Is Office 365 the one-stop cloud solution for business? Mimecast’s Senior Sales Engineer, GIULIO MAGNI, looks at its potential shortcomings and how companies can overcome these.|Is Office 365 the one-stop cloud solution for business? Mimecast’s Senior Sales Engineer, GIULIO MAGNI, looks at its potential shortcomings and how companies can overcome these.
Microsoft has said that Office 365 is the future and the numbers agree. More than a million subscribers are signing up each month and four out of five Fortune 500 companies are already using Office 365.
Why the overwhelming success? The reason lies in the rapid rate of cloud adoption, both global and local. Ipsos Mori’s SMB IT Research 2015 found that 57% of South African small to medium businesses are already accessing their work remotely through using a cloud service. And this number is only set to grow. IDC Futurescape predicts that as early as 2017, more than a third of new applications will be cloud-enabled.
The exponential growth numbers paint a picture of a future where Office 365 is standard, and the question that emerges is not so much “should I migrate” as it is “when will I migrate”. There is, however, another question that needs answering before making the leap to Office 365, namely what the practical implications of the move are.
The unspoken promise of Office 365 is that it is the last great data migration that businesses will ever have to make. Unfortunately, it still comes with all the teething problems of any largescale IT implementation. No IT solution is perfect, and identifying the gaps in Office 365 before migrating is critical to a smooth transition.
The hidden shortcomings
Microsoft’s messaging on Office 365 is that it is a one-stop cloud solution for managing important enterprise data such as email. To an extent, this is true, especially for smaller companies. Office 365 is rich in features: 99.9% email availability in its SLA, antivirus and malware protection and email recovery.
However, a closer look at the functionality of these features reveals pain points. Outages happen and even an excellent product like Office 365 is vulnerable. Officially, the amount of downtime is low enough to adhere to its SLA, however there have been recordings of O365 outages. Downtime can include admin access, AD authentication, policy engine, archive access and so on. Adding up the outages across all of the services amounts to an issue that doesn’t just impact the average email user but entire organisations.
The data dilemma
There are other gaps in the Office 365 platform that impact business continuity and data archiving. In Office 365, there is no ‘true’ email archiving, for example. Users can delete emails from the archive unless they are placed on In-Place Hold, something that can affect compliance. While there is a recovery period if this happens, the data is lost forever as soon as the deleted items folder is emptied.
Another notable shortcoming is the lack of mobile access users have to the email archive. End-users are only able to access the archive from a browser or Windows desktop. In addition, Office 365 lacks the ability to archive email on a network drive, further limiting the archive’s mobility. In an always-on world, the inability to search and recover important email data can have serious business consequences.
Two clouds are better than one
None of this makes Office 365 a poor solution. On the contrary, it is an excellent product that will over time add new layers of functionality. But just like the on premises world from which we come it is precisely this strength that opens the space for a third-party services aimed at supporting the Office 365 experience.
The cloud may have changed the way in which we work, but one thing remains true: It is risky relying on a single service provider, particularly when it means that your data is essentially sitting in one giant basket. Mimecast’s approach to this has been to create products that can work seamlessly alongside Office 365 to enhance its email archiving capabilities. We anticipate more companies will offer these kinds of add-on services as Office 365 becomes more universal.
How can businesses decide which of these third-party services is right for their needs? Companies looking to migrate to Office 365 need to closely examine their own requirements and where Office 365 might fall short in addressing these. That will give them guidance that will allow them to seek out products to successfully bridge the gap.
Giulio Magni offers these top tips to optimise your Office 365 experience:
· Not all Office 365 plans are created equal. Vet the different plans’ offerings and use your Office 365 dashboard to research which purchase service add-ons might need to be added to meet your organisational needs.
· Most on-premises legacy archive solutions don’t work with Office 365, leaving your users unable to access archived email. Get a compatible archiving solution in place before the migration.
· Never use the Permanently Delete option in Office 365 without having a robust archiving solution in place. Set up retention policies and tags early on and test these on a few accounts before applying company-wide.
· Microsoft is continuously improving functionality and fixing bugs through its updates, but these may affect productivity. Enable First Release to let your support staff try updates before worldwide release.
· Finally – You will hear Microsoft say that you don’t need another service but they cannot be impartial about mitigating themselves. It is your business and your responsibility to keep the email communication flowing and compliant. Remember the principals you adopted when everything was in house – the same methodology still applies today – just in the cloud.
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.