Can Algorithms Make Things Smarter?
Blog: Software AG Blog - Reality Check
The IoT can be used to prevent shipping delays, stop fraud before it happens, fix equipment before it breaks, address health problems before a hospital visit is necessary, and order retail inventory before it runs out. It sounds too good to be true, right?
But it is true. Intelligent algorithms can make the Internet of Things smarter. By adding a layer of predictive analytics to streaming data analysis, companies or factories or hospitals can anticipate and act upon likely future events.
This is not fortune-telling, rather it is using data intelligently; analyzing streaming data and implementing algorithms to create predictive models to radically improve efficiencies, reduce costs, and create new revenue opportunities and customer satisfaction.
Confidence in your predictions is key; if you are uncertain about the predictions, people will ignore the systems put into place. Each organization therefore has to make sense of the massive amounts of data produced from the IoT, which flows from billions of Things.
Predictive analytics is the answer, allowing organizations to build models that can be used to:
- Reduce downtime by predicting mechanical and part failures
- Reduce the risk of fraud by spotting unusual spending patterns
- Increase customer lifetime value by being able to monitor lifestyle changes as they happen
Digitization will drive your business from smart to smarter. This is the essence of digitizing a business—the business becomes an algorithm.
Imagine if the IoT could help to prevent you from having a heart attack. That little sensor on your personal fitness monitor could read your heart rate, filter the results along with your current conditions – are you running or are you asleep? – and an algorithm could make a judgement as to whether you are about to have a massive coronary. You would receive an alert at the same time as your doctor, who would instruct you to go to the hospital immediately for treatment.
What if a depressed patient who had recently been discharged from hospital was having trouble getting out of bed in the morning? A monitor in his home could let the hospital know if he has not had breakfast, or has not left the house for a day or more.
These things are not only possible, there are organizations currently working toward making them a reality. The concept of prediction is not new, but the ability to aggregate data for continuous prediction without manual intervention makes this concept useable. By continuously monitoring the actual conditions and actions of equipment, staff, inventories, trades, and anything else that impacts a business, gathered data can be analyzed and acted upon. The next step is using statistical models with historical data to try to predict the future, i.e. predictive analytics.
Of course privacy issues are a concern with IoT, particularly in health care. But if the involved parties – government, hospitals, pharmaceutical companies and technology suppliers – work together to create a trusted ecosystem, privacy issues would be resolved. And the smarter the algorithm, the better the ability to predict and act upon valuable data.
But another concern is that smart algorithms, and the business models they support, will soon be copied – removing any competitive advantage. This is Darwinism at its best; these algorithms have to continually get smarter themselves, to evolve and stay ahead of the competition. A superior algorithm will be required to stand out. In a world where the winner takes all, who will be the smartest?
At the Smart IoT conference (April 12-13) in London, we will discuss the architectural requirements for an algorithmic approach to your business and how predictive algorithms deserve a special place in it. We can help you to build the smartest algorithms and make Things smarter.
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