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MEA smartphone sales head for 155m in 2015

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IDC’s latest research figures have shown that smartphone shipments are set to total 155 million units this year in the Middle east and Africa region.

Middle East and Africa (MEA) smartphone shipments are set to total 155 million units in 2015 after increasing 66% year on year during the first quarter to reach more than 36 million units, according to the latest figures announced today by global technology consulting firm International Data Corporation (IDC). The company’s ‘Q1 2015 Mobile Phone Tracker’ shows that smartphones accounted for 63% of the handsets shipped in the Middle East during the quarter and 47% in Africa. This comes at the expense of feature phones, which suffered year-on-year declines of around 20% in both regions and will make up just 27% of the overall MEA handset market by the end of 2019.

The growth in smartphones in the MEA region is being spurred by Google’s Android and Apple’s iOS, with the two platforms accounting for over 95% of the smartphones shipped in Q1 2015. Shipments of devices featuring these operating systems increased by a combined 67% year on year. In the Middle East, Android currently represents 80% of market’s volume, while iOS accounts for 17%; in Africa, these figures stand at 89% and 7%, respectively. Android is particularly dominant in the low to mid-priced bands, while iOS is mainly found in the $450+ price category.

BlackBerry once again suffered significant year-on-year declines across the region in Q1 2015, with the vendor’s shipments falling 14% in Africa and 29% in the Middle East. “The launch of a number of new models by the vendor seems to have had little impact on lifting the BlackBerry brand out of its continuing decline,” says Isaac T. Ngatia, a senior research analyst at IDC. “The loss of the corporate segment, spurred by the continued uptake of bring-your-own-device policies among the region’s enterprises, has had an adverse effect on BlackBerry’s performance in the market.”

The strong growth in the region’s smartphone market is largely being driven by the emergence of low-priced devices that are primarily powered by Android. Indeed, almost half of all the smartphones shipped across Africa (45.1%) in Q1 2015 were priced below $100, while almost 75% fall under $200. Low-priced smartphones are also having a considerable impact in the Middle East, with the $100–200 price band accounting for the market’s biggest share.

“This price bracket seems to be the sweet point for most vendors launching in the region, as well as for established vendors looking to increase their shares by targeting the lower end of the market,” says Nabila Popal, research manager for IDC’s Mobile Phone Tracker in the Middle East, Africa, and Turkey. “This has resulted in phones priced under $200 accounting for about 36% of the Middle East smartphone market, while at the other end of the spectrum the $450+ price band has seen its share fall from 25% in Africa and 48% in the Middle East a year ago, to 14% and 34% today.”

Nigeria and South Africa contributed significantly to the overall growth seen in Africa, with the countries experiencing year-on-year growth of 135% and 56%, respectively. Nigeria accounted for 14% of all smartphone shipments across the continent during Q1 2015, while South Africa was responsible for 12%. Samsung, Tecno, and Apple were the leading smartphone vendors in Africa during the quarter, with Huawei being ousted from the top three. The three leading vendors accounted for a combined 55% share of Africa’s smartphone shipments in Q1 2015.

For the Middle East region, Saudi Arabia and Turkey were the biggest markets, with the former accounting for share of around 20% and the latter for 17.6%. Saudi Arabia saw year-on-year shipment growth of 9.5%, while the Turkish market expanded 33% over the same period. The region’s fastest growth rate in Q1 2015was seen in Pakistan, where shipments increased 123% year on year. Samsung, Apple, and Huawei made up the top three smartphone vendors in the Middle East, together accounting for over 65% share of the market.

In terms of screen sizes, the market appears to be consolidating within the 4″–5.5″ range. “For the Middle East, 78% of all smartphone shipments in Q1 2015 fell into this bracket,” says Saad Elkhadem, a research analyst at IDC. “The strongest growth was seen for smartphones with screens of 4.5″ to 5.0″, with shipments of such devices increasing 130% year on year.”

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Smart grids needed for Africa’s utilities

Power utilities across Africa should rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem, says COLIN BEANEY, Global Industry Director for Asset-intensive and Energy and Utilities at IFS.

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Africa’s abundant natural resources and urgent need for power mean that it is one of the most exciting and innovative energy markets in a world that is moving rapidly towards clean, renewable energy sources. The continent’s energy industry is taking new approaches to providing unserved and underserved communities with access to power, with an emphasis on smart technologies and greener energy sources.

Power systems are evolving from centralised, top-down systems as interest in off-grid technology grows among African businesses and consumers. And according to PwC, we will see installed power capacity rise from 2012’s 90GW to 380GW in 2040 in sub-Saharan Africa. Power utilities are needing to rethink their business models and how they manage and monetise their assets to keep pace with the changing energy ecosystem.

Energy and utilities providers are transforming from centralised supply companies to more distributed, bi-directional service providers. They can only achieve this through the evolution of “smart grids” where sensors and smart meters will be able to provide the consumer with a more granular level of detail of power usage. This shift from an energy supplier to “lifestyle provider” will require a much more dynamic and optimised approach to maintenance and field service.

African companies must thus embrace digital transformation as an imperative. This transformation begins by embracing enterprise asset management to improve asset utilisation. The subsequent steps are enhancing upstream and downstream supply chain management; resource optimisation; introducing enterprise operational intelligence; embracing new technologies such as the Internet of Things, machine learning, and predictive maintenance; and becoming a smart utility.

Embracing mobility to drive ROI

Getting it right is about putting in place an enterprise backbone that accommodates asset and project management, multinational languages and currencies, new energies and markets, visualisation of the entire value chain, and mobility apps. Mobile technologies that support the field workforce have a vital role to play in driving better ROI from utilities’ investments in enterprise asset management and enterprise resource planning solutions.

Today’s leading enterprise asset management solutions feature powerful functionality for mobile management of the complete workflow of work orders – from logging status changes and updates, from receiving and creating new orders to concluding the job and reporting time, material and expenses. Such solutions are easy to deploy and intuitive for end users to learn and use.

Importantly for organisations operating in parts of the continent with poor telecoms infrastructure, connectivity is not an issue. The solutions work offline and synchronises when network connectivity is available. Users can work on any device—laptops, tablets, and smartphones—commercial or ruggedised.

By ensuring that field technicians have easy access to information and processes, the mobile solution enables technicians and maintenance engineers to easily do the following tasks:

·         Create a new work order on the fly and log new opportunities

·         Access both historical and planned work information when requested

·         Permit customers to sign when the job is completed

·         Capture measurements and inspection notes on route work orders

·         Create new fault reports on routing

·         Facilitate documentation through photo capturing

·         Provide easy access to technical data and preventive actions.

The power of mobility allows the engineer to be the origin of all data capture on a service event. They can easily inquire on asset history, record parts used or parts needed for repair, record labour hours, and expenses as they occur, and any notes of repairs performed. When coupled with workforce management tools, such solutions unlock significant productivity gains for utilities who are trying to get the most from their workforce and assets.

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How machine learning can save your life

Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

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Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

But what if we could have predicted the outbreak months before it happened, buying us time to take proactive measures to contain it and curb its spread?

With access to overwhelming volumes of data, the computational power needed to store and analyse this data in real time, and sophisticated algorithms that can find patterns in the data and alert authorities to health problems before they become, well, problems, pandemics don’t have to be as devastating as they have been in the past.

In fact, with advanced data analytics, we can better manage any disease – long-term, short-term or pandemic – resulting in better patient treatment, more efficient use of resources and cost savings.

It’s been done before.

By analysing data from social media, blogs, online forums and keyword searches, we were able to predict the 2012-2013 US flu season three months before the Center for Disease Control (CDC) issued its first official warning.

Imagine the impact if the same analytical power was applied across the entire healthcare spectrum – not only on a national and global level, but right down to the individual level.

Data evolution

In the past, health workers relied on manually intensive, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through but because the data was anecdotal and historical, authorities did not get a complete understanding of the reach and impact of the outbreak.

During the Ebola outbreak, the CDC adopted a mobile data collection system that enabled health workers to instantly submit information to a database via text messages. This low-cost method of information gathering not only resulted in fewer errors but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres.

While this was certainly an improvement on the paper-based systems of old, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time.

Data-driven action

But mobile phones are just one source of data. Today, health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but to see them coming months in advance – and what could happen if we don’t act on the information.

By mining structured and unstructured data, we can track the movements of infected populations and who they come into contact with; we can measure the success of containment policies, education campaigns and treatments – and what to do if they’re not working; we can determine the effect of weather and other environmental factors on the spread of diseases.

Never before have we been able to act on information to save lives, not just during pandemics but through better understanding and treatment of diseases.

Personalised treatment

Until now, standard treatments for diseases such as cancer and HIV have been applied to all patients, regardless of their unique profiles and with little understanding as to why some people respond well to certain treatments and others don’t.

But by analysing and creating ‘medical maps’ of individuals that take into account their anatomy, physiology, DNA, RNA and chemical composition, doctors can prescribe personalised treatments that have a greater chance of success.

There are many other benefits of data analysis in healthcare:

·        Personalised treatment can result in fewer hospital admissions and can produce faster results and better experiences for patients;

·        By better understanding the impact of lifestyle and diet on health, medical aid providers can educate their members with the aim of improving their health, which could result in cost savings for both the provider and the member;

·        Governments can use data to develop proactive approaches to protecting and promoting public health, to prioritise services and to find ways to cut costs so that they can provide healthcare to more citizens.

·        By sharing data and results from clinical trials and combining that data with academic, patient and industry data, medical researchers can better understand the genetics of viruses, why some strains are more deadly than others, and why some people are more resistant to viruses. This could spark innovation and generate new insights that ultimately improve treatment and outcomes.

AI and machine learning

As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.

We’re already seeing successful applications of artificial intelligence (AI) in predicting relapse in leukaemia patients and in distinguishing between different types of cancer.

Machine learning can extract valuable insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.

There are plenty of doomsday theories about how machines will supersede our intelligence and rise against us. But there aren’t enough stories about the potential of data analytics, AI and machine learning to supplement human skills and knowledge to drastically changes lives for the better – and even save them. Right now, it’s looking more likely that machines will actually help us to live longer – and I don’t know many people who would object to that.

 

  • ANESHAN RAMALOO, ‎Data Scientist and Senior Business Solutions Manager at SAS.
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