From Uber to Buzzfeed, Spotify and Netflix, we’ve been finding the new paradigms that best fit digital capabilities to replace and disrupt the digitised analogue products and services that came before. Banking is no exception to this rule. We’ve moved from a passbook to a printed statement that was first mailed to us, then put on a PC screen and eventually shrunk to your phone. Digitised banking – tick! Truly digital banking – we haven’t seen it yet! When I co-founded Monzo, a new digital challenger bank in the UK, that was the challenge that faced me. As Chief Customer Officer I led product and proposition, and I knew that I didn’t want to just digitise banking, I wanted to find the new paradigm, an approach that delivered digital banking rather than just digitising what had come before.
Above the hospitality and food sector on the lower end are manufacturing (31%); admin and real estate (28%); construction (23%); transport and storage (23%); and entertainment and service (21%). This is despite the fact that cyber security has featured increasingly in mainstream media because of several high-profile data breaches and the fact that millions of UK firms are being hit by cyber attacks. According to research by business internet service provider Beaming, 2.9 million UK firms suffered cyber security breaches in 2016, costing them an estimated total of £29.1bn. According to security professionals consulted by networking hardware company Cisco, the operations of an organisation (36%) are most likely to be affected by any potential cyber attack.
A data fabric must support the modernization of storage and data management, and move away from the proliferation of data silos. But a data fabric must also integrate with legacy systems, without requiring their presence for the long-term. To work effectively a data fabric must be broad and support a vast array of applications and data types at scale across locations. While data fabrics are a significant change from the assumptions that usually surround data storage and processing, the requirements have their roots in big data. The big data era was driven by the three V’s – Volume, Variety and Velocity. A data fabric does encompass these requirements but goes well beyond. In fact, an interesting way to summarize the requirements of a data fabric is the seven V’s – Volume, Variety, Velocity, Veracity, Vicinity, Visibility, and Value.
Pattern languages are an ideal vehicle for describing and understanding Deep Learning. One would like to believe the Deep Learning has a solid fundamental foundation based on advanced mathematics. Most academic research papers will conjure up high-falutin math such as path integrals, tensors, hilbert spaces, measure theory etc. but don't let the math distract oneself from the reality that our collective understanding remains minimal. Mathematics you see has its inherent limitations. Physical scientists have known this for centuries. We formulate theories in such a way that the expressions are mathematically convenient. Mathematically convenience means that the math expressions we work with can be conveniently manipulated into other expressions.
Start with absolute buy-in from all the teams involved and a good architectural footprint. Embrace the minimum viable product, and build on it. For example, if you build servers manually, develop processes to create and deploy a golden image in your virtualization platform of choice. The next step: Implement a secure and compliant base image across all Windows systems and another across all Linux systems, then generalize the application stack. Entrenched organizations can have 50,000 servers with as many different configurations, he pointed out, so iterative platform changes must happen before DevOps processes can translate to, "I can push a button and get my application stack." "Ultimately, you want to get [to end-to-end automation], but don't go in expecting it," said Herz. "Do a little bit, make that little bit better and move up the stack."
Cognitive resources, algorithms, and learning models are now widely available through APIs and cloud services. IBM, which has been in the AI game since the ’50s, leans on Watson and Bluemix, but Amazon, Google, and Microsoft are also heavily invested and have strong offerings. And it’s not a game for traditional big tech only. Marketing platforms like Salesforce and Adobe are also starting to offer AI as platform, and a vast number of specialized startups are popping up. OpenAI and related nonprofit initiatives also offer resources and training tools. There’s not going to be one right answer for all. Like a web services stack, your cognitive stack will be guided by your needs, learning models that fit the job, expertise of your scientists and technologists, data sources and formats, and integration needs.
Many organizations have already built extensive analytics capabilities, typically housed in centers of excellence with some combination of data-science, statistical, systems-knowledge, and coding expertise. Such COEs often provide fresh insights into talent performance, but companies still complain that analytics teams are simple reporting groups—and even more often that they fail to turn their results into lasting value. What’s missing, as a majority of North American CEOs indicated in a recent poll,1is the ability to embed data analytics into day-to-day HR processes consistently and to use their predictive power to drive better decision making. In today’s typical HR organization, most talent functions either implicitly or explicitly follow a process map; some steps are completed by business partners or generalists, others by HR shared services, and still others by COE specialists.
First, HR needs to truly understand the flexible work environment. Employees of the future are no longer bound to stay in the office, sit in a cubicle and work from 9 to 5. Rather, they will become location-independent and will be able to work when and where they want as long as they can get access to WiFi and manage to get the jobs done. This is because internet and mobile devices have transformed the way people work, interact and collaborate. Next, the use of new methods to communicate and collaborate must be promoted immediately. Email will no longer be considered the most effective or efficient way to communicate or collaborate. Instead, technologies such as internal collaboration platforms are going to replace email in many situations.
Microsoft’s model for manual memory management builds on the notion of unique owners of manual objects, with locations in the stack or heap holding only the reference to an object allocated on the manual heap. The concept of shields is introduced to enable safe concurrent sharing of manual objects. The shield creates state in local thread storage to prevent de-allocation while the object is being used. While garbage collectors such as the .Net GC offer high throughput through fast thread-local bump allocation and collection of young objects, studies show that GC can introduce performance overhead compared to manual memory management, the researchers said. These overheads are amplified in big data analytics and real-time stream processing applications, partly due to the need to trace through large heaps, they explained.
“There will always be a need for people to read, interpret and understand what customer needs are and how the brand should react,” he says. Nissan North America has six to eight analysts that review data aggregated in queues by the social media management tool Sprinklr, which monitors corporate Twitter handles, Facebook pages, Instagram and Google Plus. The analysts, Long says, are the ones who decide when to respond. “An individual instance of concern might not be enough to warrant a look, but when you get into a top 10 or top 20 ranked concern, you have to start paying attention,” he says. Long considers social intelligence a very important data point that, when coupled with satisfaction surveys and other customer feedback, can help inform and shape the organization’s products, advertising and customer experience.
Quote for the day:
"The quality of a leader is reflected in the standards they set for themselves." -- Ray Kroc