The data science team embraced the iterative, “fail fast / learn faster” process in testing different combinations of variables and metrics. The data science team tested different data enrichment and transformation techniques and different analytic algorithms with different combinations of the variables and metrics to see which combinations of variables yielded the best results ... The challenge for the data science team is to not settle on the first model that “works.” The data science teams needs to constantly push the envelope and as a result, fail enough in their testing of different combinations of variables to feel personally confident in the results of the final model. After much testing and failing – and testing and failing – and testing and failing, the data science team came up with an “Attrition Score” model that had failed enough times for them to feel confident about its results
“Conversational interfaces” that improve the users’ experiences; “Automated Complex Reasoning,” which permits totally automated decision making; and “Deep Learning,” anticipating more advanced systems for fraud detection, are key cognitive technologies for the development of banking. As well as “risk scoring,” the definition of dynamic clusters of customers, the construction of artificial stress scenarios, and much more. And artificial intelligence is fundamental for the development of natural-language processing, which allows computers to maintain a conversation with human beings. This would enormously accelerate customer digitalization. On the other hand, user convenience calls for much more global and integrated solutions to their needs, and that this will be achieved through platforms combining products and services from different providers.
While the popular view is that few entrepreneurs want to tie up with a large, traditional corporation, the smartest and most ambitious innovators see the benefit of working with a leading financial institution to amplify the reach of their game-changing technology. On the flip side, some might assume that big, more risk-averse financial institutions would shy away from embracing innovation from a startup that thrives on failing fast and cheap. But the truth is banks embrace trends and technology that benefit their customers. And, speed-to-market is the new bank lexicon. That’s why Wells Fargo created a startup accelerator in 2014. Think of it like startup speed dating — we’re providing a framework and structure to nurture relationships with the startup community. The program mentors startups as they work to bring potential breakthrough technologies to financial services and other sectors.
"Some people literally want to lift and shift," said Bryant. That is often a motivation for moving into the cloud, as organizations want to strip themselves of the burden of maintaining large datacenters, or even simply avoid spending millions of dollars upgrading legacy servers. Or perhaps the goal is to simplify the maintenance and enhancement of existing SOA applications, a motivation that commonly drives the adoption of microservices, and often the adoption of container-based architectures that use technologies like Docker and Kubernetes. Understanding the underlying goals that are motivating the move to cloud computing or containerized microservices is important, but equally important is being able to objectively know if the goals have been achieved when the migration is done.
Delivering on this IoT vision demands that CIOs, CDOs and CTOs catalyze a fundamental change in how their organizations develop applications. The IoT-driven applications that truly transform business are expected to be those that are developed with speedy, agile, team-based practices. And, in turn, those practices require that your IoT app development teams—which should include business analysts, data engineers, data scientists and subject matter specialists—share a common, cloud-based collaborative platform. This development environment should be built on a high-performance data lake and span a hybrid architecture that’s equally capable of handling structured and unstructured data. It needs to also support agile building, testing, refinement and deployment of analytics algorithms into myriad IoT deployment roles, both at the edges and in the cloud.
EA has to select, together with the business, the SaaS, FaaS, IaaS, PaaS, iPaaS, and business service solutions that integrate best and minimise unnecessary diversity by standardising on certain clouds and services. It also has to also align the information formats at the interface level because each outsourced component may have its own format. Yet, note though that the technology behind the IT cloud services is not really visible to the Enterprise and relevant to its architecture and as such IT needs not be documented in detail. But, while the IT decisions remain in the jurisdiction of each company, because companies in the value chain remain still autonomous, the virtual cloud enterprise Governance function, may still aim to coordinate with long term partners the harmonisation of information formats and cloud approaches in order to reduce unnecessary variation of cloud suppliers standards and technology to obtain overall economies of scale, minimise duplication and integration issues and align information format.
While this article isn't about the Echo, I bring it up because while I was waiting for my unit to arrive, I was reading everything I could find about it on the Web. As I did, my interest in digital assistants was reinvigorated and I delved back into investigating Cortana on my Windows 10 system. I had played around with it a couple of times in the past, but since I have an iPhone, I use Siri for directions, weather, reminders, music, and impromptu internet searches. I never really found it compelling to use Cortana for those types of things while sitting at my desk. However, I decided to give Cortana a second chance and found that she does a nice job of providing me with the same types of features that I've grown accustomed to with Siri on my iPhone. Now, I haven't yet attempted to add Cortana to my iPhone, but I just might do that sometime.
Organizations can deliver apps faster and with higher quality by following by following an agile framework, but they also need to leverage DevOps tools that automate the process of moving code from Development to Operations. Sticking with our CNN Politics app example, one sprint would include developers writing APIs, application programming interfaces that are the building blocks of digital transformation, to request CNN polling data. Developers use a fast, distributed source control system such as Git, and synchronize local filesith a remote repository such as GitHub. The API code is checked into GitHub, which continuously integrates the code with a DevOps tool such as Jenkins, which automates software builds and may orchestrate with other tools to test and deploy code to an application server running in a production
Perhaps DeepMind’s most famous accomplishment so far is being the brains behind AlphaGo, the first computer program to beat a professional human player of the board game Go. AlphaGo was developed by feeding DeepMind’s machine learning algorithms with 30 million moves from historical tournament data, and then having it play against itself and learn from each defeat or victory. DeepMind’s work is based on a solid grounding in neuroscience. Two of the founders – Demis Hassabis and Shane Leg - met while undertaking research at the UCL’s computational neuroscience unit, and Hassabis has a PhD in the subject. This has underpinned their strategy of developing AI by teaching computers to mimic the thought processes of our own brains, in particular how we use information to make decisions and learn from our mistakes.
At a certain point of being agile, the traditional methods for achieving the next state fail because they are not based on self-organization and don’t see the organization as a system but as a hierarchy. The methods that were useful on the previous two levels of the #ScrumMasterWay model, such as organizing workshops, explaining, bringing in new concepts, and coaching at the team level are failing as the organization is already too complex. You would have to experiment, be playful and curious, and try different things to stimulate reactions. The system will give you some feedback, and all you have to do is to believe that every system is naturally creative and intelligent, so the people in that system don’t need you to tell them what to do. They will find out. However, they might not see it in the first instance, so they need you as a coach to challenge their status quo and reveal to them what you have seen from your different viewpoint.
Quote for the day:
"There is no monument dedicated to the memory of a committee." -- Lester J. Pourciau?