The idea is that in the normal retrospective, there are lots of exercises that we do and one of my favorites is called the Timeline. I am sure you have seen it. You use cards or stickies of different colors and you begin. If it is a reiteration retrospective, you begin with the beginning of the iteration and you put the date and then the end date and then across the time line you put the stickies or the cards that reflect events and they are of different colors. Then you reflect back and you use that to drive actions that you are going to take at the end of the retrospective because, unfortunately, most of us, not just old people like me, cannot remember what happened. So it is an exercise to help you remember what happened.
Obviously, the mass market understands little of how the wearable technology works, what’s inside those little gadgets and frankly, why should they care? But for us, the ones with a vision, the ones who see a layer or two deeper inside these devices, for those of us looking for new markets, new business ideas, the question remains: Is there anything beyond a Fitbit bracelet or the latest Apple Watch? We’ve listened to Laurenti de’ Medici, our CEO, speaking at Digital Catapult a couple of weeks ago about the constant changes in wearable tech landscape, about “wearables” as we know them nowadays – fitness trackers, NFC rings, smartwatches – slowly morphing into embedded, ingestibles, implantables and smart sensors; logical changes leading to disruption, leading to new trends, new markets, new jobs, new industries such as fashion tech and digital health.
M can do those things because the software hands off things it can’t do to human operators known as “trainers.” Sometimes a trainer has to do all the work, but M is also capable of digesting queries it recognizes but can’t handle into easy-to-process summaries that make a trainer’s work more efficient. Right now this model is not efficient enough for M to be more than just an experiment, because it requires too many human workers. But Alex Lebrun, who leads the team working on Facebook’s assistant, says that it can become a real product because the work of the human trainers is gradually teaching the software how to do a greater share of the work. LeBrun and his team joined Facebook when the social network acquired the startup he cofounded,
"The market for the kind of IT skills you need to build payment systems seems to be pretty hot right now because you've got four big institutions and then some smaller ones like us dipping into that pool," he told the House of Representatives economics committee on Friday. A Greythorn study from last year predicted that Australia would head into a "huge" skills shortage within the next five years. The survey said Australia was at risk of losing its IT professionals to the overseas market. However, the most recent Skills Shortages Australia report by the Australian Department of Employment said there was no skills shortage in the ICT sector in Australia. "Demand for ICT professionals is subdued and employers have little difficulty recruiting workers who meet their skill level expectations,"
If the focus of the problem to be solved is internal and the state is existing, then the defined monetization opportunity is business optimization. When using data for business optimization, the value generation and recognition is not defined by revenue dollars or asset assessments for accounting ledgers. The monetized value of data in business optimization is defined by reducing costs or improving productivity in business operations. While the value of business optimization can certainly be defined in monetary terms, the value can also be recognized in soft terms such as increased employee satisfaction, reduced time and effort, or increased accuracy and quality all of which have significant value for the overall business.
Although most of us will never be tasked with goals of such scope, many of us have to manage projects in one way or another. The Project Management Institute estimates there will be more than 15 million new project manager positions added to the global job market by 2020—and many of the rest of us will still have smaller projects to manage on our own. Project Management, simplified, is the organization and strategic execution of everything that needs to get done to tackle a finite goal—on time and within budget. Whether developing new software, carrying out a marketing campaign, or landing a human on Mars, project management is what gets you to your goal.
“We’re trying to create a bit of diversity away from the big whoops and high fives,” Loftis says. She adds that while Salesforce tends to be lauded by customers, the notion that deploying services is simple is wrong: companies need to put in a fair amount of effort to get the best return on investment possible. “Users don’t necessarily feel they’re getting a lot of value for the data they’re being asked to put into the system. On ROI, it’s not that people feel they’re not getting enough but people say ‘this takes more investment than we thought it would’. It’s not just licensing and implementation; adoption and the change management perspective need to be factored in. There’s a misconception that Salesforce ‘just works’.”
Many machine learning solutions have already been developed, and they are continually being improved. I spent some time at Microsoft Research doing some early work in Bayesian reasoning and machine learning. We built a solution for traffic modeling that was spun out as Microsoft Research’s first startup company, called INRIX, which now provides real-time and predicted traffic information around the world. I see three tiers of commercial engagement with these types of technologies. For one group of companies, such as Google, Amazon, Facebook, Microsoft, and Apple, these technologies are strategic, and their investment is a hundredfold or more than it would be from a more conventional business.
These attacks may go undetected and this “noisy traffic” can significantly slow legitimate traffic or cause network outages. With legacy systems, mitigation requires labor-intensive manual intervention because there’s no automated method to handle the threat. If and when network security solutions do sense a NetFlow-based volumetric attack with an application component, manual mitigation can take 15 to 20 minutes. By the time the security team has developed a strategy, the attackers have likely morphed to new signatures.
The datasets themselves also tend to be born in the cloud. As I said, the types of applications that we're building typically focus on sales and marketing and social, and e-commerce related data, all of which are very, very popular, cloud-based data sources. And you can imagine they're growing like crazy. We see a leaning in our customer base of integrating some on-premise information, typically from their legacy systems, and then marrying that up with the Salesforce, or the market data or social information that they want to integrate and build a full view of their customers -- or a full exposure of what their own applications are doing.
Quote for the day: "There are only two kinds of [programming] languages: the ones people complain about and the ones nobody uses." -- Bjarne Stroustrup