If you habitually unlock doors or switch on lights from an app on a smartphone, then you might already be very familiar with the concept of the Internet of Things, or IoT for short. Indeed, according to experts cited by InformationWeek, there will be up to 50 billion connected devices on the planet by 2020. However, IoT is already long-established in the enterprise.

Crucial applications of IoT in the corporate realm have included sensors on such critical assets as industrial and transportation equipment. Such sensors already assist businesses in automating and maintaining a wide array of equipment; however, IoT can still have various, as yet unrealised implications for businesses looking to further their success.

IoT in control center

A lot of potential data… and responsibility

In a ZDNet article, the website’s editor-in-chief, Larry Dignan, admits that “the movement with IoT is you’re just putting sensors on everything”, but he highlights IoT as the most influential component comprising digital transformation.

In providing a greater amount of analytical data and knowledge and changing the manner in which you track your inventory and supply chain, it can transform your entire firm. IoT could be especially pivotal in how smart cities handle the likes of traffic and water supplies.

However, given how inexpensively storage can be obtained, there is a risk of companies gathering much more data than they really need. As a result, they could leave data with the potential to fall into the wrong hands as the result of a cyber attack.

Therefore, should your own business invest heavily in IoT, the onus is on your company to sort crucial data from unnecessary information. Your company’s current data protection scheme ought to be extended to IoT to help secure the data sourced through such. This could involve you utilising strategies similar to those of securing a data centre.

Go for IoT: tools for doing exactly that

However, despite the early promise of IoT, it has yet to emerge from an experimental phase. The market research company Forrester Research expects this to change in 2018. Writing for ZDNet, Chris Voce of Forrester Research points out that, while there are various platforms fitting for use in IoT scenarios, only some of these platforms will be suitable for specific buyers.

For this reason, we can anticipate platforms reacting to this situation by specialising their offerings to better suit their audiences’ specific requirements. This certainly bodes well for your own business if you expect it to spend more money on IoT solutions this year. The broad choice of IoT tools can help ensure that your expenditure goes further.

There are various IoT case studies to heed if your company is set to embark on such spending. For example, with Azure IoT Hub, you can control a massive number of IoT devices. This tool has already been used by Honeywell to let its users track their thermostats and unlock their doors from their phones. Meanwhile, technology development firm Avatorion has put IoT towards deploying robots aimed at keeping lonely children entertained in hospitals.

You should also consider integrating Azure IoT Suite with systems and devices already in your workplace, as this would let you maximise returns from data sources. This suite would let you catch and analyze device data previously left untapped and so quickly improve corporate results.

If you have Big Data problems to debug and optimise, the Data Lake Analytics tool can let you do so more easily. The water technology firm Evoqua has used this tool’s sophisticated analytical capabilities to obtain valuable insights from field-equipment data.

Machine learning algorithm

Data and the machine: how machine learning can help

You can realise even further IoT possibilities through use of machine learning algorithms, which can enable you to automate data analysis and more speedily present powerful insights. If your business does not currently make use of machine learning, it can begin effectively experimenting through using the Windows and Mac desktop client called the Machine Learning Workbench.

With this tool, data preparation is already built in, letting you learn machine learning as you perform it. However, the tool can also automatically change your data as a means of optimising it thoroughly for machine learning algorithms. In using the Machine Learning Workbench, the Snow Leopard Trust charity has succeeded in automatically classifying millions of images of snow leopards in minutes.

The respected provider of cloud solutions RedPixie can help your own company make the best use of all of these tools. The website of this London-based business lists many more such tools and companies which have benefitted from using them.