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Achieve High-Value Use Cases With

Low-code and No-Code Industrial AI & Analytics

Platforms like TrendMiner empower engineers with self-service solutions to visualize and understand operational data.

Guest blog by Joe Lamming, Senior Analyst at Verdantix

Analytics software is important for decision-making in today’s industrial sector. These tools enable data ingestion, exploration, and visualization at scale. However, the inherent complexity of the most effective, advanced analytics techniques — such as machine learning (ML) — presents barriers to widespread adoption. Emerging in response are low-code and no-code solutions, such as TrendMiner. Verdantix defines these as platforms that let decision-makers easily manage analysis and visualization applications with minimal coding. Such platforms make extracting insights from industrial data more accessible.

The Rise of Low-Code and No-Code Solutions in Industrial AI Analytics

Low-code and no-code technologies originated with Xerox’s 1973 Alto, introducing the concept of point-and-click and the graphical user interface (GUI). The internet’s rise brought about web-based development environments supporting more collaborative, scalable, and flexible software products.

Within the industrial sector, numerous firms have embraced low-code technologies to boost operational excellence. TrendMiner was founded in 2008 as part of a low-code IoT, ML, and cloud-driven revolution in the way data-heavy and asset-heavy industries approach analysis and decision-making.

Engineers use TrendMiner Next Generation

Such self-service data and analytics platforms provide a user-friendly GUI that allows decision-makers to create, deploy, and maintain data applications with minimal coding knowledge. This approach not only democratizes data analytics by making it accessible to a broader range of professionals, but it also expedites deployment and scalability of ML-driven historical time-series data analysis and even real-time process and asset monitoring.

TrendMiner’s Role in Democratizing Industrial AI Analytics

TrendMiner delivers solutions exemplifying how low-code solutions enhance AI-driven analytics deployment in industrial facilities. Its platform offers pre-built connectors to integrate with existing data systems, such as process historians and ERP. It delivers robust data contextualization, transformation, and GUI-driven analysis.

Meanwhile, specialty chemicals firm Clariant leveraged TrendMiner’s platform to reduce batch cycle times. Data scientists at Clariant created machine learning models in TrendMiner’s MLHub, and engineers were able to use them to gather relevant information about batches. As a result, Clariant engineers were able to improve batch cycle times by 10% and decrease energy consumption by 9%.

“Using TrendMiner, we can bring different people with different cultures, different backgrounds together and work on one project. We can view three sites on one screen in TrendMiner, which wasn’t possible before because they were in different silos. And this helps us make a stronger golden fingerprint for production. With TrendMiner, we can decrease the amount of raw material and decrease the cycle time of batches. That was the main takeaway during our trial, and what really drove people to use it afterward. They could see with their own eyes that it is faster.”

Similarly, TrendMiner’s MLHub facilitates collaboration between subject-matter experts and data scientists to operationalize industry-proven time series ML models in an environment catering to both the avid Python developer and the non-programmer process engineer. This capability not only accelerates the time-to-value for impact-scaled AI projects but also empowers non-technical users to contribute to AI-driven initiatives. Capabilities offered by vendors such as TrendMiner means ubiquitous time series data is used to make fast, well-informed decisions.

By combining TrendMiner with generative AI, engineers and data scientists can produce snippets of Python code or even complete machine learning models.

Verdantix firmly believes generative AI is set to transform industrial operations by automating mundane tasks, extracting structured data from documentation, and facilitating a natural language interface to API-level powerful software tools. TrendMiner exemplifies this with forward-thinking initiatives to include generative AI capabilities that assist engineers, data scientists, and maintenance teams with daily tasks.

Looking ahead, we see Industrial AI & Analytics software vendors exploring further innovative applications of generative AI. This could include enabling a role- and task-aware documentation search feature and conversational copilot-style support across multimodal (documents, visual, and time series) data sets. These advancements are expected to further streamline operations and improve decision-making processes in industrial facilities.

The Future of Industrial AI & Analytics

TrendMiner continues to play a pivotal role in shaping the way brownfield and greenfield industrial facilities build and deploy analytics. By harnessing the power of low-code platforms, the Industrial AI & Analytics platform not only simplifies the complexity of industrial data analysis but also paves the way for future innovations, such as autonomous manufacturing. TrendMiner, alongside the IoT capabilities offered by its new owner Proemion, is expected to play a significant part in the Industrial AI & Analytics market, which is forecasted to grow at a CAGR of 24% to reach $5 billion in 2028.

About Joe Lamming

Industry Analyst, Verdantix

Joe Lamming is an Industry Analyst in the Verdantix Operational Excellence practice. His research includes industrial DataOps, AI/ML analytics, and the applications of generative AI for industry and enterprise. Prior to joining Verdantix, Joe worked in the consumer electronics industry. There, he gained experience in overseas manufacturing, product design, and data science. Joe has a master’s in Mechanical Engineering and Sustainable Energy Systems from the University of Southampton.

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