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Africa on cusp of M2M boom

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The advancement of ICT in Africa has put it in a strong opportunity to capitalise on technology in the machine-to-machine field, says KEES SNIJDERS, MD of Flickswitch.

Thanks to the advancement of Information and Communications Technology (ICT) on the continent, Africa is in a strong position to capitalise on forecasts that the machine-to-machine (M2M) sector will generate $40 billion in global services revenue by 2019, says Kees Snijders, MD of Flickswitch.

Machine to machine refers to direct communication between devices using any communications channel, including wired and wireless. Forming part of the Internet of Things, M2M enables systems to communicate with other devices anywhere in the world.

“Africa has an enviable reputation of using technologically-driven solutions to overcome many of its challenges. And while not all countries share the same priorities, there are sectors that are universally important across the continent. These are agriculture, asset tracking, retail payments, and energy.”

He believes that M2M has an important part to play across these industries to not only improve efficiencies but also to reduce costs.

Water and agriculture management improves

“By implementing M2M to help with flow and pump monitoring, wastage in water can be reduced. We know too well that it has become an incredibly scarce resource on the continent. The rapid detection of leaks and careful monitoring of dam and reservoir levels mean that M2M solutions can notify relevant authorities before water levels are dangerously low,” says Snijders.

Of course, it goes beyond just water monitoring. M2M is also able to track game and livestock through technology such as tracking systems and drones. This means farmers have a more real-time view of what is going on around them and where specific issues are that need to be addressed.

In South Africa, a solutions provider has developed a livestock collar incorporating GPS and GSM technology that monitors the behaviour of a group of animals and sends an alert to the farmer’s mobile phone if there is abnormal behaviour (normally associated with theft or a predator attack).

Meanwhile, in the United States, a law was passed in November last year permitting companies to fly drones commercially on a case-by-case basis. This means that for the first time, agriculture drones will (legally) gather data across an entire growing season. By significantly improving the intelligence they have at their disposal, farmers will now be able to not only test their business models, but also become significantly more efficient. Given the significant water shortages in South Africa, drones could play a similarly critical role in our near future..

M2M is more than just about vehicle tracking

“M2M enables businesses to closely monitor goods that are in transit. Everything from the temperature at the back of the truck and its ambient conditions, to finding the optimum route, can be done using the technology. Perhaps more interesting is the fact that the traditional tracking businesses are not necessarily the ones adopting the most advanced M2M solutions.”

According to Snijders, this has created an opportunity for smaller businesses to come up with innovative use cases for M2M that can appeal to a number of vertical sectors. “The level of sophistication required to keep up with theft and hijackings means traditional tracking devices are no longer good enough. M2M enables providers to adapt their solutions to meet changing requirements faster and more cost-effectively”, he says.

Research from MarketsandMarkets.com indicate the fleet management market is certainly a priority for many organisations globally. Rising global concerns around the environment and an increasing need for operational efficiencies in the fleet sector fuel expectations that the sector will grow from $8 billion in 2015 to $22.53 billion by 2020.

On the retail point of sale (PoS) front, there is a lot of movement happening thanks to M2M

“As the capabilities of consumer devices improve, mobile payment solutions like SnapScan and M-Pesa are driving significant growth in retail payments. Different markets are doing the things that suit their specific audiences, forcing retailers to think differently around M2M and adopt technologies in new and exciting ways. The pervasiveness of pay points is adding to this growth.”

Developing countries are in prime position to benefit from the strength of PoS in the M2M world. Brazil, the largest M2M market in Latin America, has already seen a compound annual growth rate of 48 percent over the last four years in M2M thanks mainly to PoS terminals connected by GSM.

Snijders adds that, “As with agriculture and water, energy is a vital sector on the continent.

Things like smart metering and solar are certainly increasing in adoption rates but they are not pervasive as yet. With energy presenting such a significant growth sector, we can expect sizeable investment to take place. Additionally, many operators are using M2M as a great way to showcase its potential in the energy sector.”

Research conducted by Ovum show that the energy and utilities sector is one of the most important ones in the global M2M market. The consultancy projects the sector to hit $7 billion in global revenue by 2018. Given the critical nature of energy in Africa, it could well be a good one to invest for the coming years.

Companies across Africa need to be aware that M2M is not only growing but thriving. Decision-makers need to think outside the box and leverage advances in technologies in innovative ways to capitalise on this.

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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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