The industrial Internet of Things (IIoT) is about to transform everything for manufacturers. From smart factories to autonomous supply chains to new product development and innovation, the vast amount of data from connected sensors can deliver a level of insight not possible before. But, the sheer volume of data and speed of its creation is creating a data tsunami. The combination of artificial intelligence (AI) and analytics can help manufacturers optimize the use of their IIoT data.
The number of IoT devices is growing rapidly. Devices are evolving in all areas: improving performance, size, energy consumption and cost. It’s now relatively inexpensive to add new sensors with many new products and parts already IoT-enabled from the outset. GSMA Intelligence estimates there will be 25.4 billion IoT devices by 2025 – more importantly, this will be a tipping point where the number of IIoT devices (13.8 billion) outstrips consumer devices (11.8 billion) for the first time.
Big Data: From torrent to data tsunami
With sensors in the plant, the supply chain and the products sold, manufacturers have unparalleled access to incredibly valuable data. However, the volume is also staggering. According to IDC, the data generated from IoT devices is currently growing from 0.1 zettabytes in 2013 to 4.4 zettabytes in 2020. A single autonomous vehicle can generate over 40 terabytes of data every day – that’s over 2.5 gigabytes every minute.
Extracting full value from this data is essential but it requires a fresh approach to how you capture and analyze information. That’s where the problems begin. A survey from the Manufacturing Leadership Council found that only 9% of companies felt that they were prepared to use the volumes of data from IoT devices to drive decision-making. Over 80% of respondents said they were either moderately or poorly prepared.
Manufacturers are already combining IIoT, AI and predictive analytics for real-world applications such as predictive maintenance. However, making predictions based on historical data is no longer sufficient. With IIoT, the volume of data and the speed at which its created means that you must be able to handle data in real time.
Traditionally, analytics has been performed on data at rest – data loaded into a central repository – but now, with the data tsunami, you also need to effectively gain insight from data in motion. This requires a new approach known as ‘streaming analytics’. Streaming Analytics is the ability to continuously process, manage, monitor, enrich and perform real-time analysis on live streaming data–typically from sensors and other components of the Internet of Things [IoT].
Predictive maintenance is an excellent example of where adding real-time data analysis helps to give an exact and up-to-the-minute picture of what’s happening within the asset. When combined with AI-especially machine learning–you are able to see much faster where faults may occur and take corrective action.
Putting you in control of the data tsunami
If you accept that IoT data has increased by nearly 50 times in the past seven years and that pace is accelerating, then the challenge of making sense of your IIoT data becomes clear. However, new tools are emerging that are able to handle the vast amounts of data in the modern manufacturing enterprise and make it available to the right people at the right time.
There is no one tool that can provide you with an end-to-end solution for the data tsunami – the management, analysis and presentation of data. Instead, OpenText™ delivers a series of complementary technologies that seamless and securely integrate together to put manufacturers in control of IIoT data. These include:
- The IIoT data becomes another data source from organizations faced with an increasing number of enterprise applications and data sources. To maximize its value, IIoT data has to be integrated with other enterprise systems. This is complex and challenging. A recent Forrester report found that 93% of respondents felt the operational and technical challenges of data integration had led to lost revenue and customers. Data integrations platforms – such as OpenText™ Alloy™ – can handle a diverse variety of Cloud and on-premise integration endpoints, including mobile apps and IIoT.
IIoT device and data management
- The growth of IIoT has led to the creation of digital ecosystems of people, systems and things. It’s essential that the data from every endpoint is securely captured and managed. Advanced identity capabilities are required to ensure that everyone can trust that the device is what t says it is, the data being created and transmitted is genuine and accurate, and that only those with the correct credentials can access a device. An identity-driven IoT platform enables the day-to-day management of data flowing over your IIoT networks.
Data preparation and analysis
- By introducing management and integration capabilities, an organization has greater control and visibility over its IIoT data. It’s able to perform the data cleansing and harmonization of IIoT and other enterprise data necessary to apply advanced analytics. Combining AI and analytics in platforms like OpenText™ Magellan™ allows the business to gain insights from all data–structured and unstructured, historical and real time–to ensure all the value of IIoT data is delivered.
- Finally, the insight derived from your IIoT data has to be presented to the people who need it. As the data has as much operational as business value, there are far more people that can benefit from this insight. The results of analysis has to be at their fingers immediately. They should be able to construct their own data visualizations – and drill down into the data – to quickly find the information they require. The latest generation of Business Intelligence solutions – such as OpenText™ Business Intelligence – helps democratize data analysis by providing in-depth self-service capabilities so end-users can build their own dashboards and reports based around IIoT data.
The reality today is that every manufacturer is going to be gathering and processing more and more IIoT data. The scale can seem overwhelming. However, the tools and solutions are available today to tame the data tsunami and put you in control. You can gain the insights needed to achieve the transformative potential of IIoT technologies.
This month, dealing with the data tsunami is the focus of a special Critical Issue Webinar from the Manufacturing Leadership Council. I will be on the panel discussing this issue with fellow council members and others. If you would like to attend the webinar, “Is the data Tsunami slowing down decision-making?” you can register here.
I’ll report back on their thoughts and recommendations in a future blog.