Industrial DataOps #6 with Splunk - Joel Jacob on the road from Cyber Security to Sensor Data

Welcome to Episode 6 of our Industrial DataOps podcast series. Today, we’re diving into a conversation with Joel Jacob, Principal Product Manager at Splunk, about the company’s growing focus on OT, its approach to industrial data analytics, and how it fits into the broader ecosystem of industrial platforms.

Splunk is a name that’s well known in IT and cybersecurity circles, but its role in industrial environments is less understood. Now, as part of Cisco, Splunk is positioning itself at the intersection of IT observability, security, and industrial data analytics. This episode is all about understanding what that means in practice.

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From IT and Cybersecurity to Industrial Data

Joel’s journey into Splunk mirrors the company’s shift into OT. Coming from a background in robotics, automotive, and smart technology, he initially saw Splunk as a security and IT analytics company. But what he found was a growing demand from industrial customers who were already using Splunk for OT use cases.

"A lot of customers had already started using Splunk for OT, and the company realized it needed people with industrial experience to support that growing demand."

Splunk has built its reputation on handling log data, security monitoring, and IT observability. But as Joel explains, industrial data has its own challenges, and Splunk has had to adapt.

How Splunk Fits into the Industrial Data Platform Capability Map

To make sense of where Splunk fits, we look at our Industrial Data Platform Capability Map—a framework that defines the core building blocks of an industrial data strategy.

Splunk’s Strengths:

* Data Storage and Analytics: This is where Splunk is strongest. The platform can ingest, store, and analyze massive amounts of data, whether it’s sensor data, log files, or security events.

* Data Quality and Federation: Splunk allows companies to store raw data and extract value dynamically, rather than forcing them to clean and standardize everything upfront. Its federated search capabilities also mean that data doesn’t have to be centralized—a key advantage for IT/OT integration.

* Visualization and Dashboards: With Dashboard Studio, Splunk provides modern, customizable visualizations that stand out from traditional industrial software.

Where Splunk is Expanding:

* Connectivity and Edge Computing: Historically, getting industrial data into Splunk required external middleware. But in the last 18 months, the company has introduced an edge computing device with built-in AI capabilities, making it easier to ingest and process OT data directly.

* Edge Analytics and AI: The Splunk Edge Hub enables local AI inferencing and analytics on industrial equipment, addressing latency and connectivity challenges that arise when relying on cloud-based models.

Joel sees this as a natural evolution:

"We know that moving all industrial data to the cloud isn’t always practical. By adding edge computing capabilities, we make it easier for OT teams to process data where it’s generated."

A Real-World Use Case: Energy Optimization in Cement Manufacturing

One of Splunk’s key industrial customers, Cementos Argos, is a major cement producer facing a common challenge—high energy costs and carbon emissions.

The Problem:

* Cement manufacturing is one of the most energy-intensive industries in the world.

* The company needed a way to optimize kiln operations while ensuring consistent product quality.

* Traditional manual adjustments were slow and lacked real-time visibility.

The Solution:

* The company ingested data from OT systems into Splunk.

* Using the Machine Learning Toolkit, they built predictive models to optimize kiln temperature and pressure settings.

* These models were then pushed back to PLCs, allowing automated process adjustments.

The Results:

* $10 million in annual energy savings across multiple sites.

* The ability to push AI models to the edge reduced response times by 20%.

* Operators could now trust AI-generated recommendations, while still overriding changes if needed.

"The combination of machine learning and real-time process control created a true closed-loop optimization system."

Federated Search: A Different Approach to Industrial Data

One of Splunk’s unique contributions to industrial data management is federated search. Unlike traditional platforms that require all data to be centralized, Splunk allows companies to analyze data across multiple sources in real-time.

Joel explains the shift in thinking:

"Most industrial data strategies assume you need a single source of truth. But in reality, data lives in multiple places, and moving it all is expensive. With federated search, we can analyze data wherever it resides—whether it’s on-prem, in the cloud, or at the edge."

This is a major departure from the “data lake” approach that many industrial companies have pursued. Instead of trying to move and harmonize all data upfront, Splunk’s model is about leaving data where it makes the most sense and analyzing it dynamically.

How IT and OT Collaboration is Changing

Bridging the IT/OT divide has been a theme across this podcast series, and Splunk’s approach to security and data federation provides a unique perspective on this challenge.

Joel shares some key insights on what makes collaboration successful:

* Security is often the bridge. Since IT teams already use Splunk for security monitoring, they are more open to OT data integration when it’s part of a broader cybersecurity strategy.

* OT needs tools that don’t slow them down. Engineers don’t want to wait for IT approval to test new models. That’s why Splunk’s edge device was designed to be easily deployable by OT teams.

* The next generation of engineers is more IT-savvy. Younger engineers entering the workforce are more comfortable with IT tools and cloud environments, making collaboration easier.

One of the most interesting points was how Splunk leverages its Cisco partnership to expand into OT environments:

"Cisco has an enormous footprint in industrial networking. By running analytics on Cisco switches and edge devices, we can make OT data integration seamless."

The Role of AI in Industrial Data

Like many companies, Splunk is exploring the role of AI and generative AI in industrial environments. One of the most promising areas is automating data analysis and dashboard creation.

Joel shares how this is already happening:

* AI-generated dashboards: Engineers can simply describe what they want in natural language, and Splunk’s AI generates the necessary queries and visualizations.

* Low-code model deployment: Instead of manually writing Python scripts, users can export machine learning models with a single click.

* Multimodal AI: By combining sensor data, image recognition, and sound analysis, AI models can detect patterns that human operators might miss.

"In the next few years, AI will make it dramatically easier to analyze and visualize industrial data—without requiring deep programming expertise."

Final Thoughts

Splunk’s journey into OT is a great example of how traditional IT platforms are adapting to the realities of industrial environments. While the company’s core strength remains in data analytics and security, its expansion into edge computing and OT integration is opening up new possibilities for manufacturers.

If you want to learn more about how Splunk is evolving in the OT space, check out their website: www.splunk.com.

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