Whether it’s IoT, big data or analytics, companies have a lot more data to base their decisions on, and data-driven decision making sounds obvious. And the next step beyond data-driven decisions is decision support systems and even automation. Are we ready for intelligent assistants with business advice? While a recent study of 50,000 American manufacturing organizations found that the use of data-driven decisions had almost tripled between 2005 and 2010, that was still only 30 percent of plants. And when telecom provider Colt surveyed senior IT leaders in Europe in 2015, 71 percent of them said intuition and personal experience works better for making decisions than using data (even though 76 percent of them say their intuition doesn’t always match the data they get).
“There is an urgent need both for the private sector and financial supervisors to collaborate,” the group said in the report, whose contributors include investment bank executives, international economists and entrepreneurs from Asia, the U.K. and the U.S. The forum’s aim is “to foster competition between traditional financial players and new entrants while also preserving system stability,” it said. Fintech was a central theme this year at the group’s annual meeting in Davos, Switzerland, and the report draws on discussions that took place there. It incorporates views of members including executives from UBS Group AG, Deutsche Bank AG and JPMorgan Chase & Co.; tech firms such as IEX Group Inc. and On Deck Capital Inc.; and regulators including the U.S. Securities and Exchange Commission and the Bank of England.
One of the real values of utilizing data is that it can uncover questions or ideas that aren't currently being considered in your organization. A data science team will need specific tasks to accomplish, but they also need a certain degree of autonomy to explore the data and experiment with it. "If you want to build a culture, set them free," Davis said. Change is hard, especially in a large organization with many moving parts. As someone arguing for an analytics culture, you are a change agent, and you have to determine how resistant to, or accepting of, change your organization is. Try asking yourself the following questions:
While that converged infrastructure move flies in the face of the promise of our server-less future, Sangster posits that the value that converged infrastructure delivers -- by taking a group of technologies that can be difficult to use on their own (much less together) and combining them into a prescriptive, pre-integrated solution -- is eternally attractive. Sangster points out that OpenStack has, until recently, been viewed as software for innovators and early adopters. This is the realm of proud DIYers blazing the trail ahead. They love to experiment, doing all the hardware and software engineering possible as they work to understand, implement and eventually deploy a new system like OpenStack. This is, of course, fun for the tinkerers, but unhelpful for the mainstream organizations that simply want to use a solution. For those folks, converged infrastructure makes sense.
The shift from e-commerce to m-commerce happened quite rapidly, too rapidly for many retailers actually. Another new paradigm in 2016 is the move from shopping in mobile browsers to shopping in mobile apps. A combination of well-designed mobile apps with good UI, enhanced smartphone capabilities, push notifications, and new mobile payment tools have led to an explosion in mobile shopping. This also brought a new agenda in sales (retail) strategies for businesses to keep customers engaged and retain to come back. ... Mobile apps play a vital role in mobile commerce growth, but still struggle. 85% of mobile time is spent in apps, which is obviously stunning. On the other hand, most of the app time is solely spent in an individual’s top 3 apps. While mobile web drives double the traffic of apps across industries.
Vendors such as Microsoft, Proofpoint, Cloudmark and Mimecast are building tools to help companies defend against these attack. Mimecast, which makes cloud software designed to spot and quarantine phishing emails with malicious attachments and URLs, has just launched a tool designed to harpoon whaling. Called Impersonation Protect, the software's algorithms analyze the language content of emails as they come in through a corporate server. It looks for key indicators, beginning with whether the source name actually works for the company. The software will then parse the email content for requests that includes keywords and phrases such as "W2" or "wire transfer," and provides a probability score that a target email is either safe or malicious. "One indicator in isolation is not bad, but two together could be fishy," Malone says.
The Issue is Clear: Why Should Anyone Trust Anyone? We could leave this issue to privacy officers, internal and external legal counsel, governments, data protection authorities, politicians, regulators, and technology companies to sort out. We could wait for the ultimate answer to solve the privacy question once and for all. And wait. And wait some more. And wait for another review, debate, newsworthy event (such as needing information from another critical terrorist phone). Or wait for the next cloud service to be hacked, exposing photos that violate an individual’s right to privacy. The reality is we just don’t trust each other—person to person or country to country. The reality is also, we have to trust each other at some level to interact personally or conduct business with each other.
WebDriver has a few different ways to temporarily pause a script in the middle of a run. The easiest, and worst way, is an explicit wait. This is when you tell the script to hang out for some amount of time, maybe 15 seconds. Explicit waits hide real problems. A lot of the time, we see the wait fail and bump the time up a few more seconds in hopes that it will work next time. Eventually we have padded enough time in the script so that the page loads completely before trying to perform the next step. But, how long is too long? These explicit waits can conceal performance problems if we aren’t careful. The smarter way to handle waits is to base them on the specific element you want to use next. WebDriver calls these explicit waits. I have had the most luck in improving stability of a check by stacking explicit waits.
Some refer to this as stateless computing or serverless computing. Personally I prefer the second term, as there is clearly a state somewhere-probably in a database service that the function may leverage— but the function itself is essentially stateless. The same argument could be held against the serverless term, clearly there are servers floating around in the cloudy background but their existence is implicit and automatic rather than explicit and manual. The next area of value in AWS Lambda stems from the ability to easily associate your function with all manner of triggers via both web-based and command line tools. There are more than 20 different triggers that can be used—most of them being from other AWS services such as S3, Kinesis and DynamoDB.
Quote for the day:
"Problems are only opportunities in work clothes." -- Henry Kaiser,