Bill Schmarzo, author of «Big Data: Understanding How Data Powers Big Business» and «Big Data MBA: Driving Business Strategies with Data Science,» is responsible for setting the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the «Big Data MBA» course. Bill was ranked as #15 Big Data Influencer by Onalytica.
Bill has over three decades of experience in data warehousing, BI and analytics. He authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Hel has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in this new hybrid and dynamic environment.
Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow’s business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business.
Making informed network investment decisions about emerging technologies such as network function virtualization (NFV) and software-defined networking (SDN) can help evolve the network to keep pace with the innovations of the devices and people it’s connecting. As you work with business leaders to make decisions about upgrading your infrastructure with these networking developments, it’s important to understand the similarities, differences, and benefits of dual NFV and SDN implementation.
With their ability to offer a new way to design, deploy, and manage the network and its services, NFV and SDN should be incorporated into modern enterprise networks and carrier infrastructures. However, lack of knowledge about these technologies can result in poor networking investments and missed opportunities to experience the benefits of each. While they hold similar principles, and benefit virtual environments overall, NFV and SDN possess distinctive characteristics that make them both individually important to successfully managing the network and its services.
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be looking at some significant engineering investment.
On-demand, serverless computing enables developers to try out a fleet of devices on IoT gateways with ease. With a sensor simulator built on top of AWS Lambda, it’s possible to elastically generate device sensors that report their state to the cloud.
In his session at 21st Cloud Expo, Raju Shreewastava, founder of Big Data Trunk, provided a fun and simple way to introduce Machine Leaning to anyone and everyone. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding.
Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Big Data teams at Autodesk. He is a contributing author of book on Azure and Big Data published by SAMS.
No hype cycles or predictions of a gazillion things here. IoT is here. You get it. You know your business and have great ideas for a business transformation strategy. What comes next? Time to make it happen. In his session at @ThingsExpo, Jay Mason, an Associate Partner of Analytics, IoT & Cybersecurity at M&S Consulting, presented a step-by-step plan to develop your technology implementation strategy. He also discussed the evaluation of communication standards and IoT messaging protocols, data analytics considerations, edge-to-cloud technical architecture, IoT platform selection, end-to-end security, enterprise systems integration and monetization techniques. Seize market opportunities by following this methodology to design and implement a systems architecture that meets complex demands for security, flexibility, durability, and scalability.
Blockchain offers impeccable security with its cryptography-based decentralized system as well as the plethora of possible uses retailers could exploit in the near future.
In a world of increasing cyberattacks, internet fraud and online hacking, blockchain comes as a breath of fresh air. With its encrypted data and decentralized network system, it’s a thorn in every hacker’s side. Generally being associated with the finance sector, blockchain is now taking retail by storm. It’s on a course that will change the retail industry as we know it. But how exactly is it going to achieve such a feat?
Smart cities have the potential to change our lives at so many levels for citizens: less pollution, reduced parking obstacles, better health, education and more energy savings. Real-time data streaming and the Internet of Things (IoT) possess the power to turn this vision into a reality. However, most organizations today are building their data infrastructure to focus solely on addressing immediate business needs vs. a platform capable of quickly adapting emerging technologies to address future business challenges.
It took Magellan’s crew three years sailing ships to circumnavigate the earth. Today, at hypersonic speeds of 7,680 MPH, it takes just over three hours to circumnavigate the earth. Data on the Internet, however, travels at 670 million MPH, which means it only takes milliseconds to circumnavigate the earth. In this age of digital businesses and digital interactions, companies must digitally transform to work effectively in a world where mass information moves at these unimaginable speeds.