Doug Theis - Leave your comfort zone.

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10
Nov 16

The New Stack – So, You Want to Go Cloud-Native? First, Ask Why

Over the last couple years, the term “cloud-native” has entered the collective consciousness of those designing and building applications and the infrastructure that supports them.

At its heart, cloud-native refers to a software architecture paradigm tailored for the cloud. It calls that applications 1) employ containers as the atomic unit for packaging and deployment, 2) be autonomic, that is centrally orchestrated and dynamically scheduled, and 3) be microservices-oriented, that is be built as loosely-coupled, modular services each running an independent process, most often communicating with one another through HTTP via an API.

Dissecting those characteristics further implies that modern applications need be platform-independent (e.g. decoupled from physical and/or virtual resources to work equally well across cloud and compute substrates), highly elastic, highly available and easily maintainable.

More of The New Stack post from Lenny Pruss

Continuity Central – ISACA looks at the advantages and risks of application containerization

Application containerization is gaining traction given its potential to increase efficiencies and data security options, and decrease cost, according to new expert analyses from ISACA; but it also brings its own risks.

A pair of new ISACA white papers offer insights and guidance on containerization. ‘Understanding the Enterprise Advantages of Application Containerization: An Overview,’ provides a summary of the rising popularity of containers; and ‘Understanding the Enterprise Advantages of Application Containerization: Practitioner Considerations,’ offers practical guidance for assurance, governance and security professionals.

ISACA defines an application container as “a mechanism that is used to isolate applications from each other within the context of a running operating system instance.” Containers let data centers / centres deploy business applications more rapidly. Increased business agility, lower costs and more efficient use of resources are among the other factors sparking increased global adoption.

Digital McKinsey – Leaders and laggards in enterprise cloud infrastructure adoption

Investments in organizational capabilities rather than specific technology choices separate the leaders from the laggards.

There is a lot of hype and hoopla about the cloud but few reliable facts and benchmarks about the adoption of this technology. CIOs, CTOs, and heads of infrastructure at large enterprises have shared with us their frustrations about adopting cloud-based platforms and migrating processing workloads to virtual environments. To address those frustrations, between 2014 and 2016 we surveyed senior business and technology leaders in more than 50 large organizations in Europe and North America to find out about their adoption of cloud and next-generation infrastructure.1 We focused on the structure and management of their cloud programs, the technical capabilities they’ve implemented to this point, the benefits realized, and their future plans.

More of the Digital McKinsey post from Nagendra Bommadevara, James Kaplan, and Irina Starikova

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07
Nov 16

SearchDataCenter – How a transition to the cloud reshapes capacity planning, DR and more

Before transitioning to the cloud, admins often need to address many questions related to everything from SaaS apps to DR and capacity planning. Here are some tips to get started.

Whether you use the cloud for bursting, disaster recovery or a number of other capabilities, a cloud computing deployment affects everything from IT capacity planning to workload management. Here are some tips for data center teams transitioning workloads to the cloud, and considerations for future cloud use.

More of the SearchDataCenter article from Tim Culverhouse

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04
Nov 16

IT Business Edge – Digital Transformation Starts with Infrastructure

Business models around the world are rapidly shifting from selling products to monetizing services. It doesn’t matter what industry you are in, if you are not generating revenue by digitally connecting to consumers, the future of your enterprise is in doubt.

While this digital transformation requires new approaches to organizational structures, workforce skillsets, business processes and customer relationships, it all starts with infrastructure. Static, silo-laden data systems are out; agile, software-defined architectures are in.

But how, exactly, are traditional enterprises supposed to implement such a radical upgrade in time to ward off competition from digitally driven upstarts who are unburdened by legacy infrastructure? To be sure, it will take a concerted effort, and a clearly defined strategy as to how digital transformation can be optimized for the enterprise’s unique market strengths.

More of the IT Business Edge post from Arthur Cole

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01
Nov 16

HBR – What Do People — Not Techies, Not Companies — Think About Artificial Intelligence?

In 1942 the author and professor Isaac Asimov introduced his Three Laws of Robotics, one of the most well-known attempts to establish workable rules integrating artificial intelligence, or AI, into society. Since then, many science fiction writers, philosophers, scientists, and others have grappled with the pros and cons of AI.

This attention has only increased. Just this September, five of the largest tech companies teamed up to create a coalition, the Partnership on Artificial Intelligence to Benefit People and Society, to assure people that AI was not about creating killer robots. And earlier this month, under President Obama’s leadership, the White House issued a report, “Preparing for the Future of Artificial Intelligence,” discussing AI’s possible applications and how it is likely to impact society, for better or worse.

What we have heard less of, however, is what everyday consumers think about AI’s potential and pitfalls, about whether AI will help or hurt the world. We decided to ask.

More of the Harvard Business Review article from Leslie Gaines-Ross

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31
Oct 16

Data Center Knowledge – “Right-Sizing” The Data Center: A Fool’s Errand?

Overprovisioned. Undersubscribed. Those are some of the most common adjectives people apply when speaking about IT architecture or data centers. Both can cause data center operational issues that can result in outages or milder reliability issues for mechanical and electrical infrastructure. The simple solution to this problem is to “right-size your data center.”

Unfortunately, that is easier to say than to actually do. For many, the quest to right-size turns into an exercise akin to a dog chasing its tail. So, we constantly ask ourselves the question: Is right-sizing a fool’s errand? From my perspective, the process of right-sizing is invaluable; the process provides the critical data necessary to build (and sustain) a successful data center strategy.

When it comes to right-sizing, the crux of the issue always comes down to what IT assets are being supported and what applications are required to operate the organization.

More of the Data Center Knowledge article from Tim Kittila

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28
Oct 16

Continuity Central – Maintenance of a business continuity management system: a managerial approach

Practical approach to achieving the difficult task of keeping your business continuity plans up to date.

When a business continuity management system (BCMS) has been established and implemented, a serious managerial challenge evolves: the BCMS has to be maintained and put into a continuous improvement process. In this article, Alberto Alexander. Ph.D, MBCI, looks at the activities that need to be performed to maintain and improve a BCMS.

INTRODUCTION

Any organization that establishes and implements a BCMS needs to follow the BCMS processes and deliverables, which are depicted in figure one. The BCMS processes, also known as the BCMS process life cycle model, (Alexander, 2009), consist of six phases.

The stages of the BCMS process life cycle model are the following:

Stage one: business impact analysis
The business impact analysis (BIA), which is conducted during the first stage, analyzes the financial and operational impact of disruptive events on the business areas and processes of an organization. The financial impact refers to monetary losses such as lost sales, lost funding, and contractual penalties. Operational impact represents non–monetary losses related to business operations, and can include loss of competitive edge, damage to investor confidence, poor customer service, low staff morale, and damage to business reputation.

Baseline – Mobility is at the Center of Digital Business

Mobile 2.0 has arrived, so organizations must develop an enterprise mobile strategy that extends beyond smartphones and tablets and into the IoT.

Only a few years ago, a mobile strategy focused mostly on arming workers with phones and laptops that would allow them to call the office and work remotely from home or while on the road. File sharing was difficult, collaboration was challenging, and staying in sync as an organization was next to impossible.

However, as enterprise mobile technology has advanced and clouds have made data more accessible, organizations are learning that basic communication and collaboration, while vitally important, are only part of the picture.

“As mobile devices have evolved into powerful computers and the definition of mobility has expanded, organizations are recognizing that mobile is now at the center of a successful business strategy,” observes Abhijit Kabra, managing director at Accenture Mobility, part of consulting firm Accenture.

More of the Baseline article from Samuel Greengard

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25
Oct 16

Fast Company – How Unconscious Bias Is Affecting Our Ability To Listen

Sloppy grammar, sounding like you just woke up, ending statements with a slight uptick in pitch, called “uptalk” or “Valley girl speak,” have all been proven to undermine a person’s success.

But how does the listener break down information when both a man and a woman are saying the exact same thing? According to research, the voice itself is the source of unconscious bias for the listener, and women are interpreted differently as a result.

“GENDERED LISTENING”
Meghan Sumner, an associate professor of linguistics at Stanford University, stumbled into the unconscious bias realm after years of investigating how listeners extract information from voices, and how the pieces of information are stored in our memory. Study after study, she found that we all listen differently based on where we’re from and our feelings toward different accents. It’s not a conscious choice, but the result of social biases that form unconscious stereotyping which then influences that way we listen.

More of the Fast Company article from Vivian Giang

Doug Theis Leave your comfort zone.