Beyond software development: the power of low code
The more efficiently the apps are developed and deployed, the better. Sometimes, businesses don’t need a Mercedes when a reliable Volkswagen will do the job just fine. The low code movement is indicative of how development is changing. Platforms including VisionX can be used by different types of users at various levels of expertise. Experienced developers can create apps faster using less time and resources, and without getting sidetracked iterating on problems that are not core to what the application is designed to do. On the other hand, less technical users can build apps in a controlled way via a visual design interface. The result is a repeatable process throughout the organisation to create sound applications faster. The easily replicable templates are at the core of the process, but along the way are powerful tools for greater customisation, advanced data modelling, automated logic, and flexible deployment so that the responsive applications work seamlessly across desktop, mobile, and other form factors.
Humanity at Work: Interview With Rich Sheridan, Author of Chief Joy Officer
In order to truly begin to understand what joy means in the workplace, we need to delve into the sort of things that are less visible, into questions such as: Why do we exist? What do we believe about ourselves? Who do we serve, and what would delight look like for them? Human energy lifts the spirit of your team, and purpose drives it forward. A shared purpose gives us that sense of why we work so hard every day on a particular goal, which in Menlo’s case is to delight those people we intend to serve. Energy and purpose are really at the root of what makes Menlo a joyful place to work — and then of course there are the other things we do as well that add joy to the room. There's no question that laughter, as an example, is part of joy. there is a component of joy that is happiness and while we may not be happy every minute of every day because the work we do can be challenging, I think we do need to carve out space and activities for having fun, being playful, cheerful and supportive.
Most digital transformation efforts do not have visibility. If anything, consumers underestimate the number of businesses that are investing heavily in every customer experience outcome by at least 50 percent. Consumers also are not giving businesses credit for the level of investments they are making. Conversely, businesses are not listening to – or understanding – the needs of customers. Most digital transformation is internally focused -- 68 percent of initiatives are business process centered, while only 28 percent were customer experience centered. A meager handful, four percent, consider their initiatives to be employee experience centered. It appears that a greater outward focus pays off for the business. A majority of customers, 62 percent, would spend more money if their digital experiences "feel effortless." The leading companies in the study "consistently recognize the need for change and they are prepared to take risks," the researchers report. "They have agile, customer-focused organizations that recognize digital transformation is a cultural change, not just a technological change."
Answer These 9 Questions to Determine if Your Data Is Safe
Do you incorporate "privacy and security by design" in your environment? - Privacy and security by design are methodologies based on proactively incorporating privacy and data protection from the very beginning. This approach follows seven principles for implementing growing processes within your IT and business environments. Advocating privacy and security early on in your design process for specific technologies, operations, architectures, and networks will ensure you are building a mature process throughout the design life cycle; Is sensitive data encrypted during transit and at rest? - Encryption keys are vital to the protection of transactions and stored data. Key management should be deployed at a level commensurate with the critical function that those keys serve. I strongly recommend encryption keys be updated on a regular basis and stored separately from the data. Essentially, data is always being pushed and pulled and protecting that information as it moves across boundaries should require strong encryption at rest and while in transit.
Predictive Analytics & Insights Discovery Were Made For Each Other
With the combined power of predictive analytics and prescriptive analytics (derived from insights discovery in big data), we can explore more insightful data-driven “what-if” decision scenarios. This type of “what-if analysis” can foresee the ripple effect of various alternative decisions that adjust different elements in our operations. When we use prescriptive analytics to turn the dials of those elements—either singly or in combination—we can see the implications across the entire landscape. The outputs of prescriptive analytics are particularly helpful in assessing risk, such as when making a strategic decision, or in deciding whether to make cuts or investments. Predictive analytics evaluate the risk of current conditions continuing on their current path, expressing the risk as a mathematical probability. Prescriptive analytics evaluate risk in new scenarios that unfold based upon different decisions, treatments, and options that we might choose to take. This is powerful insight when balancing priorities and considering tradeoffs.
Transformation Without Technology
When legacy companies mistakenly believe that adopting new technology is itself the central strategy for adapting to a digital world, they miss the important organizational and strategic changes that are legitimately essential to survival and prosperity in a dramatically changing environment. Focusing first on the nontechnological changes required for adapting to a digital world — those involving talent, leadership, culture, organization structure, and strategy — helps companies better understand the problems they face and explore other changes that may be necessary before locking into a technological solution. Yes, these same companies may also need to invest — often substantially — in new technology, but when they do, those investments will be focused quite differently than if they had dived headlong into technology as the starting point. In our book, we use the metaphor of the cyclone in The Wizard of Oz to describe the role of technology in digital transformation. We ask, “How much of the story of The Wizard of Oz is about the cyclone?” On the one hand, all of it is.
Kubernetes policy project takes enterprise IT by storm
"Treating compliance as code means adopting best practices from the software development process," Ryan wrote. "One of these is Don't Repeat Yourself. Decoupling policy from applications, and reusing policy definitions in multiple locations, is a good implementation of this rule." As Kubernetes environments grow to encompass Istio service mesh and Knative event-based orchestration in what Google calls the open cloud stack, the fact that OPA lends itself to Kubernetes policy enforcement but can expand to include those adjacent utilities boosts its appeal. "There's a lot of promise in how we might scale and better leverage OPA as the ecosystem grows," said Andy Domeier, senior director of technology operations at SPS Commerce, a Minneapolis-based communications network for supply chain and logistics businesses, which uses OPA in production.
Does Your Company Need That Chatbot?
In using chatbots, you’re relinquishing control of a wide range of customer data to AI. How will you keep it safe? What outer boundaries have you established to ensure it won’t be shared beyond your chat or phone call? What pieces of the chat will you keep, and which will you ditch in the name of data security? Companies like Bank of America have chosen to service customers on their own site or app, where they have complete control of the chatbot experience. Others, like Butterball, have decided to work with established platforms like Alexa, to field customer questions through Alexa’s “skill” platform. Obviously, questions about one’s personal finances need higher security than those about one’s Thanksgiving turkey recipe. Only you know the security requirements that will best fit your customers and industry. Just by the nature of AI taking time to learn what it needs to know, you need to understand that your chatbot won’t work perfectly straight out of the gate. As noted above, it may need to field thousands of inquiries to understand a customer’s intent or desire perfectly. Only you can answer the question: Is it really worth the effort?
Public cloud winners take all
As before, AWS, Microsoft, and Google make it into the Leaders quadrant. And as before, everyone else is largely a rounding error. For example, Alibaba gets credited as the top cloud in China, but Gartner also points out that “Alibaba Cloud’s financial losses are increasing and may prevent the company from continuing to invest in necessary expansions.” Not good. Meanwhile, Oracle keeps selling almost entirely to existing Oracle accounts (who presumably can’t escape), with little hope of expanding: “Oracle is unlikely to ever be viewed by the market as a general-purpose provider of integrated IaaS and PaaS offerings.” IBM gets much the same treatment. Which leaves us with the three leaders. Little has changed in Gartner’s assessment of AWS, Microsoft Azure, and Google Cloud. AWS, unsurprisingly, gets credited as “the most mature, enterprise-ready provider” and, as such, “enterprises make larger annual financial commitments to AWS” than other cloud providers. Microsoft keeps using its enterprise heft to drag Azure into its hitherto on-premises customers, while Google Cloud keeps getting noted for innovative technology
Informatica makes the case: Why data needs AI and vice versa
In a briefing a few weeks back, Informatica made a case for why data quality and data integration has outgrown the ability of humans. It starts with the torrents of data and the nature of the data. When you are tapping the social media or IoT firehose, you are often dealing with ingesting terabytes of data at a time. As multi-structured data, schema is far more complex. So far, this is not a case of ordinary CSV customer or product order files where, even if the schema is not consistent, it may be fairly straightforward to identify what's a name or what's a numerical field such as an order number, SKU, part number, phone number, tax ID, or social security number. Here, data prep tools emerged that used a modest level of machine learning to conduct pattern matching to identify the columns and how the columns of different data sets should be transposed or merged. Instead, the challenge is munging files where the data structure is far more cryptic and variable to the point where humans may not be able to parse it without some machine assist.
Quote for the day:
"What I've really learned over time is that optimism is a very, very important part of leadership." -- Bob Iger
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