, contextualized data at scale.

With the knowledge graph approach, we're not just looking for your keys under the lamppost because that's where the light is. Instead, the light is in more places. In other words, there's less observational bias and more explicit context.

The result is a shared advanced analytics environment we can have confidence in. 
 
This talk will discuss practical examples of knowledge graph foundations and how they're used today in analytics for ecommerce, healthcare, and scientific research. It will also lay out a roadmap to artificial general intelligence through more math, more data storytelling, and more actionable insight in the graph.

Bio
Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He is a contributor to Data Science Central with over 35 years of experience as an analyst, researcher, writer, editor and technology trends forecaster, including 20 years in emerging tech R&D at PwC.

Graph Foundations for Advanced Analytics and Collaboration

When we think of AI we tend to think of predictive models and supervised learning. But AI can be applied to the exploration and understanding of our data right from the start, leading to better business decisions. Virtualitics Co-head of AI, Aakash Indurkhya, sits down with author and data scientist Tobias Zwingmann to talk about how hypothesis-driven exploration is leaving valuable insight on the table, and setting you up for underwhelming AI. 

Join us as we discuss how to use AI to explore data—we like to call it Intelligent Exploration—and how it can impact your business.

You will learn:
How to discover high-impact insight that you’re overlooking today
How to determine where you should direct your AI resources by uncovering good use cases and assessing the feasibility of projects
How to remove bias and limits from your exploration to find impactful opportunities

How to overcome your biases and get better results from data exploration

The AI market in its current state is rapidly transforming, but not fully transformed. Organizations are shifting from AI Experimentation to AI at Scale, but very few organizations have been able to do so successfully to become completely AI-fueled. 

These AI-fueled organizations have been able to move AI out of a few use cases in an R&D setting and into a company philosophy, scaling AI across the entire organization. They have discovered how AI and Machine Learning (ML) can be used, developed roadmaps, defined strategies around Machine Learning Operations (MLOPs), and implemented ethics governance to realize evergreen business value. 

Join Deloitte Consulting's Rohit Tandon (ReadyAI Practice Leader) and Jatin Dave (ReadyAI Senior Manager), to learn how organizations can accelerate their AI journey and achieve scale with speed.

Delivering Business Value Through Scaled AI

Theoretically, AI in BI should allow all users within an organization to discern, easy to understand actionable insights that help in attaining business outcomes. Often, lack of definition, standards, defined metrics, alignment to business outcomes, paucity of skilled resources and budgets, poor implementation and hesitancy to adopt AI are among some of the top reasons for failure of AI in projects that showed tremendous promise but did not yield good business outcomes. This discussion explores some critical questions every business must ask before adopting AI and a step-by-step approach for implementing AI.

Some key areas of exploration during this session: 
• Why is AI all around us and what is AI in BI?
• What are the benefits of AI in BI?
• What does it mean to infuse AI in BI? 
• How does one successfully implement AI in BI? 
• How do you drive AI adoption in your business?
• What are some key steps to realize the power of AI in BI?

This session breaks down some of the complexity of AI in BI, where business can gain deeper insight into their data, using intuitive exploration tools, visualizations, dashboards and other easy-to-comprehend metrics and present a more compelling case to their clients, engaging them through concise, explainable data.

AI in BI: How to Kickstart Your Adoption Journey with Accenture

AI in BI: How to Kickstart Your Adoption Journey

Tune into this webinar to hear University of Washington's Deveeshree Nayak cover the security issues and challenges that may occur due to machine learning software and approaches.

Key Takeaways:
- Security challenges in the machine learning algorithm;
- Best practices to use machine learning products;
- Best practices to implement security practices in using AI/ML software.

Security Issues and Challenges in Machine Learning

Machine Learning & Artificial Intelligence

From the rapid rise of ecommerce, to labor shortages, to geopolitical  and macro economic tensions - CPG & Retail supply chains are being impacted in a myriad of ways. Join us as we share how companies have solved key inventory planning challenges by leveraging AI driven demand forecasting to bring agile, smarter decision making to daily supply chain operations. 

Hear how o9 integrated Google Cloud’s Vertex AI Forecast into their Digital Brain solution to solve forecast accuracy problems and ultimately reduce risk, improving financial performance. The Vertex AI Forecast offering, when integrated into the o9 Digital Brain’s open architecture framework, providing a more comprehensive supply chain solution.The end result helps ensure that demand will be met without running the risk of having excess and obsolete inventory.

Supply chain planning with AI

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