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Industry goliaths are the current kings of the IoT space. But machine learning and automated data science may help small and midsize businesses be more successful in their own IoT initiatives.
IoT devices have become commonplace in our everyday lives. From activity trackers like FitBit to smart speakers like Amazon Echo, many of us have at least heard of these devices if we aren’t using them already.
It’s only natural, then, that connected devices have made their presence known in the workplace. For example, many companies are saving money by using smart devices to promote energy efficiency, using these devices to automatically power off lights and appliances when guests check out of hotel rooms or turning off idle devices in the office after close.
Some businesses have taken the next step, going beyond simple IoT devices with high-concept implementations. Self-driving cars are probably the best example of this, as driverless cars would require an immense amount of data gathering and analysis as well as seamless connectivity between a variety of systems and devices. Yet that’s not scaring anyone away, with companies like Ford and Tesla launching huge initiatives into self-driving cars.
However, while enthusiasm for IoT is swelling at businesses of all sizes, it largely remains the playground of industry titans. While many small businesses would love to fully embrace IoT, they often don’t have the expertise or budget required to fully capitalize on the promise of IoT.
Fortunately, there is an answer on the horizon: automated data science and machine learning. The cumbersome process of managing IoT data and creating accurate models can now be automated using a meta-learning approach, making predictive analytics accessible to more organizations. Meta-learning principles not only help automate machine learning, they also improve the accuracy of analytical models while reducing infrastructure cost.
I recently got a chance to delve deeper into this meta-learning approach over at TechTarget’s IoT Agenda. Check out my guest blog for more insight into how automated machine learning can help equalize the IoT playground between SMBs and big corporations.
Mark Troester is the Vice President of Strategy at Progress. He guides the strategic go-to-market efforts for the Progress cognitive-first strategy. Mark has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect. Before these positions, Mark worked as a developer and developer manager for start-ups and enterprises alike.
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