Name: Improving Time to Value for Enterprise Big Data Analytics
Start: 2016-03-10T18:00:00Z
End: 2016-03-10T18:58:17.000Z
Location: BrightTALK
Rating: 5

The Enterprise Architecture channel  presents the next generation of Enterprise IT: recognizing the strategic importance of digital transformation and the data center to make companies more nimble and competitive. These presentations will help demanding IT and BizOps professionals achieve flexibility, scalability and performance with reduced risk and complexity.

In the IT industry we are eager to talk about #Tech4Good -- how our systems help deliver improved environmental, social and economic outcomes. We use these examples as ways to improve our ESG reputation and ratings. We can show case studies of how IT contributes to achieving each of the United Nations Sustainable Development Goals (SDGs).

We are much less keen to discuss #Tech4Bad - how our IT is causing harm to planet, people, and yes, to profit. This can be through the pollution associated with our IT equipment, through the carbon emissions from customer use of our services (Scope 3) and through negative impacts of applications on individuals and society. 

As a business or IT leader you need to assess and mitigate the risk that your systems pose to ecosystems, well-being and shareholder value.  In this session you will:
•        Learn about frameworks for assessing the impacts of IT on sustainability.
•        Hear examples of #Tech4Bad and their effects.
•        Start the process of mitigating your IT impacts and protecting your ESG ratings.

#Tech4Bad: Are you putting your ESG reputation at risk?

As ESG and CSR strategies begin getting deployed across organisations, IT teams are being given objectives around reducing their greenhouse gas emissions.  

The impact of digital services is extremely tangible. For instance, producing equipment requires the extraction of raw materials and is energy-intensive across production, usage and end-of-life. As we work towards our sustainability goals, it's never been more important for IT teams to have a solid grasp of their environmental impacts and to develop new strategies and processes to secure a greener future. 

In this session, join Lorraine Le Baud (IT sustainability expert at Infogreen Factory) as she explores:  
- The environmental and social impacts of IT 
- Approaches to assess your team’s maturity with regards to sustainable IT processes and usages. We will go beyond the carbon footprint and propose a 360° diagnostic, addressing the maturity of IT buying processes, eco-design processes, IT competencies and governance. 
- Key deliverables associated to diagnostics, such as the stakeholders mapping, the materiality matrix devoted to IT stakes, diagnostic tools 
- A conclusion to open to the Green IT roadmap construction approach.

About the speaker:
Lorraine has more than 20 years of experience working in consulting companies such as Accenture and Deloitte. Shre also work as an IT director of an international oil & gas company. 

She has developed an expertise in IT sustainability since 2019 and has recently graduated a master degree in circular economy, with a specialisation in digital devices refurbishing processes

IT sustainability: Build a roadmap towards a greener future

We are in a new era where organizations need to care about environmental sustainability in a way they never have before. But it takes collaboration, strategy, and knowledge in order for business and IT leaders to make sure they are doing their part.  

Rajeev Pai, Director of Technology Strategy & Transformation at Deloitte, is delivering this presentation to help you better understand the benefits of sustainable IT through the enterprise architecture lens and gain the insights you need to embark on the journey in a way that makes the most sense for your organization. You will see why sustainable IT is while good for the environment is also great for business and for the enterprise. 

In this webinar, attendees will explore:  
- What exactly is sustainable IT?
- How it enables a sustainable enterprise/business.
- A key set of principles across each architecture domain enabling enterprises to begin the journey towards sustainable IT.
- A practical starting point for CTO to begin the IT sustainability journey.
- Learnings and good practices on IT sustainability from the frontline.

About the speaker:
Rajeev is a leader in the space of Technology Consulting & Transformation with over 20 years of experience across the globe in the financial services industry, helping business leaders navigate change. He combines deep domain expertise in Capital Markets F2B Processes and Banking to provide thought leadership and tailored advise while challenging business leaders to achieve their vision. He is an AWS Certified Solutions Architect experienced in large scale System Integration, Platform Re-engineering, Enterprise Data Management (Data Strategy, Architecture and Management), Analytics & Data Visualisation, Operating Model constructs, Design Thinking, and has strong familiarity with Emerging Tech. He is certified in Business Sustainability Management from the Cambridge Institute of Sustainability Leadership (CISL).

Sustainable IT: Enabling a resilient business

Sustainability is moving higher and higher on the IT priority list. According to extensive research performed by Enterprise Strategy Group, there are six primary factors -- driven by sustainability and ESG initiatives -- changing the IT buying culture:
- IT supplier ESG programs do matter to IT buyers.
- IT buyers expect to pay a premium for ESG compliant goods.
- How buyers access supplier ESG is not yet mature.
- Corporate ESG is a branding exercise.
- The third-party ESG compliance tools and services market is set for rapid growth. 
- ESG is driving strategic IT/business alignment. 

In this research-based talk, Enterprise Strategy Group Senior Analyst Jon Brown, will present eye-opening results from their latest study exploring how ESG impacts IT strategy as well as discuss how buyers and sellers can better understand the current impact of ESG programs on IT purchasing in order to make better buying decisions.

The role of ESG programs on IT decision making

Addressing environmental and social impact is now a business imperative to retain customers, attract talent and to comply with evolving regulations and reporting, so many businesses are transforming to a 'purpose driven' model. Information technology can simultaneously be part of the solution and part of the problem when it comes to environmental and social impact, so what exactly does a sustainable IT practice look like and why should we care? What can we contribute? Where do we start?

This session will cover the scope of sustainability, how the consumption of IT services and products are contributing to the big picture and what practical steps IT and procurement departments and professionals can take to make a difference. It's packed with stats, facts, figures, concepts and advice that will get you thinking about sustainability on a whole different level.

What is sustainable IT practice and why should I care?

IT is increasingly costing the earth, in its greenhouse gas emissions, use of raw materials, energy and water, as well as e-waste and more. Businesses and IT leaders need to develop more sustainable ways of working to mitigate the environmental risks of IT expansion.  

While CSR strategies do help organisations navigate uncertainty, leaders need to understand the connection between them and other key business challenges, such as resilience, business strategy and sustainable IT. 

In this session, Denis Didier, Sustainable IT Manager with ISIA will discuss how to leverage change management, collective intelligence and strong leadership to build a sustainable IT strategy that is right for your company.   

Join this session to explore: 
• The Impact of ICT in 5 figures and trends
• Six steps to make ICT more environmentally friendly
• Why sustainable IT can be done both efficiently & profitably

Methodology for leaders looking to make IT more environment friendly

Digital sustainability needs to be looked at from two perspectives: how can we better use digital technologies to support sustainability initiatives, and how to ensure digital products and services themselves are sustainable. Both of these need to be clearly connected to the organization's strategy - we are way past the 'Business and IT' paradigm, separating the two as if that's an option.

This session addresses common challenges with digital sustainability, and provides the participants with the following:
- Techniques to address common challenges with digital sustainability.
- Recommendations for embedding sustainability in the organization's strategy.
- Ways to successfully support organizational changes needed for improved sustainability.

How to improve digital sustainability, sustainably

Composable disaggregated infrastructures provide a promising solution to addressing the provisioning and computational efficiency limitations, as well as hardware and operating costs, of integrated, siloed, systems. But how do we solve these problems in an open, standards-based way? 

DMTF, SNIA, the OFA, and the CXL Consortium are working together to provide elements of the overall solution, with Redfish and Swordfish manageability providing the standards-based interface. OFA is developing an Open Fabric Management Framework (OFMF) designed for configuring fabric interconnects and managing composable disaggregated resources in dynamic HPC infrastructures using client-friendly abstractions.

In this webinar, attendees will understand how use cases for scaling management of storage and fabrics and beyond are addressed in these architectures, and the applicability to additional technologies.

Casting the Net: Scaling Management of Storage and Fabrics

Data centers continue to expand their environmental footprints, currently consuming 2% of the developed world’s electricity. Experts predict this number could rise to 13% by 2030. This webinar will cover energy-efficiency in data centers and ways to rein in costs and improve sustainability. This includes delivering more power efficiency per capacity, revolutionizing cooling to reduce heat, increasing system processing to enhance performance, and infrastructure consolidation to reduce the physical and carbon footprint.
 
Join this webcast and you’ll hear our panel of experts discuss:

• Defining Sustainability  
• Why Does Sustainability Matter for IT? 
• Sustainability for Storage & Networking 
• The Importance Measurement and KPIs
• Sustainability vs. Efficiency 
• Best practices - Now vs. Future  
• Bringing your IT, Facilities and Sustainability Organizations Together

Sustainability in the Data Center Ecosystem

In today’s rapidly evolving business landscape data plays a critical role. Modern data architecture provides the tools and practices to harness the power of data and turn it into strategic advantage.  Whether it is data lakehouse, data fabric, data mesh or cloud data governance modern data architecture is enabling business leaders to make better decisions, quicker.
 
In this talk, Rajeev Pai, Director of Technology Strategy & Transformation at Deloitte, will explore what does modern data architecture entail and how it can be used to unlock business value. 
 
Key takeaways:  
- The need for newer ways for bringing, processing and distributing data in the enterprise. 
- Leading patterns and practices organizations are adopting in this journey with examples.
- Challenges organizations may face when adopting some of these new approaches.
- And how to overcome those challenges.

Whether you are a business leader looking to drive growth and innovation or a data professional seeking to help your business this talk will provide valuable insights from frontline and research.

About the speaker:
Rajeev is a leader in the space of Technology Consulting & Transformation with over 20 years of experience across the globe in the financial services industry, helping business leaders navigate change. He combines deep domain expertise in Capital Markets F2B Processes and Banking to provide thought leadership and tailored advise while challenging business leaders to achieve their vision. He is experienced in large scale System Integration, Platform Re-engineering, Enterprise Architecture, Enterprise Data Management (Data Strategy, Architecture and Management), Analytics & Data Visualisation, Operating Model constructs, Design Thinking, and has strong familiarity with Emerging Tech. He is a certified in Business Sustainability Management from the 
Cambridge Institute of Sustainability Leadership(CISL).

Data-driven success: How modern data architecture unleashes business value

The SODA Foundation, in partnership with Linux Foundation Research, has recently published its Data and Storage Trends 2022 Report, “Data and Storage Strategies in the Era of the Data-Driven Enterprise.” The findings from this global study provide a comprehensive look at the intersection of cloud computing, data and storage management, the configuration of environments that end-user organizations are gravitating to, and priorities of selected capabilities over the next several years. 

SNIA is pleased to host SODA members who led this important research for a live discussion and in-depth look at key trends driving data and storage, including:

• The top 12 trends in data and storage
• Data security challenges facing container deployments
• Approaches to public cloud deployment
• Challenges for storage observability
• Focus on hybrid and multi-cloud deployments
• Top use cases for cloud storage services
• The impact of open source and data and storage

Top 12 Trends in Data and Cloud Storage

Cybercriminals have always been about data – stealing data, compromising data, holding data hostage. Businesses continue to respond with malware detection on laptops and networks to protect data and prevent breaches, so why should storage be left out?  Storage houses what the bad actors are targeting - your data.  Is there anything we can do from within the storage layer to further enhance defense in depth?  
 
Enter "Cyberstorage", a term coined by Gartner, which is defined as doing threat detection and response in storage software or hardware. A parallel, related trend in the security industry is eXtended Detection and Response (XDR) which shifts some of the threat detection from centralized security monitoring tools (SIEMs) down into each domain (e.g., endpoint, network) for faster detection and automated response.  Factor in the growing impact of ransomware and all these forces are driving the need to find creative, new ways to detect malware, including from inside the storage domain.   
 
In this session we'll discuss:
• Cyberstorage and XDR – what are these emerging trends?
• Threat detection and response methods through a storage lens
• Possible approaches for detection when used in conjunction with security tooling
• Why silos between security and storage need to be addressed for successful threat detection

Cyberstorage and XDR: Threat Detection through a Storage Lens

Industry analysts predict Deep Learning will account for the majority of cloud workloads. Additionally, training of Deep Learning models will represent the majority of server applications in the next few years. Among Deep Learning workloads, foundation models -- a new class of AI models that are trained on broad data (typically via self-supervision) using billions of parameters – are expected to consume the majority of the infrastructure.
 
This webcast will discuss how Deep Learning models are gaining prominence in various industries, and provide examples of the benefits of AI adoption. We’ll enumerate considerations for selection of Deep Learning infrastructure in on-premises and cloud datacenters. Our presentation will include an assessment of various solution approaches, and identify challenges faced by enterprises in their adoption of AI and Deep Learning technologies. We’ll answer questions like:
·       What benefits are enterprises enjoying from innovations in AI, Machine Learning, and Deep Learning?
·       How should cost, performance, and flexibility be traded off when designing deep learning infrastructure?
·       How are cloud native AI software stacks such as Kubernetes being leveraged by organizations to reduce complexity with rapidly evolving software stacks (TensorFlow, PyTorch, etc.)?
·       What are the challenges in operationalizing Deep Learning infrastructure?
·       How can Deep Learning solutions scale?
·       Besides cost, time-to-train, data storage capacity and data bandwidth, what else should be considered when selecting a Deep Learning infrastructure?

Training Deep Learning Models in the Cloud

Machine learning (ML) is the study and development of algorithms that improves with use of data. As it deals with the training data, the machine algorithm changes and grows. Most ML models begin with “training data” which the machine processes and begins to “understand” statistically. 
Machine learning models are resource intensive. To anticipate, validate, and recalibrate millions of times, they demand a significant amount of processing power. Training an ML model might slow down your machine and hog local resources. 
The proposed solution is to containerize ML with NVMe over Fibre Channel (FC-NVMe) by putting your ML models in a container. In this webcast, we will highlight the benefits of containerizing ML models with FC-NVMe, discussing: 

• Containers are lightweight software packages that run-in isolation on the host computing environment. Containers are predictable, repetitive, and immutable. This ensures that no unexpected issues occur while moving them to a new system or between environments. A cluster of containers can be created with a configuration suited for machine learning requirements. Containers are also easy to coordinate (or “orchestrate”). 

• Artificial Intelligence (AI) at scale sets the standard for storage infrastructure in terms of capacity and performance, making it one of the most crucial factors for containers.

• FC-NVMe is an extension of the NVMe network protocol to Fibre Channel delivering faster and more efficient connectivity between storage and servers and providing the highest throughput and fastest response times. 

• The combination of FC-NVMe with an NVMe SSD solution with containerized ML allows orchestration to scale data-intensive workloads and increase data mining speed.

Benefits of FC-NVMe for Containerized ML Models

Open-world learning (OWL) has taken on new importance in recent years as AI systems continue to be applied in real-world environments where structural violations of expectation or novelty can occur with non-trivial frequency. Novelty can impact AI performance profoundly, ranging from overt catastrophic failures to non-robust behaviors that do not take changing context into account. In this presentation, the University of Southern California's Dr. Mayank Kejriwal argues that designing machine intelligence that can operate in open worlds, including detecting, characterizing, and adapting to novelty, is a critical goal on the path to building systems that can solve complex and relatively under-determined problems. 

He will also discuss and distinguish between three forms of OWL (weak, semi-strong and strong), and between the development of OWL algorithms in active versus passive domains. Finally, he will explore the issue of how we can properly evaluate OWL approaches. Unlike traditional machine learning, an OWL algorithm must be capable of handling situations that are genuinely and structurally unexpected. Hence, their practical evaluation can pose an interesting conceptual challenge.

Key takeaways:
- An overview on open-world learning and why it is so important in real-world infrastructures.
- How open-world learning can provide a competitive advantage.
- Reasons why open-world learning is the next frontier in machine intelligence.
- The key difference between weak, semi-strong and strong open-world learning systems.
- A primer on evaluating and stress-testing open-world learning. 

About the speaker:
Dr. Mayank Kejriwal holds joint appointments at the University of Southern California's Viterbi School of Engineering. His research, funded by the US Department of Defense, corporations and philanthropists, is focused on applying AI to solving complex data-intensive problems. He is the author of four books, including an MIT Press textbook on knowledge graphs.

Explore open-world learning at the edge: the next stage of machine intelligence

With companies all over the world grappling with exponential data growth, data intelligence at the edge is a way for organizations to manage the increasing flow of data in a more efficient manner, optimize information use, reduce costs and improve their business performance. An IBM marketing cloud study indicated 90% of global data was created in the last two years, growing at 2.5 quintillion bytes of data per day.  In order to keep up, companies are now deploying smart-connected sensors and assets daily, requiring machine to machine and internet of things (IoT) networks to alleviate costly inefficiencies of data management in the cloud. IoT networks must embrace centralized architecture, where processing raw data begins at the network edge. 

Companies have utilized data flow, the cloud, or cores or racks of equipment purchased to perform certain calculations and storing these results to bill data center operating expenses. Rather than run up costs by processing and storing data for stretches of time, companies are beginning to use artificial intelligence (AI) and machine learning (ML) to help eliminate wasteful data while streamlining relevant data meaningful to the business. AI-powered edge analytics can aid in improving response and efficiency rates, reducing operating costs and more quickly identifying raw data that hold little to no value for the business.  

Tune into this webinar, presented by industry thought leader, Dr. Sue Tripathi to learn:
1. The use of AI/ML at the edge, requiring increased processing power and logic to be built into edge devices. 
2. Key components to protect critical infrastructure, including security algorithms, encryption keys, and scan updates.
3. Data management at the edge of the network. 
4. Examples of real-time application of data intelligence at the edge with good business outcomes and timely and informed decision making.

Data intelligence at the edge

Real-time information systems impose deadlines for producing control responses, as latency in their data generation, processing and transfer may cause impermissible failures. This characteristic mainly affects data intelligence related to methods and tools for classifying and integrating real-time information. Companies can achieve timely solutions by utilizing local premises rather than supporting centralized servers or clouds. Instead, computing within the network partly takes place near the physical end points which is where edge computing paradigm comes in to help. The ultimate benefit of edge computing is cloud optimization. 

Edge computing enables splitting and sharing data between network data centers and local computing power. Meanwhile, strong authentication techniques ensure confidentiality, integrity and consistency of shared data.

Tune into this in-depth webinar to gain a proper understanding of edge computing and a deeper knowledge of the following concepts:
• Edge vs other computing paradigms.
• Infrastructure on the edge.
• Data intelligence at the edge.
• Edge data privacy.

About the speaker:
Yeghisabet Alaverdyan is currently the Head of Systems Integration Department at EKENG CJSC since 2018. She graduated from the National Polytechnique University of Armenia in 1980 with a specialization in System and Electrical Engineering. She started her professional carrier as an engineer at the RA National Telegraph. Senior Engineer, then Chief of the National Telegraph Computing Complex. In 2003, she began her teaching career as a lecturer teaching Discrete Mathematics; Finite Automata Theory; Cryptography; Programming in C++.  She obtained her PhD in 2012 and is the author of more than 20 scientific papers published in local and international scientific journals. Her current research is in Mathematical Cryptography, AI and Cognitive systems modeling.

Edge computing: Infrastructure and data intelligence

In the aftermath of COVID-19 that forced everyone in isolation, working from home became a normality, resulting in high volumes of day trading and ever-growing gaming communities around the world needing rapid access to information. Because of this minimum latency has increased tremendously. 

Edge can help bring the computing resources closer to where data is generated and consumed, so companies do not have to solely rely on a centralized, cloud-based infrastructure. This has provided the opportunity for the new generations to spend more time with family while working from home, as well as for gaming and to experiment with new endeavors such as day trading. This shift will contribute to the digitalization of our daily lives as we move to a fully developed digital economy and culture. With its benefits, edge computing also also has the potential to significantly increase both physical and logical data security concerns and present companies with a challenge for maintaining and upkeeping these facilities.

Tune into this webinar from industry thought leader Farhad Mehraban as he explores the use of edge computing and its impact on work, trading and other facets of our daily lives.

About the speaker:
Farhad Mehraban is a leading digital economy, information technology, cloud and data center expert who consistently delivers latest and most credible solutions to the industry at large and across vast geographical and vertical coverage terrains.

Edge computing's impact on today's gaming and trading culture

In this webinar Storage Switzerland, Hitachi Data Systems and Brocade discuss why enterprises need to invest in big data analytics, how they can make that investment and what are some of the key requirements in designing a system.

Improving Time to Value for Enterprise Big Data Analytics

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Improving Time to Value for Enterprise Big Data Analytics

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