As Principal Analyst for Collaboration and Conferencing at Current Analysis, Brad analyzes the rapidly expanding use of collaboration software and services as a means of improving business agility, fostering employee optimization and driving business opportunities.
Enterprise buyers looking to simplify their data integration woes through centralization are missing the value inherent in diversity.
Database diversity (actually diversity across all workloads) should not only be welcomed but actually sought after as a means of blending opportunity with capability.
Back in the ‘90s, the average enterprise maintained not one, not two, but seven databases on average: one for transactional information, one for data mining cubes, one for server logs, etc. Today, that has grown dramatically thanks to the proliferation of NoSQL-style databases built to handle unstructured, semi-structured and polymorphic data. Add to this the ever-expanding list of data storage options across public cloud data platforms, and you’ve an honest to goodness embarrassment of riches. Continue reading “Let’s Drain the Database Swamp! (Okay, Just Kidding)”→
• In 2017 the enterprise data and analytics vendor community emphasized opportunity in the cloud and the democratization of data. What will 2018 bring?
• We expect to see a shift in focus towards quality, to solving problems such as data governance, and putting AI to work within tactical business workflows.
What does the coming year have in store for the enterprise data and analytics marketplace? Sometimes, the best way to predict the future is to look at the past. To that end, here’s what we predicted for 2017 back in December of last year.
• IoT success will ride on pre-built data models and packaged software
• Smaller players will drive cognitive software innovation
• Vendors will prioritize self-service data integration, prep and management
• Vertical markets and specific use cases will fuel data-as-a-service adoption
• How did a computer algorithm like Google’s AlphaZero manage to learn, master and then dominate the game of chess in just four hours?
• AlphaZero’s mastery of chess stemmed from the sheer, brute force of Google’s AI-specific Tensorflow processing units (TPUs) – 5,000 of them to be exact.
“How about a nice game of chess?” With that iconic line of dialog from what is one of my favorite films, the 1983 cold war sci-fi thriller WarGames, nuclear war was narrowly averted by a machine (named Joshua) capable of teaching itself how to play a game. This week another machine, one of Google’s DeepMind AI offspring, AlphaZero, did something similar in that it took four hours to teach itself how to play chess and then proceeded to demolish the best, highest rated chess computer, Stockfish. After 100 games, AlphaZero racked up 28 wins and zero losses. So much for more than a millenium of human effort in teaching a computer how to play chess. But how was this possible? Was this a fair match? How did a computer algorithm like AlphaZero manage to learn, master and then dominate the game of chess in just four hours? Continue reading “The Chess Dominance of Google’s AlphaZero Teaches Us More About Chips Than About Brains”→
Digital home assistants like Google Home Mini and Amazon Echo owe users much more than privacy; if they are to be truly trusted, they must also explain how they think and how they make decisions.
Fortunately, regulations such as General Data Protection Regulation (GDPR) will begin asking such questions. The only problem is that artificial intelligence (AI) may not be able to provide any answers.
Google was quick to lay blame for its recent eavesdropping Home Mini fiasco on a ‘hardware bug,’ rolling out a quick update that purportedly prevents devices from inadvertently recording and reporting on overheard conversations should their owners accidentally press the wrong button. From now on, Google Home Mini will only record what you say after you capture its attention via “Hey Google” or “Okay Google.”
Machine learning (ML) algorithms are incredibly powerful, and companies like Google, Microsoft, Amazon, and Salesforce.com realize that – hence their intense interest in operationalizing ML and DL tooling.
But, those algorithms alone are no guarantee of value. Whether you’re predicting the weather or optimizing a delivery route, AI lives or dies according to the humans within whose care it finds itself.
Can we truly know whether or not we’re living out our lives as a part of a simulated, holographic model of the universe as proposed by mega-entrepreneur Elon Musk? Should we even care about such things? If you’re at all concerned about the weather – about the expected path a hurricane will take, let’s say – then the answer is a resounding ‘yes.’ I would argue in fact that we are living out our lives based upon countless simulations. Continue reading “Without People, There Would Be No Artificial Intelligence”→
• At its 10th annual user conference, modern BI leader Tableau unveiled a means by which customers can embed business processes within the Tableau interface, effectively upending commonly accepted ideas about the role of analytics in business.
• With Tableau’s new Extensions API, companies can start to think about analytics, not as a passive, informational adjunct to business processes, but instead as an active participant in the business itself.
These days APIs are a dime a dozen. Every vendor has one (or two), supporting basic routines like software automation or enabling more elaborate objectives like application embedding. The driving factor powering the proliferation of APIs is simple. They grant both interoperability and extensibility, two traits that are crucial to success – particularly within the enterprise data and analytics marketplace where heterogeneity reigns supreme.
At its annual Connect conference, Huawei set down its plan to become one of the five dominant public cloud platform providers, opposite IBM, Google, Microsoft, and Amazon.
Huawei’s cloud ambitions, however, aim not to dominate but to create an open, independent platform that augments and works with other clouds while maximizing differentiated Huawei functionality and expertise.
What is a computer? Is the cloud a computer and vice versa? In many ways, yes. Both a computer and the cloud represent a programmable resource, for example. Both dole out capabilities in the form of services. And both are finite in their scale and bound to the purpose of those who program them. Sure, the cloud can be seen as a never-ending cluster of computers slung together. But both, at the end of the day, return zeros and ones in exactly the same way. Continue reading “Huawei Aims for Public Cloud Market Domination in the Nicest Possible Way”→
Digital assistants like Microsoft Cortana are a lot like people in that they are the most interesting when they specialize, and when they become experts in a given field.
This is the case with human resources (HR), where there are many AI-driven chatbots available, each able to answer specific problems (like employee feedback) or support specific constituencies (like millennial employees).
If there’s one lesson to be learned from this week’s announcement that Microsoft Cortana will be able to converse freely with Amazon Alexa, it’s that AI-driven personal assistants – like people – do well to specialize. Cortana is quite adept at setting appointments and Alexa is pretty good at turning lights off and on. But don’t ask Cortana to turn down the heat or Alexa to set up an Outlook meeting. Like people, AI platforms grow up under very different circumstances, each with its own unique philosophy, friends, and culture (in the world of IT, ‘philosophy’ means AI algorithms, ‘friends’ equals data sources, and ‘culture’ means domain of expertise). Continue reading “Variety Is the Spice of Life for AI, Particularly in Humanizing HR”→
Technology companies like IBM and SAP are turning to the 50-year-old design and ideation methodology, ‘design thinking,’ in order to innovate more rapidly and better respond to customer needs.
Why is a process rooted in sticky notes and whiteboard doodles suddenly relevant for both technology providers and enterprise buyers? The reason is simple: with it, software developers can find and then answer the right questions.
A few weeks ago, a colleague passed me a link to an interesting live data dashboard for the Tour de France. Built by Dimension Data, this interactive, live view into the yearly bicycle jaunt about the French countryside was for me both fascinating and frustrating. As an IoT problem in action, the live tracking and comparative bar charts for various cycling groups (breakaway pack vs. Peloton, for example) provided an absorbing array of data points to ponder. Yet, I found myself looking for and failing to find answers to my own questions, like where are the current outliers and how does today’s stage compare with those in the past? Nitpicking, I know. But as an enthusiast of both data and cycling, I would have jumped at the chance to work with the team designing this app. Continue reading “How Design Thinking Can Save Digital Transformation and the Tour de France”→
• Cisco’s recent marketing campaign around “The Network Intuitive” calls for a radical rethink of the network where programmability, artificial intelligence (AI), and transparency point toward a self-aware infrastructure driven by business outcomes.
• But for that to work, for Cisco to help companies at last bridge the seemingly intractable rift that exists between IT and business concerns, the company will need to help its customers reimagine how apps are built.
It doesn’t matter if you run your enterprise app in the cloud or on premises, whether those apps are containerized, or if they adhere to a modern development paradigm (agile, RAD, et al.). Inevitably, each and every one will reveal what is perhaps the industry’s longest standing challenge — unifying IT and business. The app will go down; a database error will occur, client software will slow, or worse still the app may fall prey to a security breach or attack. And that’s when the finger pointing starts between development, IT, service provider, integrator or VAR, et al. Eventually the root cause of the problem will present itself, but in the meantime, reputations are sullied and money is lost. Continue reading “Cisco’s Intuitive Network Demands Much More than App and Infrastructure Unification”→