When blockchain technology first reared its head, interoperability was not a subject of much debate; there was only one blockchain and it was all about bitcoin. As time passed, more and more disparate blockchain platforms rose, among them Ethereum, and these various platforms innovated in different directions, creating their own protocols. These protocols make it impossible for the chains to send and receive data from one another for reasons similar to that of why a program designed for Windows OS will not function on Mac OS. ... These protocols are designed to both maximize efficiency between disparate blockchain deployments and allow for a co-existence between them to form, creating a more cohesive ecosystem. There are some great examples of innovation to that end.
Companies from the pre-digital era therefore need to adjust or shift their organizational culture to keep up in today’s digital world. MIT Sloan Management Review and Deloitte recently released their third annual Digital Business report. The report highlighted five key practices of companies developing into "more mature digital organizations." Each of these five key practices focuses on some aspect of organizational culture, a clear indication of the importance culture plays in a company’s ability to adopt new business methods and practices. The question is why is modifying culture so challenging and what guidance can companies follow to increase the likelihood of a successful cultural change?
In the digital era, data is pervasive. For many organizations, the amount of data they collect has become a major problem. Others struggle to identify what data will be most helpful for them to gather. Big data, while revolutionary, has created a glut of information leaving companies trying to figure out how to structure it to generate actionable insights. Business Intelligence is at the core of any kind of long-term business strategy, because it helps make sense of the data. When utilized, data strategy can have a big impact on any operation. When surveyed, 72% of business leaders said that they lacked the tools to effectively manage their data for their existing and future efforts. To meet that need, technology companies are beginning to bring more integrated solutions to market.
We’ve learned how to understand real-time customer queries via NLP and extract value from legacy data using machine learning methodology. The challenge of making use of ongoing customer feedback is bigger, but so are its benefits. This challenge requires joint forces. First, an NLP engine needs to extract sense from a query in natural language. After, machine learning steps in to extract value from this sense. Using classification, intelligent machines assign meaning to data, relying on their background and existing knowledge. In practice, the system classifies certain products, say “books,” by categories, say “popular among women over 65.” For retail, this means more focused recommendation and upselling. Using clustering for new information, in turn, opens totally new horizons.
In short, when it comes to detecting emotion in other people, the face and body do not speak for themselves. Instead, variation is the norm. Your brain may automatically make sense of someone’s movements in context, allowing you to guess what a person is feeling, but you are always guessing, never detecting. Now, I might know my husband well enough to tell when his scowl means he’s puzzling something out versus when I should head for the hills, but that’s because I’ve had years of experience learning what his facial movements mean in different situations. People’s movements in general, however, are tremendously variable. To teach emotional intelligence in a modern fashion, we need to acknowledge this variation and make sure your brain is well-equipped to make sense of it automatically.
The numbers underscore that paying attention to the packages and components that make up a container image is extremely important, especially if the container is from a public repository. Managing the software supply chain requires that companies regularly test their container images for vulnerabilities and vulnerable dependencies. The first lesson is for developers to use container images from sources that they trust, said Anders Wallgren, chief technology officer at Electric Cloud, a software deployment company. "Use images of well-known provenance. If you are going to use Ubuntu, use the published machine instance." In addition, any container image—whether sourced or homegrown—should be frequently tested for vulnerabilities and unwanted software. Luckily, software testing can be easily automated, and should be.
Open Banking is a new set of regulations in the UK that were created to give consumers more control over their money. The Competition and Markets Authority (CMA) issued new rules that would allow consumers to more easily manage their money, switch accounts to find the best deals for their particular needs, as well as avoid high overdraft charges. These new regulations will go into effect in January 2018. As part of the Open Banking regulations, the CMA set a package of remedies to increase innovation and improve competition in retail banking. This includes a requirement for the nine largest current account providers to make available to authorized third parties – customer consent and secure access to specific current accounts in order to read the transaction data and initiate payments.
Data platforms should support applications in context, blending transactional, analytical, search, and graph capabilities. In a financial services context, that might mean taking a credit card transaction, analyzing the customer’s buying patterns and searching for the information to approve the transaction. Data management platforms have to process multiple workloads in a single data platform simultaneously. ... A data platform must provide zero downtime. For example, one DataStax customer kept its recommendation engine running despite a hurricane that took down a whole data center. All of the company’s databases failed except DSE because its architecture was able to retain uptime via data distribution across other data centers.
One area where businesses are finding the most value from AI today is in customer service. Chatbot applications are among the most mature areas of AI. But enterprises are finding that, while AI chatbots can provide value, they have to be deployed the right way. For online test preparation company Magoosh Inc., that means giving machines license to recommend responses to simple customer service queries, while still maintaining a team of agents who handle more complicated issues. Magoosh uses a customer service bot from DigitalGenius to handle incoming customer service inquiries. The system scans messages for their content and recommends prewritten responses that can be personalized or sent out as is.
So there’s a very real and difficult problem in getting a user to get their phone out while they are in the best place to use your app. Notifications could come via traditional push messages, or the user might think to use the app by seeing something in the real world that they want more information on, and they already know your app can help with this. Otherwise, your app just needs to work anywhere, either through using unstructured content, or being able to tap into content that is very, very common. This problem is the No. 1 challenge for all the “AR graffiti” type apps that let people drop notes for others to find. It’s almost impossible for users to be aware that there’s content to find. FYI — this is just another version of the same problem that all the “beacon” hardware companies have, getting the shopper to pull out their phone to discover beneficial content.
Quote for the day:
"As long as you are fighting for what is right instead of who is right, you can never lose!" -- @Rory_Vaden