Name: Using Docker to realise a modular Big Data Platform & Leveraging SANSA Stack
Start: 2017-10-24T12:00:00Z
End: 2017-10-24T12:00:42.000Z
Location: BrightTALK
Rating: 4.5

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.

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

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

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?

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

There has been much work done, by academics and product designers on data visualisation. But relatively little attention has been paid to the overall analytic experience - how users discover the features, orient themselves and then make use of their discoveries.
 
Tune into this session from industry thought leader Donald Farmer as he explores some very useful behavioral patterns which enable us to create a more engaging and decision-oriented analytic experience. The webinar will further consider how these patterns can be applied in tools for analytics specialists, business users and when embedded in operational applications.

Analyzing the analytic user experience

In the world of big data, the importance of data visualization and related technologies cannot be stressed enough. These tools play a crucial role in transforming raw data into easily comprehensible forms, stripping away unnecessary noise, and highlighting valuable insights that tell a compelling story. Effective data visualization is an art that requires a delicate balance between aesthetics and functionality. Data and visuals must come together to work in such a manner where the combination of analysis and storytelling make a powerful imprint and convey the right message.

But just as a coin has two sides, a foundational understanding of data visualization tools is essential for swiftly grasping data trends, outliers, and patterns. In this presentation, Dr. Sue Tripathi will explore: 
- What data visualization is and why is it important today. 
- How to create accessible data visualizations.
- Tools available on the market and how to evaluate them. 
- Implementation strategies. 
- Real-world examples showcasing the potential of data visualization in business.
- Trends and the future of data visualizations.

Creating accessible data  visualizations and evaluating appropriate tools

AI can be applied to various fields, including manufacturing, automobile, healthcare, finance, and IT, and its impact is becoming increasingly evident. However, developing an AI system that meets essential criteria such as reliability, safety, trustworthiness, security, explainability, interpretability, accountability, transparency, privacy enhancement, and fairness while managing harmful biases poses significant challenges. It is the collective responsibility of all stakeholders to address these challenges.

During the session, I will delve into the obstacles and ethical considerations involved in creating a responsible AI system. I will provide valuable insights into areas such as data privacy, data security, governance, regulations, and the integration of AI and ML into existing digital strategy frameworks. By participating in this session, attendees will gain a comprehensive understanding of the importance of developing responsible AI practices to harness the power of data-driven decision-making, ultimately driving business revenues and success in today's competitive digital world.

Key topics to be covered include:
- The significance of data in shaping data-driven decision-making strategies.
- Ensuring robust data governance and maintaining high standards of data security.
- Ethical considerations surrounding AI and the impact on data-driven decision making.

Ethical considerations in data-driven decision making

In today’s era of digital disruption, extending automation with AI capabilities, also known as intelligent automation (IA), remains on the agenda of C-suite executives. Enterprise leaders are recognizing that automation and AI technologies have the potential to transform productivity, improve accuracy, reduce costs, and improve customer and employee experiences. Extending automation with AI opens the door to driving further exponential impact in terms of accelerated innovation, higher revenue, better efficiency and enhanced experience for customers and employees.

By virtue of being at the intersection of process automation & AI, IA brings increased benefits. However, IA can also increase execution complexities. To successfully adopt and extend automation capabilities with AI, more than just the technical implementation is required – a holistic approach is required to achieve and sustain breakthrough value. Organizations need to navigate across process centric automation and data and machine learning centric AI for what may appear to be a minefield of complexities and dependencies – such as mapping business challenges to processes, incorporating business process optimization, having high data quality, creating integrated AI & automation governance, and having the right expertise in ML and MLOps, to name a few. 

Join Deloitte Consulting's leaders, Joe Surprenant (VP of Innovation, AI & Innovation) and Ketan Ambani (AI & Data Operations Managing Director), where they will present findings from surveying over 2,600 business leaders from Deloitte’s latest State of AI in the Enterprise, as well as findings from Deloitte’s Automation with Intelligence report where Deloitte surveyed over 470 executives across 35 countries. They’ll also dig into the keys to success and lessons from driving and maintaining intelligent automation programs, while highlighting success stories across several organizations.

Extending automation with AI capabilities

In recent years, there has been a significant and rapid shift in how organizations in the Life Sciences, Bio pharma, and Medical Device sectors prioritize digital transformation.

The Life Science Industry, in particular, has emerged as a frontrunner in digital disruption, transitioning from basic healthcare services to digital therapeutics and connected care. The COVID-19 pandemic has further accelerated the evolution of the healthcare landscape. Patient data sharing has increased, patients themselves are demanding greater engagement in their care, and non-pharmaceutical interventions are increasingly being utilized to treat various diseases. This heightened awareness of the future of health has prompted enterprise leaders in the industry to scale up and accelerate their automation and AI initiatives. However, without a well-defined strategy and approach, organizations may encounter stagnation in their digital transformation efforts.

To overcome these challenges, leading Life Sciences organizations have turned to AI-powered automation as a means to enable growth and success. This approach involves leveraging advanced technologies such as Generative AI, Machine Learning, Natural Language Processing, computer vision, and Robotic Process Automation to enhance the efficiency of automated processes.

By incorporating AI into automation workflows, organizations can empower their technology to make intelligent decisions, perform cognitive tasks, and automate non-rules-based processes. This not only opens up numerous opportunities for innovation but also presents new challenges.

Join Deloitte Consulting's leaders, Bhavish Madurai and Prashanth Sivadasan, to learn the importance of choosing the right capability model, engaging successfully with the business, and creating the right approach for demand generation. They will also dig into the keys to success and lessons for scaling and accelerating AI and automation programs across Life Sciences organizations.

Accelerating and scaling AI-powered automation in Life Sciences

In the session, attendees will explore the critical role of data in shaping successful digital strategies and discover how AI and ML technologies can be utilized to extract meaningful insights. Topics covered include leveraging AI and ML algorithms for data analysis, real-time analytics, predictive modeling, customer segmentation, and targeting. 

The session also addresses challenges and ethical considerations in data-driven decision making and provides insights on integrating AI and ML into existing digital strategy frameworks. By attending this session, participants will gain the knowledge needed to leverage AI and ML for data-driven decision making, enabling them to develop effective digital strategies that drive business growth and success in today's competitive digital landscape.

Key Topics: 
- Importance of data in shaping digital strategies.
- Leveraging AI and ML for data analysis and insights.
- Real-time analytics and predictive modeling for proactive decision making.
- Optimizing customer segmentation and targeting using AI and ML.
- Addressing challenges and ethical considerations in data-driven decision making.
- Integrating AI and ML into digital strategy frameworks.
- Exploring emerging trends and future directions in data-driven decision making.

Data-driven decision making: Harnessing AI and ML for digital strategy

Crowdfunding is a new frontier for financial service firms; but one that holds great promise for providing extended value to all of a financial service firm’s clients. For the firm’s issuer clients, crowdfunding is a relatively new, and often effective, way to raise capital for the issuer. For the firm’s investor clients, it is a chance for investors that were not able to access parts of the equity markets previously, to reach those markets and investments now. It’s also a more effective and efficient way for all investors to find and access different types of alternative investments.

The crowdfunding that will be discussed in this webinar is equity crowdfunding, as opposed to donation-based crowdfunding. The differences between these two major types of crowdfunding will be discussed, along with the unique characteristics and benefits of equity crowdfunding.

This presentation will also discuss how the use of Alternative Asset Classes and individual Alternative Assets can substantially increase portfolio returns while reducing portfolio risk. Alternative investments can be delivered by means of digital platforms such as crowdfunding platforms.

Here are some of the topics which will be discussed:
1. The different types of crowdfunding.
2. Equity versus donation-based crowdfunding.
3. The unique characteristics and benefits of equity crowdfunding.
4. Regulation CF crowdfunding.
5. Non- regulation CF crowdfunding.
6. How to get into the crowdfunding business.
7. Alternative investments – what they are and how to use them?
8. Increasing portfolio returns and reducing portfolio risk with alternative investments.
9. Putting alternative investments on crowdfunding platforms.

Using crowdfunding and alternative Investments to better serve clients

As big data and artificial intelligence (AI) continue to transform the finance industry, CIOs and tech leaders must stay on the cutting edge of these advancements to stay ahead of the competition and provide better experiences for customers. Embracing AI technology and leveraging big data can help financial institutions streamline processes, improve efficiency, and deliver more personalized services. 

To fully realize the potential of AI, CIOs and tech leaders must have a deep understanding of the latest AI trends and how they could shape the future of the finance sector. This includes exploring the implications of generative AI, such as ChatGPT, and identifying good and bad use cases for AI to ensure optimal outcomes. 

Join Starling Bank's Chief Technology Advocate, Jason Maude, for an insightful discussion on how big data and AI are revolutionizing the finance industry. Key topics to be explored include:  
- Today's top AI trends and future directions for the industry. 
- The potential impact of generative AI tools, including ChatGPT, on the finance industry. 
- Best practices for identifying good and bad use cases for AI in finance. 
- And much more.

How big data and AI are shaping the future of finance

While customer experience (CX) seems easy to define at first glance, in practice, it's difficult to capture how a customer truly feels, thinks, and reacts during these encounters. 

AI helps organizations overcome this problem, allowing them to quickly and accurately analyze customers’ cognitive, affective, sensory and behavioral responses. To effectively leverage, teams need to understand all the touchpoints throughout the customer journey, from pre- to post-purchase.  

By breaking down CX into smaller "micro" interactions, brands can gain valuable insights into their customers' needs and preferences, allowing them to personalize the customer experience and ultimately build loyalty. AI techniques like machine learning, deep learning, and natural language processing can help brands interpret and analyze these micro-interactions in real-time.  

In this session, Dr. Sue Tripathi explores how AI can be harnessed to optimize the customer experience. Key takeaways include: 
- How to leverage AI to manage key aspects of customer encounters and use customer journey mapping to design better touchpoints. 
- Embedding the voice of the customer; quantify the cost of inaction, through real world examples 
- Strategies to comprehensively manage a customer's cross-channel exposure, interaction, and transaction with a product or service. 
- And more…

Optimizing customer experience with AI

How to Build an Open Sourced Data Science Platform

Effective High-Speed Multi-Tenant Data Lakes

Enterprise AWS data lakes made easy: from POC to production.

Data Lake: Putting data, not technology, at the center of your Big Data strategy

Scaling Data Lakes through the Power of Cloud

Building Scalable Data Lakes Powered by Automation

Three Ways To Accelerate Your Data Lake Migration To Cloud

Data Fabric @ Scale: Breaking Through Legacy Data Architectures

Designing Data Lakes: Architecture options with open source tools

Virtual Data Lake: A Reality

Rev Up Your Cloud Integrations with Big Data Technology

Bias in the Web

Panel Discussion: Predictions for BI and Big Data Analytics in 2018

Advanced Analytics: The Future of BI is Here

What Advanced Analytics Will Look Like in the Future

Visualizing what the web knows about you

Panel Discussion: When Traditional BI Isn't Enough

Advanced Analytics in the Cloud - the latest cloud analytics innovations

Big Data in the Cloud

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

Using Docker to realise a modular Big Data Platform & Leveraging SANSA Stack

big data

docker

analytics

machine learning

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Enterprise Applications

Practicing business intelligence allows your company to transform raw data into sets of insights for targeted business growth. The business intelligence and analytics community on BrightTALK is made up of thousands of data scientists, database administrators, business analysts and other data professionals. Find relevant webinars and videos on business analytics, business intelligence, data analysis and more presented by recognized thought leaders. Join the conversation by participating in live webinars and round table discussions.

Business Intelligence and Analytics

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Using Docker to realise a modular Big Data Platform & Leveraging SANSA Stack

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