50 per cent of corporate data is stored in branch offices, creating an insecure and complex network of distributed servers and storage. WIMPIE VAN RENSBURG explains how Riverbed’s Zero Branch IT support helps companies consolidate their data.
The lifeblood of many companies today depends on branch offices. Whether these are remote sites, retail outlets or manufacturing plants, they must be agile and able to quickly respond to the business’s ever-changing needs. But too often, branch offices operate as independent data centres which are difficult to support and protect. Consequently, services outages and data loss are a common occurrence, leading to productivity issues including missed sales opportunities, customer churn, assembly-line stoppage and ultimately, lost revenues.
How can businesses efficiently address their branch office needs? The solution is to take a completely new approach to branch office IT which will improve system performance and resiliency, ensure reliable data backups, and greatly reduce operating expenses, particularly as more companies adopt a hybrid enterprise IT infrastructure that combines on-premises and cloud or SaaS-based applications and services. By implementing a “Zero Branch IT” model, businesses will no longer install new equipment and assign additional on-site support at each location. Instead, they will centralise data without compromising performance, while enabling instant provisioning of new applications and services at remove locations and branches, as well as making instant recovery of applications and services a reality.
The challenges of outdated branch IT
A recent Riverbed report found that that 50 per cent of corporate data is stored in branch offices and that branch offices represent 50 per cent of an average company’s total IT budget. This creates an insecure and complex network of distributed servers and storage deployed solely to meet local performance and reliability needs.
In other words, half of today’s IT organisations are using outdated methods of operation, forcing branches to subsist on decentralised, ad hoc, and rigid legacy infrastructures. In addition to being costly and complex to manage, outdated infrastructures limit IT’s ability to proactively respond to businesses’ ever-changing needs, prevent security breaches, and recover from unplanned outages.
How Zero Branch IT supports wider business goals
Though CIOs are expected to play a central role in driving business objectives, few organisations take into account the IT challenges involved in rolling out new services across all branch locations, such as WAN constrictions, security concerns, and minimal staff. Disorganised, legacy branch infrastructures are costly to both the business and to the IT department, making even the smallest propositions a worrisome task- and making it harder for the CIO to support the business.
As an alternative, CIOs can implement a Zero Branch IT model which will address the needs of the IT department as well as those of the organisation as a whole. To better understand this new model, IT can imagine the branch as a smartphone – a simple device, fully equipped with applications and high-speed access to data over the cellular network or the Internet. When buying a smartphone, mobile providers just provide the device itself, not a rucksack full of application servers, storage, and backup infrastructure users must also carry and maintain.
Branch offices and other remote sites can operate in a similar way. Taking a new approach to branch IT enables the CIO to manage everything inside a secure, central data centre and deliver performance out to the branches. The result will be an almost non-existent IT infrastructure footprint with no remote servers, storage racks or backup and recovery systems.
The benefits of optimising the network
Gartner describes the average WAN optimisation system as a deployment of appliances at the central data centre and in each branch office, though an additional option is to deploy appliances as virtual machines or as a cloud resident service. For mobile or remote users, WAN optimisation can be deployed as a soft client that runs on individual user devices.
As wide area networks (WANs) are notoriously unreliable and do not offer protection against the creation of localised pockets of systems and information stores, Zero Branch IT requires tools that enable the convergence of IT systems and applications with WAN optimisation technologies. This will offer local LAN performance, bringing data back to the data centre, while maintaining application performance at all branch offices.
The most recent Gartner Magic Quadrant for WAN Optimisation conveys that WAN optimisation technologies can provide a range of features that improve application performance running across the entire WAN, and reduce the overall cost of the WAN. Gartner describes the typical WAN optimisation setup as a deployment of appliances at the central data centre and in each branch office. Another option is to deploy the appliances as virtual machines or as a cloud resident service. For mobile or remote users, WAN optimisation can be deployed as a soft client that runs on individual user devices.
Businesses can cut their branch IT costs by eliminating the need to purchase, maintain and protect servers, storage and backup systems in branch offices. Additionally, by using a centralised infrastructure managed via a single console or dashboard, IT can achieve greater visibility and control over the network, so as to quickly and easily redeploy, upgrade, move, or migrate systems, applications and services to accommodate the opening of new branch offices.
Using new technologies, IT can store and protect sensitive data in a centralised, strictly controlled location, with stringent backup and replication policies. Using specialised applications, businesses can easily access that information in an agile way. They can therefore deploy new services, applications, or entirely new branch sites while ensuring maximum productivity of branch staff.
New tools also enable real-time continuous data capture and analysis so that companies can view network delays, providing speed, insight and control no matter where data is stored. As a result, businesses will experience far fewer instances of system outages, slow application performance and downtime, making it easier for employees to work anyplace, anytime, using an ever-increasing selection of work-issued and personal computing devices, including laptops, smartphones and tablets.
By taking a new approach to branch IT, businesses can bring disparate systems and applications to the data centre manned by the full-time IT team, driving tangible economic benefits, including efficiencies of scale, improved employee productivity and the ability for all remote offices to share expensive backend solutions. A Zero Branch IT approach enables today’s organisations to leverage IT strategies such as branch converged infrastructure, storage delivery, virtualisation and WAN optimisation to address the unique needs of branch offices, all while delivering better business performance overall.
* Wimpie van Rensburg, Country Manager of Sub Saharan Africa at Riverbed Technology.
What’s left after the machines take over?
KIERAN FROST, research manager for software in sub-Saharan Africa for International Data Corporation, assesses AI’s impact on the workforce.
One of the questions that we at the International Data Corporation are asked is what impact technologies like Artificial Intelligence (AI) will have on jobs. Where are there likely to be job opportunities in the future? Which jobs (or job functions) are most ripe for automation? What sectors are likely to be impacted first? The problem with these questions is that they misunderstand the size of the barriers in the way of system-wide automation: the question isn’t only about what’s technically feasible. It’s just as much a question of what’s legally, ethically, financially and politically possible.
That said, there are some guidelines that can be put in place. An obvious career path exists in being on the ‘other side of the code’, as it were – being the one who writes the code, who trains the machine, who cleans the data. But no serious commentator can leave the discussion there – too many people are simply not able to or have the desire to code. Put another way: where do the legal, financial, ethical, political and technical constraints on AI leave the most opportunity?
Firstly, AI (driven by machine learning techniques) is getting better at accomplishing a whole range of things – from recognising (and even creating) images, to processing and communicating natural language, completing forms and automating processes, fighting parking tickets, being better than the best Dota 2 players in the world and aiding in diagnosing diseases. Machines are exceptionally good at completing tasks in a repeatable manner, given enough data and/or enough training. Adding more tasks to the process, or attempting system-wide automation, requires more data and more training. This creates two constraints on the ability of machines to perform work:
- machine learning requires large amounts of (quality) data and;
- training machines requires a lot of time and effort (and therefore cost).
Let’s look at each of these in turn – and we’ll discuss how other considerations come into play along the way.
Speaking in the broadest possible terms, machines require large amounts of data to be trained to a level to meet or exceed human performance in a given task. This data enables the bot to learn how best to perform that task. Essentially, the data pool determines the output.
However, there are certain job categories which require knowledge of, and then subversion of, the data set – jobs where producing the same ‘best’ outcome would not be optimal. Particularly, these are jobs that are typically referred to as creative pursuits – design, brand, look and feel. To use a simple example: if pre-Apple, we trained a machine to design a computer, we would not have arrived at the iMac, and the look and feel of iOS would not become the predominant mobile interface.
This is not to say that machines cannot create things. We’ve recently seen several ML-trained machines on the internet that produce pictures of people (that don’t exist) – that is undoubtedly creation (of a particularly unnerving variety). The same is true of the AI that can produce music. But those models are trained to produce more of what we recognise as good. Because art is no science, a machine would likely have no better chance of producing a masterpiece than a human. And true innovation, in many instances, requires subverting the data set, not conforming to it.
Secondly, and perhaps more importantly, training AI requires time and money. Some actions are simply too expensive to automate. These tasks are either incredibly specialised, and therefore do not have enough data to support the development of a model, or very broad, which would require so much data that it will render the training of the machine economically unviable. There are also other challenges which may arise. At the IDC, we refer to the Scope of AI-Based Automation. In this scope:
- A task is the smallest possible unit of work performed on behalf of an activity.
- An activity is a collection of related tasks to be completed to achieve the objective.
- A process is a series of related activities that produce a specific output.
- A system (or an ecosystem) is a set of connected processes.
As we move up the stack from task to system, we find different obstacles. Let’s use the medical industry as an example to show how these constraints interact. Medical image interpretation bots, powered by neural networks, exhibit exceptionally high levels of accuracy in interpreting medical images. This is used to inform decisions which are ultimately made by a human – an outcome that is dictated by regulation. Here, even if we removed the regulation, those machines cannot automate the entire process of treating the patient. Activity reminders (such as when a patient should return for a check-up, or reminders to follow a drug schedule) can in part be automated, with ML applications checking patient past adherence patterns, but with ultimate decision-making by a doctor. Diagnosis and treatment are a process that is ultimately still the purview of humans. Doctors are expected to synthesize information from a variety of sources – from image interpretation machines to the patient’s adherence to the drug schedule – in order to deliver a diagnosis. This relationship is not only a result of a technicality – there are ethical, legal and trust reasons that dictate this outcome.
There is also an economic reason that dictates this outcome. The investment required to train a bot to synthesize all the required data for proper diagnosis and treatment is considerable. On the other end of the spectrum, when a patient’s circumstance requires a largely new, highly specialised or experimental surgery, a bot will unlikely have the data required to be sufficiently trained to perform the operation and even then, it would certainly require human oversight.
The economic point is a particularly important one. To automate the activity in a mine, for example, would require massive investment into what would conceivably be an army of robots. While this may be technically feasible, the costs of such automation likely outweigh the benefits, with replacement costs of robots running into the billions. As such, these jobs are unlikely to disappear in the medium term.
Thus, based on technical feasibility alone our medium-term jobs market seems to hold opportunity in the following areas: the hyper-specialised (for whom not enough data exists to automate), the jack-of-all-trades (for whom the data set is too large to economically automate), the true creative (who exists to subvert the data set) and finally, those whose job it is to use the data. However, it is not only technical feasibility that we should consider. Too often, the rhetoric would have you believe that the only thing stopping large scale automation is the sophistication of the models we have at our disposal, when in fact financial, regulatory, ethical, legal and political barriers are of equal if not greater importance. Understanding the interplay of each of these for a role in a company is the only way to divine the future of that role.
LG unveils NanoCell TV range
At the recent LG Electronics annual Innofest innovation celebration in Seoul, Korea, the company unveiled its new NanoCell range: 14 TVs featuring ThinQ AI technology. It also showcased a new range of OLED units.
The new TV models deliver upgraded AI picture and sound quality, underpinned by the company’s second-generation α (Alpha) 9 Gen 2 intelligent processor and deep learning algorithm. As a result, the TVs promise optimised picture and sound by analysing source content and recognising ambient conditions.
LG’s premium range for the MEA market is headlined by the flagship OLED TV line-up, which offers a variety of screen sizes: W9 (model 77/65W9), E9 (model 65E9), C9 (model 77/65/55C9) and B9 (model 65/55B9).
NanoCell is LG’s new premier LED brand, the name intended to highlight outstanding picture quality enabled by NanoCell technology. Ensuring a wider colour gamut and enhanced contrast, says LG, “NanoColor employs a Full Array Local Dimming (FALD) backlight unit. NanoAccuracy guarantees precise colours and contrast over a wide viewing angle while NanoBezel helps to create the ultimate immersive experiences via ultra-thin bezels and the sleek, minimalist design of the TV.”
The NanoCell series comprises fourteen AI-enabled models, available in sizes varying from 49 to 77 inches (model 65SM95, 7565/55SM90, 65/55/49SM86 and 65/55/49SM81).
The LG C9 OLED TV and the company’s 86-inch 4K NanoCell TV model (model 86SM90) were recently honoured with CES 2019 Innovation Awards. The 65-inch E9 and C9 OLED TVs also picked up accolades from Dealerscope, Reviewed.com, and Engadget.
The α9 Gen 2 intelligent processor used in LG’s W9, E9 and C9 series OLED TVs elevates picture and sound quality via a deep learning algorithm (which leverages an extensive database of visual information), recognising content source quality and optimising visual output.
The α9 Gen 2 intelligent processor is able to understand how the human eye perceives images in different lighting and finely adjusts the tone mapping curve in accordance with ambient conditions to achieve the optimal level of screen brightness. The processor uses the TV’s ambient light sensor to measure external light, automatically changing brightness to compensate as required. With its advanced AI, the α9 Gen 2 intelligent processor can refine High Dynamic Range (HDR) content through altering brightness levels. In brightly lit settings, it can transform dark, shadow-filled scenes into easily discernible images, without sacrificing depth or making colours seem unnatural or oversaturated. LG’s 2019 TVs also leverage Dolby’s latest innovation, which intelligently adjusts Dolby Vision content to ensure an outstanding HDR experience, even in brightly lit conditions.
LG’s audio algorithm can up-mix two-channel stereo to replicate 5.1 surround sound. The α9 Gen 2 intelligent processor fine-tunes output according to content type, making voices easier to hear in movies and TV shows, and delivering crisp, clear vocals in songs. LG TVs intelligently set levels based on their positioning within a room, while users can also adjust sound settings manually if they choose. LG’s flagship TVs offer the realistic sound of Dolby Atmos for an immersive entertainment experience.
LG’s 2019 premium TV range comes with a new conversational voice recognition feature that makes it easier to take control and ask a range of questions. The TVs can understand context, which allows for more complex requests, meaning users won’t have to make a series of repetitive commands to get the desired results. Conversational voice recognition will be available on LG TVs with ThinQ AI in over a hundred countries.
LG’s 2019 AI TVs support HDMI 2.1 specifications, allowing the new 4K OLED and NanoCell TV models to display 4K content at a remarkable 120 frames per second. Select 2019 models offer 4K high frame rate (4K HFR), automatic low latency mode (ALLM), variable refresh rate (VRR) and enhanced audio return channel (eARC).
To find out more about LG’s latest TVs and home entertainment systems, visit https://www.lg.com/ae.