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Trade evolves in Africa – again

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Arica’s recent historical deficit puts it in a convenient position in terms of adopting new technologies. Other regions have paid the expensive price of being early adopters but African companies can now adopt the best in modern technology, writes BRETT PARKER, MD for SAP Africa.

Trade is in Africa’s blood. Its rich resources, numerous societies and access to the world have created a hotbed of trade civilisations. One could go back millennia to the early kingdoms around the continental horn – the forefathers and peers of the Ancient Egyptian world – or the mysterious Kingdom of Nok in West Africa as examples.

But even in the last 2,000 years Africa never shied away from trade. The Kingdoms of Ashanti and Kongo were world-famous business hubs. In Libya vast desert cities can be found where ancient Berbers built elaborate irrigation systems. The Zimbabwe ruins and South Africa’s Mapungubwe had yielded evidence of extensive trade with Asian and Middle Eastern nations.

But most striking is the legacy of kingdoms that existed along the Sahel: the transitional area between the Sahara and the rest of the continent. Here numerous societies sat shoulder to shoulder, controlling the vast trade moving between Eurasia, West and Central Africa for ages.

Today the world is shifting gears into a new revolution, creating an opportunity for Africa to assert its legacy as the birthplace of business networks. Computational power and connectivity is shrinking the globe, changing how we compete and cooperate. Mastering pace, scale and complexity, creating channels and fostering partnerships have never been more achievable. Some have called this the Network Revolution and it is Africa’s greatest business opportunity yet.

The continent’s recent historical deficit ironically puts it in a very convenient position. Whereas other regions have paid the expensive price of being early adopters, African companies and states can readily adopt the best in modern technology, resulting in real gains on the ground. If there are any doubts about this, just look at the spectacular penetration of mobile devices in Africa: more than any other region in the world. Consider the remarkable growth of Rwanda, which thanks to savvy technology investments has tripled its GDP since 2000.

Success and growth is almost a given when developing markets jump onto the Network Revolution bandwagon. The real question is how to go about it. Here are three steps defining the transition:

From manual to electronic and Internet-based. The Network Revolution is a shift from manual processes kept separate in silos. Automation and accessibility are among its pillars, opening both resources and the ability to cross-pollinate ideas. South Africa’s Department of Home Affairs has dramatically improved its service, auditability and turnaround times by going paperless. It captures all data electronically, which is shared across its footprint. This not only made for happier citizens, but opened the way to adopting the country’s award-winning Smart ID cards.

From an entity and chain to a network. Business networks are the oldest and most vital components to any enterprise’s survival. These are jealously guarded because of their fragility: all it takes is for that proverbial weak link in the chain to break. But today digital

sourcing marketplaces such as Aruba are making it easy to find suppliers, partners and buyers. The mobile phone is a cornerstone to these networks: Africa is currently undergoing a farming revolution in countries such as Kenya and Tanzania, where mobile services help farmers get daily prices, share advice and even gain micro-insurance for their crops across a web of networks, not flimsy top-down chains.

From need to reach and fusion. The biggest impact of the Network Revolution is being born from data. We are increasingly able to quantify aspects of the world through data, be it consumer behaviour, environmental shifts, mechanical maintenance or anything that generates information about its behaviour. That may soon become everything as the Internet of Things brings sensors to every nook of our world. And fusing the resulting data in creative ways to offer new insights will be the differentiator between the haves and have-nots of tomorrow. This is extending the reach and proactivity of companies and governments beyond their traditional boundaries. One example is the Ethiopian Electric Power Corporation, which has accelerated its delivery and boosted efficiency by adopting data-centric thinking.

One element underpins all of the above: the platform. For any business or government to take advantage of the Network Revolution, it must consolidate its processes into a unified software platform: a powerful foundation where everything ties together. Called the 3rd Platform, this is the next step in digital technology, taking advantage of the power and scale provided by modern data centres and connectivity. One example is SAP HANA, the pioneering in-memory platform. Think of it as an operating system for the entire enterprise: a single space upon which all other processes – be it internal tasks, external collaboration, differentiating applications or new technologies – can find a home.

This consolidation pays dividends. Research from McKinsey & Company shows that networked enterprises using collaborative technology to connect processes to customers, suppliers, and partners outpace their peers in nearly every category of business performance. Africa is primed to take the Network Revolution by the horns and reassert itself as the birthplace of business.

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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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