Opinion: An Intel-free Mac in 2020 might seem unlikely, but it is coming soon

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It’s almost exactly a year since I last discussed the possibility of Apple ditching Intel in favor of Macs powered by Apple-designed CPUs. I argued then that it was a question of ‘when’ rather than ‘if,’ echoing a view earlier expressed by my colleague Chance.

Bloomberg yesterday suggested that the ‘when’ might be 2020. That might seem like an ambitious timescale, but I do firmly believe two things. One, Apple is already running ARM-based Mac prototypes internally. Two, if it doesn’t happen in 2020, it won’t be too long afterwards …

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Opinion: Why emerging markets should choose GSM LPWAN for IIoT projects

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OPINION Neil Hamilton, VP of Business Development at Thingstream, explains why businesses in emerging markets should choose GSM-based LPWAN connectivity to realise the full potential of IIoT projects.

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An occasional series of vendor perspectives on the world of connected business – because it’s all about making new connections and starting new conversations.

The rapid adoption of consumer and Industrial Internet of Things (IIoT) applications in developed markets, powered by the cloud, has already changed the way in which services are consumed, and their potential is vast. However, the potential for the IIoT in developing markets is also enormous; IDC predicts that projects in Africa and the Middle East alone will grow to a market valuation of $ 7 billion in 2018.

However, fragmented connectivity and infrastructures in these regions are still significant barriers to deploying effective, widespread IIoT systems.

The challenge in emerging markets

Current low-power wide-area networks (LPWANs) struggle to provide full coverage outside of major cities and towns even in developed nations, so overcoming fragmented rural connectivity in emerging markets is far from easy.

While cellular data connectivity in most developing markets remains limited, it is still more prevalent than other LPWANs offered by unlicensed providers; these still need to connect to a cellular network to communicate with the IoT ecosystem.

This is why businesses need a cost-effective, reliable, secure, and low-power option that provides ubiquitous connectivity, using the existing infrastructure.

There are many industries in these markets in which cellular or unlicensed technologies severely restrict the deployment of IIoT applications, largely due to a lack of roaming coverage.

For example, an organisation that wishes to track its assets across borders in rural areas will be unable to have full visibility of goods whenever connections are lost. Similarly, for fixed-location services where there is a lack of coverage, regularly sending data to the cloud isn’t always possible. And when a network is available, cellular roaming charges can be prohibitively expensive.

GSM-based low-power connectivity

The most ubiquitous network is the established GSM voice network, which is now available in more than 190 countries and is increasingly reliable, especially when compared with cellular data.

IoT devices can automatically connect wherever GSM connectivity is present, using the strongest network available. This avoids disruption when moving between carriers on a cellular signal, ensuring worldwide connectivity. So it makes sense to leverage this network, as other internet-based options are unable to compete in terms of cost, reliability, and coverage.

One solution is low-bandwidth messaging, achieved through a Message Queue Telemetry Transport for Sensor Networks (MQTT-SN) system. Communicating across a USSD messaging protocol that’s available on the GSM voice network, this lightweight publish/subscribe protocol can send tiny packets of data –160 bytes or less – providing true ubiquitous IoT connectivity.

This is boosted by the inclusion of integrated Quality of Service (QoS), allowing an MQTT-SN protocol to handle the transmission and re-transmission of messages, guaranteeing delivery to the corresponding ‘thing’ or application. The level of QoS is fully customisable for IoT adopters, depending on network security and application logic.

Furthermore, IoT sensors can be programmed to communicate almost any type of information that can be carried across a low-bandwidth signal, avoiding the need to have multiple devices that further clog the network.

The power issue is also circumnavigated, thanks to the way in which the devices can work. By sending data only when needed, a device’s on/off setup enables battery longevity to be maximised, not only for months, but for years, creating a true LPWAN.

This is also advantageous in emerging markets with unreliable power grids, where outages are more commonplace. Instead of sending data at regular intervals, data can be delivered when parameters have changed. For example, this would allow for remote condition monitoring of equipment, allowing for maintenance to be better planned for and more predictable.

Furthermore, data is not communicated using the internet, greatly improving cyber security by having no need to use IP addresses between devices and the connectivity platform, helping to keep connectivity levels high and costs low.

For devices that are remotely connected via the internet, the issue of securely bridging the ‘air gap’ between operational technology and IT systems continues to prove a major challenge for the safe transfer of data, which again favours GSM connectivity.

Choosing the right connectivity for emerging markets

The emergence of LPWANs, such as a GSM voice-based network, has forced businesses in emerging markets to change how they approach IoT deployments. This is because they need to think about what data is actually required from devices and how often that data is needed.

If this can be included in 160 bytes or less, why pay for an energy-sapping internet connection that is costly to implement and run, while also being visible to potential hackers?

An alternative, GSM voice-based network is the strongest and most reliable option that offers true global connectivity for IoT devices to communicate in emerging markets. Using a network with an already-established infrastructure offers huge advantages in scalability, connectivity, security, and cost.

Choosing such a network can enhance efficiencies in a variety of sectors, such as agriculture, logistics, and utilities, all of which are economically crucial in emerging markets. This type of connectivity will enable IIoT projects to be quickly accelerated in developing countries, helping to create a truly global supply chain.

Internet of Business says: This opinion piece has been provided by Thingstream, and not by our independent editorial team.

The post Opinion: Why emerging markets should choose GSM LPWAN for IIoT projects appeared first on Internet of Business.

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Exploring a controversial net neutrality opinion: Not all data should be treated equally

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Washington recently became the nation’s first state to pass net neutrality legislation, a law in which violations by all internet service providers (ISPs) are enforceable, under Washington’s Consumer Protection Act. Net neutrality, or the principle that all internet data must be treated and delivered to consumers equally, was repealed at the federal level and remains a source of great debate across the tech industry.    Several states are already exploring passing similar legislation, though it’s worth noting that these laws are widely considered a symbolic move as federal regulation prevents states from passing their own net neutrality legislature. While we…

This story continues at The Next Web
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Opinion: The Visual Internet of Things – why IoT needs visual data

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OPINION James Wickes, CEO and co-founder of Cloudview, explains why visual data is an untapped resource for smart analytics within many IoT projects.

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An occasional series of vendor perspectives on the world of connected business – because it’s all about making new connections and starting new conversations.

We are constantly reading about IoT developments, but these rarely include visual data – which is strange, because sight is our most powerful sense and we are surrounded by digital cameras. However, much of the visual data currently collected is stored locally and only used for a single purpose, while a huge percentage is never used at all. Combining this with other IoT data streams and adding analytics would make it immensely valuable.

The volumes of visual data available are eye-watering. Looking at CCTV alone. In 2015, the British Security Industry Association estimated that there were between four and six million security cameras in the UK. Our own research suggests there are now around 8.2 million. Even six million cameras recording 12 hours a day would capture 72 million hours of footage every day, producing 7.5 petabytes of visual data every hour.

Analytics and visual data: a formidable pairing

Applying analytics to visual data is complex. However, we now have the processing power, bandwidth, data storage capacity, and computing ability to enable fast, reliable analysis to a standard that makes it commercially viable. McKinsey expects video analytics to experience a compound annual growth rate (CAGR) of over 50 percent over the next five years.

Adding analytics and cloud storage to cameras provides the ability to spot anomalies that we are unable to identify with our own eyes. For example, in health and well-being alone there are many opportunities, such as:

• A camera trained on a patient in a hospital with the right analytics can now spot irregular breathing or an irregular pulse.
• Cameras are being used in care situations to monitor individuals to ensure they are being well-treated (with appropriate permissions).
• Qualified health and social care professionals are able to review footage for safeguarding purposes, and this can prove popular with both residents and staff.

Building the VIoT

The next step is to combine visual data with other data sets – from static data, such as grid references, to dynamic data, such as weather information.

This will create a vast new market – the Visual IoT (VIoT).  In other words, the integration of visual data into a uniform, IP-based data stream, combined with the capabilities and functions of a network of physical objects and devices.

In this way, cameras can be turned into super-charged sensors providing data that can then be acted upon, such as identifying that a car with a certain numberplate is allowed to enter a given area, which automatically opens the gate.

The potential is huge, and could revolutionise traffic management, and the reporting of crimes or accidents. For example, when an individual with a VIoT device enters a certain area, by previous agreement their data could be aggregated with that of others to create an accurate picture of an event.

For a motorway accident, combining data from road cameras and in-vehicle routing systems would pinpoint the precise location and help first responders to arrive more quickly. Meanwhile, adding visual data from drivers’ dashcams (with permission) could add unique views of the area around an incident.

Combining visual data with analytics can provide insight into both what is happening and why things happen, together with the ability to anticipate what might happen next.

Consider the control centres used by emergency services to monitor cameras in city centres. Adding analytics and machine intelligence would enable them to identify impending problems and send resources to defuse a situation before it escalates. The same process could identify potential risky or suspicious behaviour at transport hubs and other public spaces.

There is also tremendous potential for smart city initiatives that use existing camera data to improve the local environment. For example, NVIDIA is developing an intelligent video analytics platform for smart cities, which will apply deep learning techniques to video streams. Applications include public safety, traffic management, and resource optimisation.

Safeguarding privacy and GDPR

The big issue, of course, is privacy, but technologies such as facial and behaviour recognition can be used to reduce human involvement to a minimum. The General Data Protection Regulation (GDPR) provides additional protection, as it includes provisions for how visual data is collated and used in applications that apply AI, analytics, and deep learning techniques to that data. There are also applications in sectors such as the environment that will not involve individuals at all.

Provisions such as privacy by design, Privacy Impact Assessments, and the appointment of a data protection officer will be mandatory for public authorities and any organisation whose core activities require regular and systematic monitoring of data subjects on a large scale. There are also applications in sectors such as the environment that will not involve individuals at all.

By providing information that is not available in any other way, visual data will enable the IoT to bring even more benefits to all our lives. More information is available in the white paper Visual IoT: where the IoT, cloud and big data come together.

Internet of Business says: This opinion piece and the link to an external white paper have both been provided by Cloudview, and not by our independent editorial team.

The post Opinion: The Visual Internet of Things – why IoT needs visual data appeared first on Internet of Business.

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Opinion: Apple’s privacy-first approach has downsides but is really paying dividends now

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HomePod reviews almost universally agreed on two things: the speaker sounds incredibly impressive for the size and price, and Apple’s smart speaker is the least-smart one on the market. Both Amazon’s Echo and Google’s Home speakers were found to be significantly more capable when it comes to answering questions and carrying out tasks.

This is not, of course, coincidence. Amazon opens its Alexa ‘recipes’ up to any third-party developer, and Google has long snaffled-up as much data as it can to make its smart assistants as capable as possible. Apple, in contrast, carefully controls the personal data available to both itself and to third-party developers …

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Opinion: It should be easier to share and collaborate on some Google services, particularly Chrome and Contacts

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For the first thirty years of my life, I was a lone wolf both offline and online. Then a funny Tinder conversation (of all places, gosh do I know!) with a stranger turned into a dinner, and we were pretty much inseparable since. Suddenly, most of the “me” decisions became “we,” and as much as I like to think that choosing between Google Drive and Dropbox isn’t a life or death situation, I do rely a lot on the services I use daily.

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Opinion: It should be easier to share and collaborate on some Google services, particularly Chrome and Contacts was written by the awesome team at Android Police.

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Huawei Mate 10 Pro (U.S.) second opinion: A great phone that probably won’t make it

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Huawei has had a tough time of things in 2018, getting the rug pulled out from under it just before it launched the U.S. version of its flagship Mate 10 Pro on AT&T. The blows just kept coming with Verizon pulling out of a deal, followed by “security warnings” from some of the American government security agencies. While all of the political mess is somewhat fun to look back at and is good for a nice chuckle or two, my focus here is on the phone itself, Huawei’s centerpiece.

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Huawei Mate 10 Pro (U.S.) second opinion: A great phone that probably won’t make it was written by the awesome team at Android Police.

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Opinion: What’s the deal with Customer Segmentation?

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The following is a guest contributed post from Iqbal Kaur, co-founder of Zylotech.

In a highly competitive market, it is critical for a marketer to know who the right person is for sending information about their latest marketing campaign. It is not simply looking for any existing customer, but the customer who would be most likely to act on the specific offer provided.  By sending the correct message, to the correct person, at the appropriate time, a company achieves the following:

  1. The best return on marketing effort,
  2. Saving resources by appropriate targeting,
  3. Making customers feel understood and valued.

To make a long story short, in today’s world where information is readily available, but quickly outdated, it is hyper critical to stay relevant to your customers.

How do I segment my customers?

Now that we understand the need for segmentation, let’s ask the question of how?

Before this process can begin, it’s clear that further information is needed, such as:

  • What is my business objective?
  • How much data do we have?
  • How do we validate our results?

Once we have answers to the above, we begin to segment our customers.   There are several approaches that can be taken.  These approaches depend on the type of information you have and how it is being used.  Let’s take a look at a few:

Life Stage Segmentation

 

This type of segmentation that groups customers is based on where they are in the course of their lives. Are they young and single? Are they married with children?  This is done using demographic data like age, gender, marital status, number of children at home, etc. One interesting thing to note about this type of segmentation is that it lasts for quite some time–at least a few years–until the customer moves from one life stage to another.

What’s the relevance of Life Stage Segmentation?

Your customer, Sara, has recently had a baby. She makes the trips to her favorite retailer to get diapers regularly. One day Sara receives her mail and finds discount coupons for baby clothing.  Sara is delighted. How did they know? Sara feels understood, and uses the coupons to buy clothes for her baby.  This retailer has managed to personalize, stay relevant, and monetize.

Life Style Segmentation

This type of segmentation looks at what types of products customers buy the most.  Are they always buying the latest gadgets? Do they prefer branded products? Do they like buying things on sale? Here we mostly use information about the attributes of the products that customers are buying and at what price point.

What is the relevance of Life Style Segmentation?

Your customer, Kevin, loves gadgets. He’s been waiting for the launch of the next big cell phone. He does not want to miss out on information about the pre-launch offers at his favorite retailer. He waits and waits, and never gets any information.  Sara, however, does receive this promotion. Sara didn’t need a phone.  Kevin is frustrated. He decides to buy the phone from a different store. Having the insight to find your Kevins and Saras on an ongoing basis is a solid basis for promotional strategies.

RFM Segmentation

This type of segmentation is interesting, so let’s explore it in a bit more detail.   Here we are looking at a customer’s purchase behavior.

  • Recency: When was their last purchase?
  • Frequency: How frequently do they buy?
  • Monetary Value: How many sales do we get from them?

This is the most popular form of segmentation as it yields very useful information from basic transaction data. Let’s take a look at RFM Segmentation using five questions:

Question 1: What is my purpose?

Before we start getting into details, let’s do a small exercise. As retailer, which of the following customers is more valuable to you?

  • Rony: He made 2 orders, spent $ 200 and came last week
  • Sandra: She made 2 orders, spent $ 1000 and came 5 months ago

At first glance, it looks like Sandra is most valuable.  She spent big bucks, but it’s important to note that she hasn’t been to the store in quite some time.   Rony he has come in more recently, but has spent much less. How do we crack this riddle?  Let’s continue using RFM segmentation to make this question easier.  Now that we have set the context, we can move on.

Question 2: What data is required?
Interestingly, we can derive all our fields from our basic POS transaction data. We just have to get the numbers rolled up at the customer level. Let’s see how.  Recency is calculated by the last order day subtracted from the current day and, obviously, less is better.  Frequency is the total number of orders for a particular customer. Here, more is better. Monetary is the sum of the total sales from a particular customer, and more is certainly better.

Question 3: How do we create these segments?

Here is a step by step look at the model creation process:

 

Step 1: Data to scores.  The first step is to create our individual R, F, and M scores. Here we simply put the data in ascending order and divide the data into five equal parts (Pentiles). Each of these pentiles is then given an ordinal score of 1 to 5. Each customer receives a score for each for the 3 metrics. Now, we concatenate the 3 scores in the order of R, F and M and get a 3 digit score for every customer

Step 2: Scores to segments: Each of the possible 125 scores are then used to identify visual patterns to get a handful of manageable behavior types.  The easiest way to identify visual patterns is to put the average value for all the customers in each R, F & M table into a stacked contingency table.

Question 4: How do we get these results?

The whole segmentation is done by an unsupervised method along with a bit of human inspection. Again, this is not a predictive activity, this is a classification activity.  Keeping this in mind, we do not have any post analysis diagnostics to validate our results. The best way is to split the data into test and validation periods to see if the segments remain stable. For example, we would build the segments on 2018 data and validate on 2017. Do the segments show very similar profiles? If they do, this method is fine!

Question 5: How does the business use this information?

This information is widely used by marketing teams to identify their most valuable customers.  Perhaps the marketing team needs to understand which of their recently inactive customers should be approached with retention efforts.   It is easy to identify the best candidates by looking at their RFM segments.  Segment information can also be used to identify customers most likely to enroll into loyalty programs.  Also, if we have further information on the demographics of each segment. We see a neat profile of the typical high value customer

Closing Notes

Hopefully this article has helped you gain a better understanding of the importance of customer segmentation, and also taught you a bit about some of the most popular methods. The two most important questions you must always ask are: What is my business objective? and How do I make the segments relevant for the business and the customer?

The post Opinion: What’s the deal with Customer Segmentation? appeared first on Mobile Marketing Watch.


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Opinion: Google Smart Lock for Passwords is underused, underrated, and I wish more Android developers implemented it

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You’d be forgiven if you don’t remember what Google Smart Lock, aka Smart Lock for Passwords, is. The functionality, which aims to bridge your Google-saved website and service logins on Chrome with those in your Android apps, showed up almost three years ago in the Android M Dev Preview then started rolling to pre-Marshmallow devices. Codenamed YOLO for You Only Login Once, it is the precursor to the Autofill API we saw in Oreo and a solution to all those services that don’t use a Google/Facebook/Twitter account login.

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Opinion: Google Smart Lock for Passwords is underused, underrated, and I wish more Android developers implemented it was written by the awesome team at Android Police.

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Opinion: Giving AirPods the praise they deserve and why I won’t upgrade to version 2

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Alongside the announcement of the iPhone 7 and its controversial removal of the headphone jack, Apple teased its truly wireless AirPods. After a brief delay, they were released a just before the end of 2017, in very limited supply.

Since then, despite continued supply troubles, AirPods have gradually grown in popularity and we’re now preparing for version 2 of the wireless headphones coming later this year.

Over the last year, AirPods have become one of my most used tech products, but here’s why I don’t plan on upgrading to this year’s release…

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