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Big data key to energy

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The current energy situation in South Africa has caused many companies to look at ways to manage their power usage. However, JACO BARNARD of Wipro. says that in order to properly manage consumption, vast amounts of data need to be collected.

Given the current South African power and energy situation, energy management has become a necessity rather than a choice, particularly in the retail environment. Reducing carbon footprint while adopting sustainable strategies to balance business objectives with environmental responsibilities has become critical. Aside from increasing international pressure to adopt greener technologies as part of sustainability initiatives, the cost of energy has become a significant challenge. While energy-saving initiatives around lighting, heating, ventilation and cooling can provide assistance, these are often capital-intensive projects that need to be implemented effectively to deliver maximum benefit. In the low margin, high volume retail environment, it has become essential to keep the spiralling cost of energy under control to maximise profitability by optimising operational expenses. This requires data, and more importantly insight into data that can drive actions that will help retailers optimise energy management to curb costs.

The importance of data

For many retailers, problems with power supply can be catastrophic. Without power, cold chain logistics can be compromised and hundreds of thousands of Rands worth of perishable stock can be spoiled. In addition, stores themselves cannot operate, losing business and customers. As a result, many retailers have resorted to backup power and alternative energy sources. However, these initiatives are often costly, particularly if energy consumption is not managed and optimised. In order to manage the cost of energy, both from traditional and alternative power sources, effective energy management is required. This in turn requires data, as without data around metering, measurements and monitoring it is all but impossible to gain the insight required to manage energy consumption. Collecting consumption data is the first step, as this data can then be analysed to deliver the required insights to drive energy saving and improvement initiatives.

By collecting large volumes of data around energy consumption, costs, asset operations and business policies, it is then possible to determine potential operational savings. For example, temperatures can be optimised according to locational and seasonal climate, unnecessary lights and cooling can be switched off when not required, and efficiency of working assets such as refrigerators can be assured. This data can also be collected over extended periods and analysed to determine long-term trends, energy leakages such as chronic equipment efficiency issues, insulation problems and more. Savings can then be achieved by correcting major issues and fine-tuning operations and controls. There are hundreds of ways that energy consumption can be improved across areas such as lighting, electrical, cooking, air conditioning and refrigeration systems. This means that there are many opportunities for savings, but there is no ‘one size fits all’ approach. Big data and analytics are the crucial components in effective energy management.

Making big data work for energy efficiency

The first step in improving energy efficiency requires the establishment of savings protocols. Pilot studies should be carried out and savings strategies that can be actioned with data should be determined. The range or significance of savings can then be used to determine the feasibility of rolling these solutions out, based on spend and expected returns. The second step is to set up data collection mechanisms. The volume and method of data collection depends on the current technology deployed and how granular the data is required to be – for example monthly invoices on energy consumption will typically not provide enough visibility, so it may be necessary to implement a monitoring solution that provides sample data every half an hour to provide more accurate insight. Data may be collected via Building Automation Systems, directly through controllers or through management applications. Many legacy and proprietary systems do not allow any access to data, in which case metering and sub-metering analysis must be incorporated.

Simply collecting data will not enable retailers to determine savings, so once data has been obtained, it must be analysed in order to provide insight. This is a specialist skill set that may be expensive to maintain in-house, so often it is advisable for retailers to collaborate with an expert service provider. Given the volume of data, it is also advisable that structured methods and toolsets be put into place across all sites for analytical purposes, another area where an expert provider can assist. The final step is action, as savings will not result from insight alone. Volume is also essential, and retailers need to action initiatives based on insight across all or most of their sites to produce meaningful savings.

Beyond energy

Big data can be harnessed for more than just energy efficiency, and has potential to deliver significant additional advantage in the retail space. For example, the customer experience can be improved by collecting data from various channels and using it to improve in-store temperatures for comfort, or by utilising online channels and sensor data to tailor the shopping experience. Store layouts can be improved, footfall can be increased and more. In addition, analytics can be leveraged to improve asset maintenance to help bring down maintenance costs and improve asset life. The data from in-store devices, video and wearable technology has the potential to improve sales effectiveness and improve workforce productivity. All of these initiatives will use the same foundation of big data and analytics.

Ultimately big data analytics, whether in the form of energy management or other initiatives, can help retailers to improve their competitive edge. More efficient operations and reduced costs lead to enhanced profits, as do improved customer experiences. Data and analytics are the crucial elements to more successful, more efficient and more profitable retail environments.

* Jaco Barnard, Head of Retail at Wipro, South Africa.

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Smart home arrives in SA

The smart home is no longer a distant vision confined to advanced economies, writes ARTHUR GOLDSTUCK.

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The smart home is a wonderful vision for controlling every aspect of one’s living environment via remote control, apps and sensors. But, because it is both complex and expensive, there has been little appetite for it in South Africa.

The two main routes for smart home installation are both fraught with peril – financial and technical.

The first is to call on a specialist installation company. Surprisingly, there are many in South Africa. Google “smart home” +”South Africa”, and thousands of results appear. The problem is that, because the industry is so new, few have built up solid track records and reputations. Costs vary wildly, few standards exist, and the cost of after-sales service will turn out to be more important than the upfront price.

The second route is to assemble the components of a smart home, and attempt self-installation. For the non-technical, this is often a non-starter. Not only does one need a fairly good knowledge of Wi-Fi configuration, but also a broad understanding of the Internet of Things (IoT) – the ability for devices to sense their environment, connect to each other, and share information.

The good news, though, is that it is getting easier and more cost effective all the time.

My first efforts in this direction started a few years ago with finding smart plugs on Amazon.com. These are power adaptors that turn regular sockets into “smart sockets” by adding Wi-Fi and an on-off switch, among other. A smart lightbulb was sourced from Gearbest in China. At the time, these were the cheapest and most basic elements for a starter smart home environment.

Via a smartphone app, the light could be switched on from the other side of the world. It sounds trivial and silly, but on such basic functions the future is slowly built.

Fast forward a year or two, and these components are available from hundreds of outlets, they have plummeted in cost, and the range of options is bewildering. That, of course, makes the quest even more bewildering. Who can be trusted for quality, fulfilment and after-sales support? Which products will be obsolete in the next year or two as technology advances even more rapidly?

These are some of the challenges that a leading South African technology distributor, Syntech, decided to address in adding smart home products to its portfolio. It selected LifeSmart, a global brand with proven expertise in both IoT and smart home products.

Equally significantly, LifeSmart combines IoT with artificial intelligence and machine learning, meaning that the devices “learn” the best ways of connecting, sharing and integrating new elements. Because they all fall under the same brand, they are designed to integrate with the LifeSmart app, which is available for Android and iOS phones, as well as Android TV.

Click here to read about how LifeSmart makes installing smart home devices easier.

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Matrics must prepare for AI

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students writing a test

By Vian Chinner, CEO and founder of Xineoh.

Many in the matric class of 2018 are currently weighing up their options for the future. With the country’s high unemployment rate casting a shadow on their opportunities, these future jobseekers have been encouraged to look into which skills are required by the market, tailoring their occupational training to align with demand and thereby improving their chances of finding a job, writes Vian Chinner – a South African innovator, data scientist and CEO of the machine learning company specialising in consumer behaviour prediction, Xineoh.

With rapid innovation and development in the field of artificial intelligence (AI), all careers – including high-demand professions like engineers, teachers and electricians – will look significantly different in the years to come.

Notably, the third wave of internet connectivity, whereby our physical world begins to merge with that of the internet, is upon us. This is evident in how widespread AI is being implemented across industries as well as in our homes with the use of automation solutions and bots like Siri, Google Assistant, Alexa and Microsoft’s Cortana. So much data is collected from the physical world every day and AI makes sense of it all.

Not only do new industries related to technology like AI open new career paths, such as those specialising in data science, but it will also modify those which already exist. 

So, what should matriculants be considering when deciding what route to take?

For highly academic individuals, who are exceptionally strong in mathematics, data science is definitely the way to go. There is, and will continue to be, massive demand internationally as well as locally, with Element-AI noting that there are only between 0 and 100 data scientists in South Africa, with the true number being closer to 0.

In terms of getting a foot in the door to become a successful data scientist, practical experience, working with an AI-focused business, is essential. Students should consider getting an internship while they are studying or going straight into an internship, learning on the job and taking specialist online courses from institutions like Stanford University and MIT as they go.

This career path is, however, limited to the highly academic and mathematically gifted, but the technology is inevitably going to overlap with all other professions and so, those who are looking to begin their careers should take note of which skills will be in demand in future, versus which will be made redundant by AI.

In the next few years, technicians who are able to install and maintain new technology will be highly sought after. On the other hand, many entry level jobs will likely be taken care of by AI – from the slicing and dicing currently done by assistant chefs, to the laying of bricks by labourers in the building sector.

As a rule, students should be looking at the skills required for the job one step up from an entry level position and working towards developing these. Those training to be journalists, for instance, should work towards the skill level of an editor and a bookkeeping trainee, the role of financial consultant.

This also means that new workforce entrants should be prepared to walk into a more demanding role, with more responsibility, than perhaps previously anticipated and that the country’s education and training system should adapt to the shift in required skills.

The matric classes of 2018 have completed their schooling in the information age and we should be equipping them, and future generations, for the future market – AI is central to this.

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