Gartner predicted in 2017 that there will be 20 billion connected things online by 2020. Since that prediction, the adoption of IoT technology has met and surpassed the predictor’s expectations. The number of companies that will invest in IoT will continue to expand rapidly due to technological advancements producing sensors that are smaller, cheaper and more effective.
The challenge is no longer in the technology, but in the value organizations can extract from the data they collect. IT teams deploying new IoT solutions need to deliver the value the companies desire from their investment and are struggling with several roadblocks in fulfilling this task.
Extracting value by making insights quickly and easily accessible to the business from data has always been difficult, often like finding a needle in a haystack. Adding more data, data sources, data types and streaming data to the mix can make it close to impossible to get the desired value with existing methods of data processing, storage and analytics. To make the most out of their investment in IoT, organizations need to get several important things aligned within their strategy.
Looking to Automation
Due to the sheer volume of connected devices and the amounts of data they produce, the only realistic solution to cope with the massive amounts of IoT data is automation. Automation helps organizations ingest, transform and deliver data and insights from it in real-time. It can ensure that IT teams can absorb the astronomical volume of data and be in a position to deliver insights in a way that the businesses can use and extract value from it.
Automation eliminates the burden of hand-coding the repetitive and time-intensive aspects of data infrastructure projects for data warehousing teams which, in turn, delivers several critical advantages. Insights from the data can be delivered in a much shorter time frame at a lower cost with drastically improved quality and reliability of the results. It additionally frees up data warehousing teams to concentrate on the more strategic work of analysis and data output. However, it isn’t enough to just automate the processing of data. The only way to realistically process data is on a streaming basis from devices out in the field as soon as it’s created, not at a point in time in the future.
Automation additionally plays a critical role in the processing of data. In the case of processing data from devices in the field, streaming data automation allows data warehousing teams to process data as it’s created in the field, closing the gap from data to insights.
For example, a bus company that has hundreds of buses on the road every day wants to understand, as close to real-time as possible, how its fleet of buses is performing so that it can maximize the efficiency of its service. With IoT-data captured from onboard sensors, the bus company can analyze that data in real-time in the field, allowing it to diagnose and detect problems immediately.
Historically, data was downloaded from sensors at the end of the day, which proved problematic, because the bus could have already broken down or could have been behind schedule all day. There would be no way to get ahead of the problem. However, with streaming data automation, if a bus was in danger of breaking down, the problem could be picked up by sensor units in real-time and then steps could be taken to prevent it. By processing data in real-time, the bus company could identify immediately if the brake pads were wearing thin and could then notify a mechanic to replace them before the bus could break down.
Understanding IoT Data Sources
There are many different types of data sources and formats created and leveraged by IoT devices – sensors in buses’ brakes, thousands of sensors in a modern airplane, video surveillance cameras and machines in a factory. Some of it is traditional, structured data, but there’s an increasing amount of semi-structured and unstructured information produced that also needs to be processed in near real-time. Before this data can be transformed into insights, it needs to be collated and processed into a more manageable form. Attempting to do this complex task manually isn’t an option given data volume and complexity. Automation is the only way to do this efficiently.
In certain cases, value can be gained by consuming entire streams of data. Those data sets can be saved in their entirety to be analyzed at a later point in time to identify trends. But, generally, it’s more beneficial to filter all data during ingestion. To understand exactly what to do with different IoT data streams, organizations need to build an information flow that creates a big picture view of the critical, time-sensitive information most valuable to their organization.
At the same time, organizations need to identify the historical information they should store that will be useful to reveal trends over time. Something like a data lake architecture can be useful as a repository to store the full mass of structured, semi-structured and unstructured data in its native format. However, automation tools will then be needed to transform the data from an indistinct heap of ones and zeros into valuable insights.
IoT’s Impact on Storage
When it comes to infrastructure to support IoT environments, the knee-jerk reaction to the huge increase in data from IoT devices is to buy a lot more storage. However, with this growth being exponential, this is a costly and short-term strategy. Instead, businesses need to consider how to transform the data in the process of being stored – and, by doing that, decrease it in the process. Data analyzed in real-time means that organizations can save down data summaries rather than large transactional tables for future analysis.
Not only does this save on storage costs, but it also speeds up future reporting processes, and improves the quality and reliability of insights. It’s a question of sifting out what’s valuable and what isn’t. That said, there’s often value in storing the raw data for a period of time to test exploratory workloads. For this, cloud storage can be a cost-effective short term option as part of a data lake infrastructure. However, it will also be critical to deploy automation tools to organize this information, manage the schemas and allow the data to be analyzed, queried and searched in the most effective format.
The IoT Market is Growing
Sensors for every thinkable purpose have become very affordable and IoT is quickly becoming mainstream. The market’s economic value is expected to reach $11.1 trillion by 2025. It’s no longer limited to big enterprises with large budgets as many smaller companies are looking for ways to improve their business based on the information IoT-applications can provide.
Additionally, for sensors and other IoT applications, mature automation tools are also available to accelerate the time to value and create an immediate impact. The next step for many companies to manage and drive value from their data will be the implementation of artificial intelligence, deep learning and machine learning. We will then see that the limit of what companies do with their data will no longer come from their ability to afford the technology, but instead from their creative application of the insights that they’re able to create.
For businesses, big and small, data is one of the most precious assets they can use to gain an edge over their competitors. Sensors for IoT applications have become much cheaper and can provide organizations with every possible data set. But, an investment into IoT is pointless if the organization can’t derive actual insight and value for its business. For all of the reasons previously discussed, automation tools are absolutely critical to leveraging the full value of IoT investments.
Written by Chris Stewart