Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
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”→
• In studying buyer expectations and experiences from more than 1,000 IoT practitioners, GlobalData found that the majority of users rely upon basic reporting mechanisms as found in business intelligence (BI) systems when analyzing IoT data.
How can technology vendors guard enterprise IoT buyers against the dangers of cost and complexity?
They should endeavor to solve specific IoT problems through readily consumable, outcome-based IoT services.
As my compatriot Kitty Weldon pointed out in a blog post earlier this week, the success or failure of an IoT project isn’t something you stumble on a year or two after rolling out a solution. A recent Global Data Technology IoT Enterprise Survey of more than 1,000 IoT buyers showed that failure happens very early on during the investigation phase of a given deployment and more often than not centers on the cost and complexity of the project at hand. Continue reading “The Three Pillars of IoT Success”→
At its annual user conference, customer experience management player Genesys introduced Kate, a personified artificial intelligence (AI) platform tailored to augment and automate multimodal customer interactions.
Genesys Kate, however, is not meant to compete with AI platforms such as IBM Watson or Salesforce.com Einstein. Instead Kate seeks to blend its own capabilities with those offerings, serving as an open platform.
Personified AI platforms – suddenly every technology vendor seems to have an AI persona that’s eager to strike up a one-on-one conversation. There’s of course IBM Watson, Amazon Alexa, Apple Siri, Google Assistant, Salesforce.com Einstein, and Adobe Sensei, but that somewhat lengthy list doesn’t even scratch the surface of what’s available when you bring AI bots like Mitsuku, Poncho, Melody, Rose, and my personal favorite, Dr. AI. And now we have Kate, a personified AI platform introduced by customer experience manager Genesys this week during its annual user conference. Continue reading “Genesys Jumps on the AI Bandwagon, Invites Others Along for the Ride”→
At its annual user conference, Informatica quietly introduced an entirely new brand identity, which is designed to free the company from its ETL roots and target enterprise cloud data management.
To reach this goal, however, the company intends to do far more than merely switch up its logo and mission statement.
It is not uncommon for a technology vendor to make a break with the past by rolling out a new brand identity. Such efforts typically involve an extravagant launch party, an extensive marketing campaign, and of course, an extremely expensive logo retrofit. On average, these rebrands aren’t a good thing, at least not initially, since they’re often undertaken in response to an existing or anticipated threat. The idea is to create some cognitive dissonance among existing and potential customers, severing existing perceptions and creating new associations that are in tune with current (and hopefully future) market ideals. Continue reading “There’s a New Informatica in Town That Wants to Unleash the Power of Your Data”→
• Red Hat’s re-energized partnership with Amazon and its continued investment in Red Hat Insights smartly emphasizes AI-driven IT automation as a way to root out the foibles of human-mediated decisions in optimizing hybrid cloud/premises environments.
• Beyond automation, however, Red Hat’s growing focus on big data points to a distinct need within the industry to bring both operational and business analytics together within a single pane of glass.
• At its annual Huawei Analyst Summit 2017, Huawei spoke of solutions built on top of two pillars: an open IaaS/PaaS cloud platform paired with an open ecosystem of partners.
• The vendor’s premise? Let Huawei handle the infrastructure (the cloud, pipes and devices, as Huawei puts it), leaving the rest to those who know business outcomes the best.
At its annual Huawei Analyst Summit 2017, the Chinese powerhouse took a surprising turn. At past events the vendor emphasized its ability to squeeze more performance from its sizable portfolio of predominantly hardware-based data center and networking offerings. The objective was simple: demonstrate a better cost/value ratio for high value workloads like video. That approach remains a solid strategy, one that is all too familiar to rival firms like Cisco. This is especially true for vendors seeking attention from the still lucrative telecom operator marketplace. Continue reading “Huawei Analyst Summit 2017: Huawei, More Open Than You Think When it Comes to Big Data”→
• The seemingly immutable law of data gravity, which has kept most large-scale data stores tucked safely away behind the corporate firewalls, is no more.
• Cloud platform providers of all shapes and sizes are actively redefining such laws, showing that even the largest data warehouse can live happily in the clouds.
While attending the aptly named Domopalooza conference in Salt Lake City earlier this month, what struck me the most wasn’t the number of concerts, ski parties, parties and after party parties put on by the host, cloud-borne BI vendor Domo. Oh, that sort of thing is quite normal for the unconventional vendor from American Fork, Utah. I was instead dumbstruck to learn of the vendor’s seemingly crazy, all you can eat cloud business model. That’s right. Domo doesn’t care how much data you dump into its proprietary data warehouse or how many calculations, transformations, joins, etc. you perform upon said data. There’s just one price to pay, and that’s a simple, per user fee. Continue reading “Redefining the Law of Data Gravity, One Cloud at a Time”→
• Google wants to democratize AI and operationalize machine learning (ML) with the release of Google Cloud Machine Learning Engine, a platform that includes developer-friendly APIs and pre-trained data models.
• But what the company really needs isn’t just data, algorithms or even data scientists but instead a new breed of developers, who can build software that can anticipate outcomes.
It’s always the same at the end of a company’s keynote address. After all of the important messages have been conveyed and all of the product announcements have been made, a mid-level corporate mouthpiece will take the stage and provide the audience with some positive reinforcement of what went before. It’s like the closing credits of a film, something that may contain a nugget of interest to the cinephile. More often, it serves as filler, a thematic soundtrack to accompany attendees as they make for the exits.