Daily Tech Digest - April 09, 2017

Why more tech workers should take sabbaticals

Taking a break from work, like a long weekend, is one way that employees and their managers can use to counteract burnout. Another tactic is to move an employee to a less stressful assignment, or to transfer an employee to a new area of work where he/she can get away from older pressures and start fresh. Still another tactic is to develop staff "bench strength" so that project stresses don't continuously fall on the same group of people. Finally, it helps to have fun and relaxation at work! An occasional pizza party, an onsite exercise workout room, or even a quiet sanctuary where employees can meditate or relax their minds, all contribute. Five years ago, only 4% of American companies offered sabbaticals, and the reality is, many smaller and mid-sized companies simply don't have the bench strength to offer them.


Bank consortium demonstrates leveraged loan trade via blockchain

Long said the goal of the test was to prove not just that a trade can be done over blockchain, but that it is well worth the investment in terms of time and cost savings for syndicated loan buyers and sellers. The demonstration, which involved a typical roster of syndicated loan trade participants, showed that a trade could potentially be settled in a few days, at much lower cost. Other efforts are underway to speed up trade settlement. The LSTA recently introduced rules aimed at discouraging buyers from dragging their feet in bringing their money to the table. As a result, median settlement times have been reduced from 16 days to 11 days since 2013. But banks are still aiming for loan-trade settlements in under three days, Long said. “We were looking to automate processing and remove all duplication and we did succeed at that,” Long said.


Why a combination of agile and DevOps is essential in propelling digital transformation

New research commissioned by CA Technologies shows that 67 per cent of UK organisations using an agile methodology experience an improvement in customer experience.  It also highlights how DevOps and agile are better together than apart: Organisations that add DevOps practices to an agile environment improve new business growth by 38 per cent more than using agile alone. Agile and DevOps together also increase operational efficiency by 23 per cent, compared to using agile alone.  However, organisations need to do more than simply launch both in unison and assume great customer experiences will emerge. They need to mature their agile and DevOps deployments as quickly as possible, because that’s where the greatest payback lies.


How CIOs can drive change by setting a vision

“We lead people,” he continues, “not projects.”Shurts forged that perspective through, among other experiences, a series of challenging and complicated CIO assignments, all of which involved organizations in massive need of change, and in massive need of decisive leadership. ... The mission mattered, not just for motivational purposes, but also because it was true and transparent. And sharing ownership of that mission was sorely needed to get past the reticence of many IT staffers. “Otherwise, to some degree, we were a bunch of professionals coming into the office just to do something,” Shurts says. To a high degree, it worked. After Shurts invited his boss, the division president, to a town hall about the project, the executive told his colleagues (as Shurts recalls) that Shurts’ team ‘really believes they’re working on the most important thing for this company.’ Shurts thought to himself, “Damn it, Rick — you should, too!”


The Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research

A contemporary example of such “unreasonable effectiveness” is the success that machine learning has had in transforming many disciplines in the past decade. Particularly impressive is the progress in autonomous vehicles. In the 2004 DARPA Grand Challenge for autonomous vehicles, which popularized the idea of driverless cars, none of the vehicles was able to complete a relatively simple route through the Mojave Desert, and I thought it unlikely that I would see driverless cars operating in urban environments in my lifetime. Since that time, progress in this area has been phenomenal, thanks to rapid advances in using machine learning for sensing and navigation. Driverless long-haul trucks are apparently just a few years away, and the main worry now is not so much the safety of these trucks but the specter of unemployment facing millions of people currently employed as truck drivers.


Artificial intelligence (AI) and cognitive computing: what, why and where

Artificial intelligence is being used faster in many technological and societal areas although there is quite some hype about what “it” can do from vendors. Still, the increasing attention and adoption of forms of AI in specific areas triggers debates about how far we want it to go in the future. Prominent technology leaders have warned about the danger and think tanks and associations have been set up to think about and watch over the long-term impact of AI (and robotics) with dicussions on the future of humanity and the impact of superintelligence but also, closer to today’s concerns, impact of automation/AI/robots on employment. Anyway, it again adds to that mix of ingredients that creates the conditions to strengthen the negative connotation regarding the term artificial intelligence


So You Want to Be a Data Scientist? – It’s Complicated

Anyone who is considering a career in data science needs to understand first, the myriad of things such a career involves, the type of education and training required, and exactly what the job market holds. And because the field is growing so fast, students and mid-career professionals both have an opportunity to move into data science careers, if they get the right education and training. ... There is no single definition of data science, as it varies with industry, specific business, and what the purpose of the data scientist’s role is. And different roles require different skill sets, therefore the educational and training path is not uniform. Data scientists can come from many fields – math, statistics, computer science, and even engineering.


Technology has forever changed our creative thinking. Here's how to take it back

When you walk around these days, count how many people are looking down at their phones. Almost everyone! Surprising, right? It’s sad how frequently screens have substituted the need for others in our lives. The same is true when working on projects. Having a person around IRL is more valuable than shooting an email over or setting up a call to ask for feedback. When creating with others, you’re able to share your ideas and creations at the moment they’re being made. While you chat things through, new ideas could even come to light based on the discussions that you’re having with real time feedback. In addition, behind a screen, you don’t get to see the actual project you’re working with nor have the luxury to read the body language behind the other individual to see their thoughts and feelings.


The Synthesis Of Enterprise Architecture And Design Thinking

To be human-centred is to focus on people and outcomes. While traditionally Enterprise Architecture has arguably been pre-occupied with outputs (i.e., various domain specific models or views of the business) a human-centred approach demands a shift in focus to the outcomes that a design process delivers (including the experience of the design process itself as an outcome). This dynamic has seen us re-think the TOGAF ADM as a series of design activities that each require a meshing of both Enterprise Architecture and Design Thinking to deliver not only the blueprints and plans needed to guide change, but also carefully crafted experiences that change individuals, organisational culture and create opportunity for insight.


The relationship between enterprise architecture artefacts

Considerations (principles, policies, maxims, etc.) are global conceptual rules and fundamental considerations important for business and relevant for IT. Standards (technology reference models, guidelines, reference architectures, etc.) are global technical rules, standards, patterns and best practices relevant for IT systems. Visions (business capability models, roadmaps, future state architectures, etc.) are high-level conceptual descriptions of an organization from the business perspective. Landscapes are high-level technical descriptions of the organisational IT landscape. Outlines (solution overviews, conceptual architectures, options papers, etc.) are high-level descriptions of specific IT initiatives understandable to business leaders. Designs are detailed technical descriptions of specific IT projects actionable for project teams.



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