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Can machine learning solve IoT data challenge?

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A new report from Forrester advises CIOs to leverage machine learning to turn the tsunami of data obtained in Internet of Things (IoT) deployments into actionable insights.

Successful companies in the industrial sector that are doing this are not only predicting problems and opportunities before they occur, but are also developing new revenue streams during their digital transformation.

Large volumes of data are required to train and then exploit machine learning algorithms, and fortunately that data is now easily accessible, especially as IoT gains traction in industries. According to Forrester’s Paul Miller, senior analyst serving CIO professionals and lead author of the report, “Put Data to Work in the Industrial Internet of Things,” machine learning is becoming a powerful tool in efforts to win, serve, and retain customers.

“It’s easy to focus on automating or augmenting existing processes with IoT, and this can deliver real cost savings and efficiency gains. But the bigger opportunity is using IoT and machine learning to drive entirely new business models, with far-reaching implications for the way in which your products are built, sold, used, and maintained,” explains Miller in the report.

Some organisations are already seeing good results by combining machine learning with IoT:

  • Ocado, one of the UK’s online-only grocers, has augmented its human packers with robots that swarm and cooperate. Average picking times have dropped significantly from two hours to just 15 minutes.
  • HUK-Coburg, a German car insurer, has partnered with IoT and telematics company Robert Bosch to develop a usage-based insurance and rescue solution which monitors driving patterns and rewards safe driving habits. Good drivers have seen premiums drop by as much as 30 percent.
  • Siemens’ claimed that shortly after giving control of the turbines to a set of machine learning algorithms at a gas-fired power station, emissions of nitrogen oxides reduced by almost 20 percent beyond the best engineers could achieve.

Miller also points out that Forrester currently identifies three core scenarios driving IoT adoption: designing connected products and experiences; operating connected business processes; and consuming connected insights. He also says that Forrester is now observing three broad classes of adoption for IoT.

Asset monitoring and control 

Although basic asset monitoring and control is rarely exciting, the report points out that this is often the first experience of IoT within the industrial sectors. Moreover, Miller writes that when the experience is done right, its return on investment could free up the resources to pay for future developments.

Some examples of these uses include smart meters to monitor energy usage; keeping track of movable assets in the transport sector; and managing temperatures in smart buildings.

Prediction and action 

The report acknowledges that the migration from asset monitoring and basic control to prediction and action is a big step, particularly for manufacturing firms that have typically focused on the physical aspects. Forrester advises that in order to succeed, companies must gather data from their own systems and from the environment in which those systems operate. They should extract insights from that data, (perhaps using the digital twin concepts that most IoT platforms support), and then interpret those insights and take action.

According to Miller, data and the insights extracted from it, are key to digital ecosystems that so many organisations now try to control.  IoT devices are an important source of data, but it’s vital that organisations understand and use the data in a timely and effective manner. Forrester believes that this is an important juncture where machine learning begins to play a real part in an organisation’s use of IoT.

Some examples where this next step in the IoT / machine learning can benefit companies include: Smart buildings which monitor weather and adjust temperatures in anticipation; transport companies anticipating failure as a means to better manage moveable assets; and building supply chains which are able to adapt to allow for customisable production, but still retain efficiencies and optimisation of resources.

Powering new business models

While the progressive use of IoT and machine learning is helping drive efficiencies as described above, Forrester believes the truly digitally minded CIO can make use of IoT and machine learning to imagine and implement entirely new business models.

Some examples of these new businesses models include: train-as-a-service offerings where the manufacturer owns and maintains the trains and simply sells their services to the rail companies; and compressor manufacturers selling compressed air by the litre to buildings. In both these instances, the manufacturer can monitor equipment, predict failures and ensure less downtime, while the customer gets exactly the service they need at a more competitive rate, without carrying the asset on their books.

Finally, Forrester cautions that while companies make the transition from physical to digital organisation, CIOs will need to ensure that they facilitate the transition and avoid putting a chokehold on the evolution – which could, ultimately, damn the organisation to irrelevance.

Arts and Entertainment

Deezer to host Hotstix’s Mandela tribute playlist

Deezer is celebrating Nelson Mandela on the centenary of his birthday by hosting a tribute playlist created by music legend Sipho “Hotstix” Mabuse.  

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Mabuse, a legendary figure in African music, first rose to prominence in the 1970s with his band Harari and later developed a name for himself as a solo artist. One of his best known songs was the global hit BurnOut in the 1980s.

The playlist takes the listener on a captivating musical journey through the life of Nelson Mandela.  It was compiled by Mabuse, who consulted with Mandela’s family and friends to ensure that the music would be relevant and accurate. The playlist also features commentary by Mabuse, which was recorded in his Soweto home.  

“I have tried to tell the story of the music that Madiba loved,” says Mabuse. “The Playlist excludes the time in prison obviously, as Madiba would not have had exposure to music in that time.  We have focused on the music we know he loved before and after that period. This recording was really an emotional journey for me, but an incredible opportunity to document these memories.”

The playlist features the music the young Mandela loved, such as The Manhattan Brothers, Solomon Linda, Brenda Fassie and Miriam Makeba.  It includes struggle songs from Chicco, Johnny Clegg, Hugh Masekela and Yvonne Chaka Chaka.  The playlist also includes Mandela by Zahara, one of the younger artists who caught Madiba’s ear.

Mabuse also offers stories of his own songs, such as Shikisha, a song greatly beloved by the former President.

“I was delighted to share my thoughts and hope the listeners enjoyed the musical journey,” says Mabuse. “Madiba did enjoy music immensely and we all have a purpose wherever we are in the world to celebrate culture and to learn from different cultures and music forms and styles.”

This playlist was inspired by the Nelson Mandela 100 campaign, calling on corporates and individuals to act as sources of inspiration and engage in conversation and action.

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Sports streaming takes off

Live streaming of sports is coming of age as a mainstream method of viewing big games, as the latest FIFA World Cup figures from the UK show. Africa isn’t yet at the same level when it comes to the adoption of sports streaming, but usage is clearly moving in the right direction.

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England’s World Cup quarter-final against Sweden was watched by just under 20 million viewers in the UK via BBC One. While this traditional broadcast audience was huge, it was streaming that broke records: the game was the BBC’s most popular online-viewed live programme ever, with 3.8 million views. In Africa, the absolute numbers are lower but the trend towards streaming major sports events on the continent is also well under way.

According to DStv, live streaming of sports dominates the usage figures for its live and recorded TV streaming app, DStv Now. The number of people using the app in June was five times higher than a year ago, with concurrent views peaking during major football and rugby games.

Since the start of the World Cup, average weekday usage of DStv Now is up 60%. The absolute peak in concurrent usage for one event was reached on 26 June, during the Nigeria vs Argentina game. The app’s biggest ever test was on 16 June with both Springbok Rugby and World Cup Football under way at the same time, resulting in concurrent in-app views seven times higher than the peaks seen in June last year.

The World Cup has also been a major reason for new users to download and try out the app. First-time app user volumes have tripled on Android and doubled on iOS since the start of the tournament.

“While we expected live sports streaming to take off, it’s also been pleasing to see that the app is really popular for watching shows on Catch Up,” says MultiChoice South Africa Chief Operating Officer Mark Rayner. “Interestingly, some of the most popular Catch Up shows are local, with Isibaya, Binnelanders, The Queen and The River all getting a significant number of views.”

With respect to app usage, the web and Android apps are the most popular way to watch DStv Now, with Android outpacing iOS by a factor of 2:1.

“We’re continuing to develop DStv Now, with 4k streaming in testing and smart TV and Apple TV apps on their way shortly,” says Rayner. “The other key priority for us is working with the telcos to deliver mobile data propositions that make watching online painless and worry-free for our customers.”

The DStv Now app is free to all 10 million DStv customers in Africa. The app streams DStv live channels as well as supplying an extended Catch Up library. Two separate streams can be watched on different devices simultaneously, and content can also be downloaded to smartphones and tablets. The content available on the app varies according to the DStv package subscribed to.

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