Monthly Archives: March 2017

Enterprise Mobility, key to your business strategy.

Tony Storr, an Architect and Leader, IBM Mobile at Scale, writing in Mobile Business Insights talks about the necessary factors a C-suite  executive should focus on if enterprise mobility has to play a key role in their business strategy.

Tony Storr starts off how enterprises in their anxiety to get on the mobility bandwagon encouraged various business units to follow their own roadmap and even though it felt empowering at that time led to some serious issues over time. These issues like varying standards, system fragmentation, security implications and provider volatility resulted in uneven user experience. But, the real business issue was when it was time for digital transformation, these issues being key factors effecting pace of change, posed serious challenges, especially as speed to market was crucial. This scenario is widespread irrespective of the industry and geography.

The challenge for the enterprises is to consolidate and industrialize the mobile applications without encroaching upon the innovation aspect brought in by the business unit. This entails much more than having a single vendor or internal team churning out applications in a consistent manner. Enterprises embarking upon consolidation should not forget that consolidation need to happen holistically and continuously across portfolios of apps and also across mobile app services (design, develop, maintain, support and monitor).

The key characteristics of effective mobile services are, continuous operation, increasing assets and accelerators, increasing productivity, cross app design and architecture, integrated support functions and innovative tooling and techniques.

In conclusion, Tony Storr writes that consolidation and refactoring of all existing applications may not be advisable and suggests this may start with the next generation of employee apps and enterprise mobility may well take off from there.

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How machines learn?

Writing in Datanami, a news portal dedicated to Big Data news, insights and analysis, Fiona McNeill, a SAS global marketer and Dr. Hui Li,  senior staff scientist for SAS, shares light on how machines learn. Starting with examples of various enterprises using machine learning to design personalized offerings to attract customers, they then raise the important point of different vendors jumping on the bandwagon of machine learning with their own approaches and solutions making the whole thing confounding to the user. And thru’ this article in Datanami, the duo from SAS try to unravel machine learning and make it easier for users to understand how exactly machine learning works.

Machine learning models are designed to learn how to perform tasks and with algorithms designed to see relationships and patterns between various factors, these models learn continuously from data.  And to generalize this model for business they are then validated on whole set of new data not used initially for training. These models may be made to learn in different ways like supervised learning, semi-supervised, unsupervised and reinforcement learning.

Machine learning is at the center of many advanced intelligent solutions emerging now, like AI, Neural networks, Natural Language Process and Cognitive Computing.

  • Artificial Intelligence – A discipline enabling the design of machine with problem solving skills to accomplish tasks just as human beings can.
  • Neural Networks and Deep Learning – Neural networks are programs written to learn from observational data and present solution to the problem on hand. These are used in speech and image recognition and are very successful in supervised learning.
  • Natural Language Processing and Cognitive Computing – NLPs are interfaces which enable machines to understand human language and humans to interpret machine output. These are applied in image captioning, text generation and machine translation.

The confluence of Big Data and massive parallel computational environments are driving the machine learning initiatives and the goal is to deliver solutions that can be highly customizable and with human – like cognition features.

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2016, Big Year for Big Data

Writing recently in insideBigData, Linda Gimmeson, a technical writer focusing on Big Data, machine learning and IoT, takes a look back on the year 2016, and tells how Big Data contributed technologically and socially, she then sets out to make a list of six disciplines which has benefited by the application of Big Data.

AI advancement – Big Data is advancing the speed and capacity of Artificial Intelligence and taking it to the next level, for example  Google DeepMind AI beating humans in the game of Go, and becomes unbeatable as the game progresses due to AI and the Big Data applied to its functioning.

Tax Shelters Unveiled – Investigative journalists collaborating across continents and using cloud based data analytics and Big Data were able to pursue effectively and unveil tax shelters now famously known as “Panama papers”.  This is one of the first known instance of the real-world good, Big Data can contribute to bring about.

Human Trafficking – Big Data is lending its helping hand to “Polaris Project” in its fight against human trafficking, even though Polaris project has made tremendous progress over the years  in their fight, Big Data became their strongest tool in the year 2016 to decipher the complex numbers and patterns to give useful insights and help victims of this  horrific crime.

Cancer Research – Intel’s Bryce Olson , himself a cancer survivor lead, Trusted Analytics platform is “ a collection of Big Data tools and data analytics, to help in breaking down of DNA – the complex code of human genetics to give insights into where cancer begins and how it can be controlled”.

HIV Outbreaks – When Centre of Disease Control were struggling to contain the HIV outbreak in 2016, which was taking extensive death toll and seriously damaging the health of survivors, they turned to Big Data for insights to fight the outbreak.

In 2016 we were witness to the fact that Big data in addition to its applications in optimizing business efficiency and effectiveness can also be deployed to harness real- world good, and we are sure to see more such deployments in the coming years.

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