Stretching Agile is not easy, especially when factors like distributed development and teams with different culture come in place. In my opinion, how you communicate with people is fundamental if there is a need to create awareness and self responsibility. Another advice is to put robust working agreements in place, created and reviewed with involved parties, not enforced or imposed, so everybody will understand their benefits. ... First I would say it is primordial to understand the culture and how other people behave, react and talk. Second, it is important to have frequent face to face meetings and trips between sites. After getting to know people we tend to work together in a more collaborative way, that’s how humans behave.
It turns out the robot is not good at grasping "meaning in a broad spectrum," said Noriko Arai, a professor at the National Institute of Informatics, who heads the team behind Torobo-kun. Torobu-kun, for instance, did not perform well in English, where it had to link phrases to come to logical conclusions. It received scores of 36.2 in listening and 50.5 in written exams. "As the robot scored about the same as last year, we were able to gauge the possibilities and limits of artificial intelligence," she said. Torobu-kun received scores of 45.1, 47.3 and 57.8 from 2013 to 2015, according to the Asahi Shimbun. This year, the score was lower than last year. However, the machine showed progress in some areas, such as physics and world history.
Awesome Business Analysts must learn how to operate well in the fog of projects. There’s always going to be ambiguity at the start of projects but it’s also the business analyst’s job to assist with removing ambiguity. Often ambiguity is hidden in project assumptions. Start by capturing an exhaustive list of all of the assumptions you’ve heard, both explicit and implicit, then attack them aggressively by doing what you can to clarify, validate and remove them. Project managers will be particularly happy if these investigations help to provide more clarity on the scope of the project. From the beginning of a project, it’s important to try to gain consensus around what success might look like. That way, when lost in the fog, there is a compass bearing that everyone knows so that they can course correct throughout the project to ensure that everyone is moving in the right direction.
Nations must not look upon AI as a novelty or an economic asset, but also as a central component to national security. Nations depending on Facebook and Google to prop up their IT infrastructure rather than brewing their own national alternatives are akin to nations during the age of empires inviting British gunboats into their harbors. Artificial intelligence, biotechnology, and other forms of emerging technology must be viewed by each nation, state, community, and individual not as a mere novelty or potential industry, but also as a potential means to grant those who develop and monopolize it economic, political, and even military superiority history has taught us they most certainly will abuse.
Capturing all the data you need is great but what you do with it is far more important. Making your data work hard is not restricted to delivering campaigns. You now have the ability to extract that data to feed other elements of marketing; feed behavioural information to or from your mobile app, your email service provider or your corporate CRM in real time. By bringing these different elements together you will be one step closer to a single customer view, the Holy Grail for marketers. It would seem that the future for the marketer will continue to change and at a pace too. The challenge to make sense of the data you capture becomes increasingly difficult on relational databases as volumes and variety increase.
Machine learning can help humans become better managers by removing any biases a manager might have. With machine learning, employee performance is backed up by raw, inarguable data that shows how employees are actually performing. By taking advantage of this rich repository of data, managers can better recognize which employees are achieving important goals. In turn, they can provide appropriate feedback without relying on their personal opinions. With its ability to eliminate bias and prompt a data-driven approach to feedback and recognition from managers, machine learning can completely transform the workplace by making coming to work an engaging experience for every employee — no matter their age, race or gender. Employees shouldn’t have to worry about the personal biases of their managers.
“Test/dev and disaster recovery will be the main components of a company’s environment that will be moved to the cloud, and production continuing to remain on premises,” says Marc Clark, director of cloud strategy and deployment at Teradata. ... Deep learning is getting massive buzz recently. Unfortunately, many people are once again making the mistake of thinking that is a magic, cure-all bullet for all things analytics, according to Bill Franks, chief analytics officer at Teradata. “The fact is that deep learning is amazingly powerful for some areas such as image recognition,” says Franks. “However, that doesn’t mean it can apply everywhere. While deep learning will be in place at a large number of companies in the coming year, the market will start to recognise where it really makes sense and where it does not.”
The culture of the DevOps community seven to ten years ago was very motivated toward open source. Open source tools are almost, by definition, point solutions. I think a lot of the automation solutions, even the commercial automation solutions have been designed to solve a very specific or narrow problem. So there are tools that solve the deployment problem. There are tools that solve the configuration problem. There are tools that solve testing problems. And so on…There is no such thing as a standard DevOps tool chain. They’re like snowflakes. So developers gravitate toward their tool of choice and the DevOps culture encourages experimentation. Enterprises haven’t bought into the giant, does everything kind of tool. Instead enterprises are choosing very specific point solutions and then weaving them altogether to generate efficiencies across the value stream.
One of the more valuable benefits of strengthening enterprise data governance with machine intelligence capabilities is an expeditious efficiency that is otherwise difficult to match. Semantic technologies allow for machine-readable data which can accelerate most processes involving those data, decreasing time spent on data modeling and other facets of data preparation. “The ability for data to be discoverable and linkable through an adoption of identifiers in a consistent way allows that data to move and to be reached more rapidly,” Hodgson said. “Whether you get the data into a machine learning environment is another matter. But at least you’re insured of its integrity, and that’s a big issue as well.”
There’s great value in establishing a liaison and mediator between business and IT team leads. This helps business teams work with IT to maintain information protection, governance, and data quality while also working with business representatives to create value from data assets faster. The governance protocol then moves down the ladder to all aspects of the business where data is involved. Each business unit needs a representative to make sure that their team is up-to-speed on the process for inputting and drawing data and trained with the technology that enables them to do so. Data governance is not just about technology. It’s about key stakeholders and employees creating processes and best practices to properly organize, validate, and derive business value from their own information.
Quote for the day:
"The task of leadership is not to put greatness into humanity, but to elicit it, for the greatness is already there." -- John Buchan