Name: How Design Thinking Can Improve your Data Science Workflow
Start: 2020-03-19T21:00:00Z
End: 2020-03-19T21:01:02.000Z
Location: BrightTALK
Rating: 4.57

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

Enterprise Strategy Group Distinguished Analyst Jon Oltsik Presents: 

From skills shortages to nuanced cyber-attacks, cybersecurity leaders have well-documented challenges that threaten the successful protection of their business. Generative artificial intelligence (GenAI) has burst onto the scene promising a long list of potential applications, with cybersecurity improvement touted among them. This session will explore cybersecurity use cases, including those employed by Security Operations Analyst for threat detection and response purposes.

Cybersecurity GenAI Use Cases

Enterprise Strategy Group Practice Director and Principal Analyst Paul Nashawaty Presents: 

Application developers are challenged with efficiently creating innovative solutions while managing time constraints, which can be mitigated by the transformative impacts of generative artificial intelligence (AI) streamlining code generation and accelerating development processes. Organizations have integrated generative AI (GenAI) into their operational setup to accelerate code creation, refine code structures, elevate code quality, and deliver personalized customer experiences. By harnessing GenAI, application developers tackle issues by capitalizing on the technology’s ability to automate tasks, drive creativity, and deliver innovative solutions.

The Role of GenAI in Application Development

Enterprise Strategy Group Senior Analyst Jonathan Brown Presents: 

A common criticism of AI is its high potential to increase carbon footprints due to its heavy compute requirements. As a result, sustainability has risen to become a critical consideration for nearly all organizations. This session will explore how organizes balance deploying AI-ready infrastructure with well-defined sustainability requirements and the metrics designed to monitor energy-intensive AI environments.

Deploying Sustainable AI Infrastructure

Enterprise Strategy Group Principal Analyst Mike Leone Presents:

The larger and more accurate the data set, the more effective the AI model. However, the road leading to that seemingly simple goal is strewn with roadblocks that pose substantial challenges for many organizations. Data experts and data quality tools and techniques to ensure the quality and accuracy of collected data while also assuring data is compliant with industry regulations is critical. This session will explore these considerations as well as how moving huge data sets to AI is also subject to service level agreements.

AI Data Quality, Compliance, and Infrastructure Requirements

In an era where artificial intelligence (AI) is transforming industries and reshaping the way we live and work, striking the delicate balance between innovation and data privacy compliance is paramount. This session delves into the evolving landscape of AI, discussing how businesses can harness its power while safeguarding sensitive data and adhering to privacy regulations. Explore the challenges, ethical considerations, and strategies that are shaping the future of AI in a privacy-conscious world.

In this session, you will: 
1. Recognize how AI is reshaping industries and business operations, presenting exciting opportunities for innovation.
2. Understand the increasing concern regarding data privacy as AI systems rely on vast amounts of personal and sensitive information.
3. Gain insights into navigating the complex regulatory landscape, including GDPR and CCPA, to ensure compliance in AI projects. 
4. Explore ethical concerns such as bias, fairness, and transparency in AI development and their intersection with data privacy.
5. Learn about strategies and best practices for maintaining a balance between AI innovation and data privacy, building trust with consumers, and staying compliant.

Data Privacy and the Future of AI: Balancing Innovation and Compliance

Most organizations use Microsoft Teams (or the equivalent toolsets from Zoom or Cisco Webex) for internal collaboration. But while Teams may be the de-facto standard, it’s hardly the last word in effective collaboration. 

This webinar introduces exciting examples of next-generation collaboration applications.

Beyond Microsoft Teams: Emerging Collaboration Applications

The collaboration application landscape is rapidly changing. Generative AI offers the potential to improve productivity and streamline employee engagement. New apps are quickly arriving that aim to bridge the home and office employee divide. And, employee experience platforms offer new ways to share information and establish employee relationships no matter the location. 

In this webinar, we'll share the top five emerging collaboration applications and what IT and business leaders need to do to ensure success.

Emerging Collaboration Applications

The tectonic shifts in today's business landscape call for more than just a repackaging of old operational models. A fragmented technology landscape and a laser focus on transactional efficiency won't cut it. What's needed is a paradigmatic shift -- a symbiosis of people, data, automation, and AI -- that activates continuous growth. This is the fundamental operational challenge, and also the most transformative opportunity, that confronts businesses across industries today.

Join Mark from Monitor Deloitte's Strategy Practice, a pioneer in AI Business Strategy and TMT advisory, as he decodes the role of Large Language Models (LLMs) in this transformation. LLMs serve as the 'Cognitive Connective Tissue,' intricately weaving isolated tasks and broader strategic outcomes into a unified tapestry. This goes far beyond operational streamlining; it redefines how cognitive and financial capital are allocated, fundamentally rethinking return on invested capital (ROIC).

This session will delve into real-world examples to demonstrate the multiplicative value creation potential of LLMs, from predictive analytics to real-time customer engagement. Mark will also introduce the ancient concept of 'Metis,' representing the balance between human ingenuity and machine efficiency, and discuss the pivotal 'efficiency vs. explainability' trade-offs that become increasingly relevant as tasks atomize.

Redesigning Productivity in the Age of Cognitive Acceleration

Machine learning is the most efficient known way to create software models that solve real-world problems. When trained correctly, ML models can know more than any human who ever lived, explain or summarize anything, and find patterns in the data that world-class experts have been missing for decades. But what happens when you train these incredible artificial brains by feeding them noise?

In this talk, we will explore both directions of this noise problem:
- What does AI "learn" when you train it in an unsupervised way, using data that hasn't been scrubbed beforehand?
- What do humans learn when the information that surrounds them is 90% noise, including noise that was produced by AI?

In the process, we will work together to articulate a new paradigm of AI that applies to everything from news media to business intelligence tools -- a paradigm that emphasizes quality over quantity, and focuses on removing bad inputs to produce better outputs.

About the speaker
Alex Fink is the Founder and CEO of the Otherweb, a Public Benefit Corporation that uses AI to help people read news and commentary, listen to podcasts and search the web without paywalls, clickbait, ads, autoplaying videos, affiliate links, or any other junk. The Otherweb is available as an app (ios and android), a website, a newsletter, or a standalone browser extension.

De-Noising AI: Why Quality Beats Quantity for Humans and Machines Alike

Unveil the AI platform dilemma! In this session presented by Gopinath Manimayan, lead architect at Moonraft Innovation Labs, you’ll delve into the ultimate showdown between open-source and commercial AI platforms. Discover the strengths, weaknesses, and unique offerings of each option, helping you make the perfect choice tailored to your business needs. Empower your enterprise with invaluable insights to leverage AI strategically and gain a competitive edge in today's dynamic market. 

In this presentation, you will: 
- Understand the key differences between open-source and commercial AI platforms.
- Explore the advantages and disadvantages of each option for your business.
- Learn how open-source platforms promote customization and community-driven innovation.
- Discover the benefits of commercial AI platforms, including robust support and seamless integration.
- Gain valuable guidance on selecting the right AI platform to accelerate your business growth.

Open Source Vs. Commercial AI Platforms: Make the Right Choice for Your Business

In recent times, generative AI (GenAI) has garnered significant attention and organizations are eager to explore its potential for enhancing business operations. 

In this session, scientist and professor Dr. Umesh R. Hodeghatta will explore the practical applications of GenAI within an enterprise context, shedding light on how it can propel innovation, creativity, and operational efficiency. Further, he’ll discuss the transformative role of AI as a catalyst for creativity, efficiency, and innovation within organizations.

Generative AI in Enterprise Operations

Data storytelling is building stories supported by data, allowing analysts and data scientists to present their insights to engage the audience and inspire them to make decisions. Generative AI (GenAI) can assist analysts and scientists while writing their stories supported by data and speed up the story creation process, making data-driven narratives more accessible, engaging, and impactful. 

This webinar will analyze how to use GenAI to empower data storytelling, by building data stories tailored to a specific target audience and insight. Through practical examples and real-life applications, we will explore how GenAI can adapt its narrative style and language to engage different audience groups. By attending this webinar, you will gain a comprehensive understanding of the concept of data storytelling and how it can be harnessed to make a lasting impact. In addition, you will discover the potential of GenAI in enhancing data storytelling, revolutionizing the way in which information is communicated and received. 

3 key takeaways:
- The concept of data storytelling.
- Using GenAI to enhance data storytelling.
- Tailoring GenAI to specific audiences and insights.

AI-Assisted Data Storytelling

While ChatGPT took the world by storm, we also saw the darker side of new GPT and GenerativeAI solutions. There are so many emerging use cases around data-driven AI solutions. And many of these evolve a legacy approach to AI. While many of these new initiatives aim to make life easier for users and the organization, there is a darker side to data. 

With over 90% of threats coming in directly through an inbox, how do data centers ensure they're protected against some of the most advanced threats in our industry? All-new findings and security research points to an entirely new era for security and the enterprise data center. Get ready to hear some of the newest findings. 

This session will explore the meteoric rise of LLMs and GPT solutions. We'll also dive into the dark web to see how malicious actors are using these technologies to target the data center. Most of all, we'll explore how leaders protect their clients, data, and digital infrastructure. 

This session will cover:
- Enterprise data centers and the evolution of AI.
- Updating the data center: Preparing for weaponized AI
- A dive into the dark web: Learn what your Data is worth 
- Understanding AI threats impacting enterprise data centers.
- Tools and strategies to fight back.

Weaponized AI and GPT: Updating data center security practices

The arrival of ChatGPT was a culture-altering event that upended business strategies and made AI a priority in IT planning. But ChatGPT and similar generative AI tools, with their large language models and uncanny talent for human-like communication and analysis, have complicated the AI picture. It can be hard for business and IT leaders to understand their options and make good choices.

Few areas seem more suited to AI-based automation and decision making than the supply chain with all its challenges and complexities. AI has long been touted as a way to improve predictive analytics and make supply chains more responsive by optimizing demand planning, inventory management, logistics and other key processes. Now generative AI promises unheard-of levels of intelligent automation.

In this in-depth discussion, speakers will explain AI types and terminology and their implications for supply chain management. Avoiding rosy scenarios, they'll zero in on the following:

- Where AI is making a difference in supply chains today
- How to sort through the hype and choose AI apps that add real value
- AI's possible impact on supply chain security

Practical AI Applications in the Supply Chain: Finding Value While Managing Risk

In today's ever-changing business landscape, supply chain management faces numerous challenges. From volatile market demands to complex logistics and inventory control, staying competitive requires innovation and efficiency. Just as market volatility slows and supply chains begin to stabilize, another disruption is coming – but this time, it will be for the better. 

With applications across demand forecasting, inventory management, quality control and more, Artificial Intelligence (AI) has the potential to revolutionize supply chain operations, leveraging real-time data insights to enhance performance, reduce costs, and improve customer satisfaction. 

In this session, join Dr. Amin Kaboli (Lecturer, Swiss Federal Institute of Technology at Lausanne, EPFL) to get a roadmap to future-proof your supply chain with AI.  

Topics for discussion include: 
- Practical applications of AI to effectively optimize your supply chain. 
- The latest AI trends and successful practices reshaping the supply chain landscape. 
- Strategic navigation strategies to successfully guide your organisation through its AI-driven transformation journey. 
- And more...

AI and the Supply Chain: What's Changing and Why Should You Care?

Practical AI applications in the supply chain: Finding value while managing risk

Data analysis is the heartbeat of modern marketing, operational improvement and human capital management. But with an increasing emphasis on privacy and governance, it’s important to strike a delicate balance between utilizing data for analytics and ensuring best practice. This session aims to shed light on this crucial issue.
 
Why should you attend this session?
 1.) Understanding the Importance of Privacy: Explore the significance of privacy in data analytics and why it must not be overlooked.
2.) Strategies for Balancing Analytics and Privacy: Hear about effective strategies that allow you to draw insights from data without compromising on privacy.
3.) Learning from Case Studies: Gain insights from real-life examples showcasing successful integration of data analytics and privacy.

Data analytics meets privacy

Affordable Fast Big Data Analytics: What is your strategy?

Tools, Techniques and Strategies for Enterprise Data

Enterprise Data Challenges

Data Governance for Business Transformation

How to get the technology stack right for your business

If you can’t measure it, you can’t protect it: Quantifying Privacy Risk

Connecting the Dots with Alteryx

Finding Diamonds in the Data Rough

A 4 Step Data Strategy for Big Data Analytics

How Nielsen is Becoming Predictive with the Help of an Enterprise Data Catalog

Enabling Outcomes in Medical Affairs: The Role of Data-Informed Decisions

Putting the “Science” into Data Science

Advanced Analytics and the Modern Enterprise

An AI Experience Playbook for Enterprise IT

Expert Interview: How to Foster a Data-Driven Culture

Cloud Data Warehouses and Data Lakes in a Modern Data Architecture

Busting Data Lake Myths and Dragons

Refine Your Data Lake Strategy for Analytics

Keeping Your Data in Check: Data Governance Made Easy

Preparing Data for Migration

Big Data Analytics for the Enterprise 2020

Design thinking is no longer only for software engineers.  Listen in as David Yakobovitch, Principal Data Scientist at Galvanize, shares insights into how his Fortune 500 clients use Design Thinking to Improve their Data Science Workflow, discover hidden insights, and improve their business metrics.

You'll learn about:
- What is Design Thinking and its Implications for Data Science
- The evolving Data Science Industry, and its change to Software Engineering Frameworks
- Trends in Data Science and Predictions in 2020
- New areas of focus for Data Science teams to apply to their workflows
- Insights into Privacy and Responsible AI for Data Science Organizations

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How Design Thinking Can Improve your Data Science Workflow

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