Daily Tech Digest - July 30, 2017

How CIOs can use machine learning to transforming customer service

Machine learning means your company’s programs can make use of this data without being explicitly programmed to do so, as programs collect, learn, and adapt by utilizing ever-greater sources of data. Your human employees who do relatively simple and often mundane task, such as answering phones, will soon be replaced by much more efficient machines.  Workers who remain employed will find themselves working alongside of greater numbers of machines, too. Employees will find it much easier to comb through databases of information, retrieve specific solutions to costumer issues, and detect what kind of customer they’re on the line with by utilizing advanced software. Machine learning also enhances your company’s knowledge-retention capacity in the long run.

The New Enterprise Business Integration Approach In Banking

Some ‘trendsetter’ banks after having established maturity in mobile banking, have now begun to focus on the Omni-channel strategy. Omni(s) (Latin for “Universal”) – Channel is a digital transformation paradigm to provide common customer experience in multiple channels through which a customer interacts with the bank. Omni-channel implementation path often starts with the convergence of customer facing capabilities in Online, Tablet, Mobile and new disruptive channels such as Wearables; while also developing a transition roadmap to further integrate capabilities such as alerts, notifications, 3600 customer view to other self-service channels such as IVR (Interactive Voice Response), Kiosk, ATM; and assisted channels.

Preparing For Disruption: Fintech And The Fortune 500

Startups usually concentrate on one area of the financial business and do it well, whereas most Fortune 500 financial companies have diverse lines of business. As a result, large financial companies are fending off assaults on their bottom line from multiple fronts. For instance, PayPal, Stripe and Square are honing in on payment processing on one side, while robo-advisors like Betterment and Wealthsimple are looking to take over a chunk of the wealth management sector. And it doesn't stop there. The latest fintech players have ventured into the lending industry where companies like LendingClub and SoFi attack the consumer lending market, and Kabbage and OnDeck Capital look to become leaders in small business lending – a traditionally under-served market due to the high cost of processing loans.

The battle between banks and disruptors is just beginning

There are companies that do similar things in lending, savings, investments and other specific areas of financial services based upon internet technologies. These companies have names like Zopa, SmartyPig, Nutmeg and eToro, and have fun branding and cool offices. They are very different from banks. They all share many of the same attributes, in terms of being young, aspirational, visionary and capable. This is why, collectively, they have seen investments from venture capital and other funds averaging $25 billion for the last four years, according to figures published by KPMG. However, there is a possible impasse here. The most successful fintech firms are not replacing banks, or at least not yet. They are serving markets that were underserved. But none of them have replaced a bank.

SmartTechnologies to SmartLiving

Closed Ecosystem IoT relates to a fully integrated system of several types of network including machine to machine M2M, machine to human M2H and machine to data system M2D through an application gateway. Additionally these networks provide pre-connected and situational relationships dependent upon tasks, locations and users. An example would be a Home Ecosystem, again as this is the most likely location to get investment at this point in human society. All possible actions and interactions within a home, including disallow rules, policies related to sensors and personal ecosystems are defined and can be added to by users with the correct rights. Every sensor device, task and activity, item can be included in the ecosystem. Personal ecosystems and an Adoption / Attribution Ecosystem

How to stop stakeholders from sabotaging projects

"It is always best to have the stakeholders be included in critical meetings. Here they can voice their concerns early and request needed information. This also allows everyone to agree on a timeline for when critical decisions need to be made," said Lane. To address any form of stakeholder sabotage, Brzychcy recommended that leaders "maintain a holistic approach to projects and carefully game out several courses of action for any undertaking." ... Brzychcy also said that project managers need to be aware of what they don't know and spend the time filling in any knowledge gaps. Continual communication is an easy way to keep stakeholders emotionally invested and interested, said Nolan. There may be times when despite all efforts, a stakeholder remains disinterested in the project. "It may become necessary, ask for a new lead or point of contact on the stakeholder's team; this can re-energize the project and keep things moving forward," he said.

AML - A Paradigm Shift That Accelerates Data Scientist Productivity

There is a growing community around creating tools that automate the tasks outlined above, as well as other tasks that are part of the machine learning workflow. The paradigm that encapsulates this idea is often referred to as automated machine learning, which I will abbreviate as “AML” for the rest of this post. There is no universally agreed upon scope of AML, however the folks who routinely organize the AML workshop at the annual ICML conference define a reasonable scope on their website, which includes automating all of the repetitive tasks defined above. The scope of AML is ambitious, however, is it really effective? The answer is it depends on how you use it. Our view is that it is difficult to perform wholesale replacement of a data scientist with an AML framework, because most machine learning problems require domain knowledge and human judgement to set up correctly.

Artificial Intelligence Can Be a Catalyst Across Most Cycles of the IoT

Overall statistics aside, individual enterprises have their own stories about data growth, and how they intend to handle it. Surely, this is what cloud computing in all its forms is all about. But simply processing it, transmitting it, and storing it is not enough. Data is not simply water or electricity. It’s a core asset of any company. Well, the third wave of artificial intelligence (AI) upon us. A survey by Pega investigated what organizations do think about AI. The capabilities that seem attractive include speech recognition, replication of human interaction, problem solving, etc. The capabilities to actively developing within AI mentioned were game playing, running surveillance, automating manufacturing, etc. What’s the urgency? We’ve been hearing about the IoT for several years now, with focuses on making sense (if possible) of the protocols involved, the security of data, and how to handle it in its many varieties, velocities, and volumes.

On business-architecture and enterprise-architecture

The key problem here is that what most people call ‘enterprise-architecture’ is actually a contraction of an older and more accurate term: ‘enterprise-wide IT-architecture’ [EWITA]. Which no doubt seems fair enough at first – after all, ‘enterprise-architecture’ is a useful shorthand for EWITA. The catch is that that contraction becomes dangerously misleading when we move beyond an IT-only domain, and outward towards the enterprise itself. ... The point here, that way too many people still miss, is that we cannot run a BDAT-stack backwards: it is always base-relative. The mistake that is made time and again by users of TOGAF et al. is that they assume we can start anywhere in the stack – but if we do that from anywhere other than the base, the result gives us a scope that can be dangerously incomplete.

INDEA: Catalysing One Nation One Govt In India with EA

The vision of IndEA is “to establish best-in-class architectural governance, processes and practices with optimal utilisation of ICT infrastructure and applications to offer ONE Government experience to the citizens and businesses through cashless, paperless and faceless services enabled by Boundaryless Information Flow™.” The IndEA comprises of eight distinct yet inter-related reference models, each covering a unique and critical architecture view or perspective ... Integration of government business processes and services across the breadth of the enterprise is needed for delivering the services conveniently to the citizens on a sustainable basis. Data interchange in an e-Government setup is a primary need.

Quote for the day:

"It takes courage to believe that the best is yet to come." -- Robin Roberts

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