Neil Barton is the CTO for WhereScape.
The emergence of The Internet of Things (IoT) has presented exciting opportunities for the technology industry. It signals a pervasive change in society and new commercial opportunities for companies, with widespread adoption of cutting-edge IoT technology. In fact, according to Gartner research, there will be 20 billion IoT devices by 2020, with the IoT economic value expected to reach $11.1 trillion by 2025.
Many companies today are only focused on what they can "do" with the data and overlook how to plan, store and manage the abundance of new data. And as such, they could be in for a shock when they realize the potential and challenges that IoT brings. Here’s why data streaming automation is essential to companies that are embracing the IoT revolution.
Streaming Data in Real-Time
Put simply, the sheer volume of data that is being generated and consumed in IoT today can be overwhelming without the infrastructure in place to manage it. Additionally, being able to analyze the data as close to the time it was generated can have significant benefit to a business. The only way to realistically process and analyze this data is on a streaming basis from devices out in the field as soon as it is created, not at a point in time in the future.
For instance, imagine a bus company that has hundreds of buses on the road every day. The company wants to understand, as close to real time as possible, how its fleet of buses is performing so that it can maximize the efficiency and reliability of its service.
In the past, data was downloaded from sensors at the end of the day, which proved limiting. The bus could have broken down earlier in the day or could have been behind schedule all day, and there would be no way to get ahead of the problem.
Today, with data captured from on-board sensors, the bus company can analyze that data in real-time, allowing it to diagnose and detect problems immediately. With streaming data, if a bus is in danger of breaking down, the problem could be detected by analyzing the sensor data as it is produced and then steps could be taken to prevent it.
By processing data in real-time, the bus company could identify immediately if, say, the engine temperature was outside of historical norms and therefore recommend the bus be brought in for service, before it could break down.
The Necessity of Automation
The simple answer is that automation of the data management process must be a given – it saves time, reduces costs and prevents risk – all things that drive competitive edge for businesses. So, as people are creating large quantities of data, automating the data ingestion process can drastically improve the quality and reliability of the results.
Automation reduces the need for human interaction by eliminating the hand-coding and repetitive, time-intensive aspects of data infrastructure projects. This then means two things. First that insights from the data can be delivered in days, rather than months or years. And second, by freeing up humans to concentrate on the more strategic work of analysis and data output, you can deliver richer insights back to the business.
The Future of IoT
IoT is fast becoming mainstream. It’s no longer limited to big enterprises with large budgets because "in-the-field sensors" are becoming so cost affordable now. Companies of all sizes, should be looking to take advantage of the information stream IoT can provide. Additionally, the cost effectiveness and quick "time to value" offered by streaming data automation tools to any size of business to enable access to technology, data and insights that can create immediate value.
From here, businesses will then start to incorporate artificial intelligence, deep learning and machine learning into their organizations to further manage and derive value from their data. This trend will continue to grow and evolve in the coming years. We will then see the limit of what people do with data no longer comes from their ability to afford the technology to harness it, but from their creative application of the insights that they are able to create on a continuous basis.
Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.
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