Myths around the challenges of implementing Industrial Internet of Things (IIoT) systems and building smart factories have made the prospect of adoption unnecessarily intimidating. In some cases, industrial organizations have avoided the most effective IIoT implementations available to them merely due to false understandings of the technology.
While IIoT adoption does require a new approach to managing and analyzing data collected in real-time, this isn’t as difficult an obstacle as many have been led to believe. Let’s take a look at four common myths about IIoT systems and the realities behind them.
This is false. The traditional databases that most industrial organizations already have in place (e.g., Microsoft SQL Server, Oracle, and so on) are wholly inappropriate for use with IIoT systems, given the tremendous volume and complexity of data in question. Expansions of these databases don’t work. In practice, there’s a night-and-day difference between standalone machines and those networked within IIoT monitoring systems.
When industrial businesses mistakenly implement IIoT infrastructures using traditional databases (which happens often), they soon discover them to be expensive to scale, unable to process the vast amount of incoming data, or incapable of handling the more complex queries required to realize the IIoT’s benefits.
When it comes to querying IIoT data streams in real-time, traditional databases are not designed or equipped for the task.
Given the fact that IIoT sensors collect massive volumes of unstructured (JSON) data, it’s understandable—but incorrect—that industrial organizations believe they must use a NoSQL database. In reality, applying NoSQL databases to IIoT use cases means overcoming many inherent issues, and are by no means the only solution. NoSQL databases do offer efficient scaling and distributed architectures that lend themselves to performing complex, flexible queries. But NoSQL databases also often bring complex infrastructures, which take intensive planning and administration to operate correctly. Additionally, NoSQL databases require engineers with specialized (i.e., expensive) expertise, which can be hard to come by, especially if scale is required. The fact that each NoSQL database solution uses its own query language can only exacerbate the challenge of enlisting the right talent.
Nearly every IIoT system must manage JSON and relational data, consisting of topological, firmware, ERP, or article data. If an industrial business chooses to address this need by running both relational and non-relational databases, those systems need to be synchronized for use in parallel. This synchronization results in complex setups, unnecessarily large cloud footprints, and disadvantages when running queries.
A full understanding of IIoT-database requirements and options points to an alternative. Advanced SQL systems are now available which offer both the ease of ANSI SQL and the flexible and scalable nature of NoSQL solutions. This combination of features makes them a good fit for IIoT implementations.
This myth leads to a frequent strategy error when implementing IIoT systems (or choosing not to due to this perceived need). A time-series database should not be foundational to an IIoT system, because intense parallel usage will severely limit its functionality and scalability. IIoT systems don’t just need to visualize data streams. They also must perform analysis, run highly concurrent workloads, and perform frequent changes to the data model. The IIoT database must enable interactive work under heavy real-time data loads, performing reads, writes, and executing ad hoc queries all at once. This processing makes it feasible to quickly and accurately identify and correct production issues at smart factories as well as leverage advanced techniques such as machine learning. These capabilities are integral to IIoT systems.
The IIoT database must also be able to adapt and extend data schemas at runtime to support agile processes. It must be possible to investigate anomalies in production using the bare sensors, ERP, quality, or other data to recognize issues. If specific jobs, materials, or suppliers are the sources of a problem, adapting the data model to examine these data types will provide the correct insights. Unfortunately for businesses using time-series databases for their IIoT systems, making such changes to the data model requires performing total rebuilds of their databases, at a great expense of both time and money.
One alternative is to use a relational database for non-time series data along with a time-series database. However, this method can result in high database expenses as the system grows and adds the challenge of maintaining the sync between data across separate databases.
It remains common for organizations to believe that because they don’t yet have large amounts of sensor data (or data that’s perfectly clean), they aren’t capable of leveraging AI systems. While a shortage of data can result in low-quality automation when driven solely by AI, it’s inaccurate to assume AI is an all-or-nothing proposition from a data perspective.
Even with limited data, it’s possible and beneficial to build a real-time data store and use AI and machine learning to augment and optimize IIoT decision-making, with final decisions remaining in human hands. Developing these AI systems is a bit of a chicken-and-egg situation: you have to start somewhere. Few industrial companies have vast stores of clean data available, but by monitoring analysis results and introducing systems to clean data automatically, AI capabilities will improve and opportunities for automation will increase as more and more data is collected
Businesses that attempt to clean all of their data before introducing it to their AI system will find themselves running into difficulties trying to grow and improve the system. With IIoT AI systems, slow-and-steady incremental improvement is the correct way forward.
Those reluctant to explore IIoT implementations should become aware of the profound operational insights and benefits these systems can deliver. The IIoT is likely within their reach.
At the same time, organizations need to be aware of what capabilities are mandatory for IIoT success. The IIoT requires entirely new data management and analysis capabilities. Monitoring, predicting, and controlling equipment across massive pipelines calls for data-management systems that enable real-time analysis of data coming from vast arrays of sensors and using diverse message formats, all under highly concurrent loads.
The future value of IIoT projects also depends on achieving data-driven automation. Specifically, data-management systems must provide rapid development and time-to-value, maintain consistent uptime, and offer low IT-operating costs when it comes to hosting, integration, and administration. This is where the IIoT concepts differ significantly from typical IoT use cases (e.g., DevOps IoT monitoring).
By separating myth from reality, industrial organizations can recognize the opportunities of IIoT for what they are and pursue them more effectively and efficiently.
This post was originally published on SmartIndustry.