Industrial DataOps #8 with InfluxData - Evan Kaplan on how Developer-Led Innovation is Reshaping Industrial Data

Welcome to Episode 8 of the IT/OT Insider Podcast. Today, we’re diving into real-time data, edge processing, and AI-driven analytics with Evan Kaplan, CEO of InfluxData.

InfluxDB is one of the most well-known time-series databases, used by developers, industrial companies, and cloud platforms to manage high-volume data streams. With 1.3 million open-source users and partners like Siemens, Bosch, and Honeywell, it’s a major player in the Industrial DataOps ecosystem.

Evan brings a unique perspective—coming from a background in networking, cybersecurity, and venture capital, he understands both the business and technical challenges of scaling industrial data infrastructure.

In this episode, we explore:

* How time-series data has become critical in manufacturing.

* The shift from on-prem to cloud-first architectures.

* The role of open-source in industrial data strategies.

* How AI and automation are reshaping data-driven decision-making.

Let’s dive in.

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From Networking to Time-Series Data

Evan’s journey into time-series databases started in venture capital, where he met Paul Dix, the founder of InfluxData.

"At the time, I wasn't a data expert, but I saw an opportunity—everything in the world runs on time-series data. Sensors, machines, networks—they all generate metrics that change over time."

At the time, InfluxDB was a small open-source project with about 3,000 users. Today, it’s grown to 1.3 million users, powering everything from IoT devices and industrial automation to financial services and network telemetry.

One of the biggest drivers of this growth? Industrial IoT.

"Over the last decade, we’ve seen a shift. IT teams originally used InfluxDB for monitoring servers and applications. But today, over 60% of our business comes from industrial IoT and sensor data analytics."

How InfluxDB Maps to the Industrial Data Platform Capability Model

We often refer to our Industrial Data Platform Capability Map to understand where different technologies fit into the IT/OT data landscape.

So where does InfluxDB fit?

* Connectivity & Ingest → One of InfluxDB’s biggest strengths. It can ingest massive amounts of data from sensors, PLCs, MQTT brokers, and industrial protocols using Telegraf, their open source agent.

* Edge & Cloud Processing → Data can be stored and analyzed locally at the edge, then replicated to the cloud for long-term storage.

* Time-Series Analytics → InfluxDB specializes in storing, querying, and analyzing time-series data, making it ideal for predictive maintenance, OEE tracking, and process optimization.

* Integration with Data Lakes & AI → Many manufacturers use InfluxDB as the first stage in their data pipeline before sending data to Snowflake, Databricks, or other lakehouse architectures.

"Our strength is in real-time streaming and short-term storage. Most customers eventually downsample and push long-term data into a data lake."

A Real-World Use Case: ju:niz Energy’s Smart Battery Systems

One of the most compelling use cases for InfluxDB comes from ju:niz Energy, a company specializing in off-grid energy storage.

The Challenge:

* ju:niz needed to monitor and optimize distributed battery systems used in renewable energy grids.

* Each battery had hundreds of sensors generating real-time data.

* Connectivity was unreliable, meaning data couldn’t always be sent to the cloud immediately.

The Solution:

* Each battery system was equipped with InfluxDB at the edge to store and process local data.

* Data was compressed and synchronized with the cloud whenever a connection was available.

* AI models used InfluxDB data to predict battery failures and optimize energy usage.

The Results:

* Improved energy efficiency—By analyzing real-time data, ju:niz optimized battery charging and discharging across their network.

* Reduced downtime—Predictive maintenance prevented unexpected failures.

* Scalability—The system could be expanded without requiring a centralized cloud-only approach.

"This hybrid edge-cloud model is becoming more common in industrial IoT. Not all data needs to live in the cloud—sometimes, local processing is faster, cheaper, and more reliable."

Cloud vs. On-Prem: The Future of Industrial Data Storage

A common debate in industrial digitalization is whether to store data on-premise or in the cloud.

Evan sees a hybrid approach as the future:

"Pushing all data to the cloud isn’t practical. Factories need real-time decision-making at the edge, but they also need centralized visibility across multiple sites."

A few key trends:

* Cloud adoption is growing, with 55-60% of InfluxDB deployments now cloud-based.

* Hybrid architectures are emerging, where real-time data stays at the edge while historical data moves to the cloud.

* Data replication is becoming the norm, ensuring that insights aren’t locked into one location.

"The most successful companies are balancing edge processing with cloud-scale analytics. It’s not either-or—it’s about using the right tool for the right job."

AI and the Next Evolution of Industrial Automation

AI has been a major topic in every recent IT/OT discussion, but how does it apply to manufacturing and time-series data?

Evan believes AI will redefine industrial operations—but only if companies structure their data properly.

"AI needs high-quality, well-governed data to work. If your data is a mess, your AI models will be a mess too."

Some key AI trends he sees:

* AI-assisted predictive maintenance → Combining sensor data, historical trends, and real-time analytics to predict failures before they happen.

* Real-time anomaly detection → AI models can identify subtle changes in machine behavior and flag potential issues.

* Autonomous process control → Over time, AI will move from making recommendations to fully automating factory adjustments.

"Right now, AI is mostly about decision support. But in the next five years, we’ll see fully autonomous manufacturing systems emerging."

Final Thoughts: How Should Manufacturers Approach Data Strategy?

For companies starting their Industrial DataOps journey, Evan has a few key recommendations:

* Start with a strong data model → Don’t just collect data—structure it properly from day one.

* Invest in developers → The best data strategies aren’t IT-led or OT-led—they’re developer-led.

* Think hybrid → Balance edge and cloud storage to get the best of both worlds.

* Prepare for AI → Even if AI isn’t a priority now, organizing your data properly will make AI adoption easier in the future.

"Industrial data is evolving fast, but the companies that structure and govern their data properly today will have a huge advantage tomorrow."

Next Steps & More Resources

Industrial DataOps is no longer just a concept—it’s becoming a business necessity. Companies that embrace scalable data management and AI-driven insights will outpace competitors in efficiency and innovation.

If you want to learn more about InfluxDB and time-series data strategies, visit www.influxdata.com.

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Disclaimer: The views and opinions expressed in this interview are those of the interviewee and do not necessarily reflect the official policy or position of The IT/OT Insider. This content is provided for informational purposes only and should not be seen as an endorsement by The IT/OT Insider of any products, services, or strategies discussed. We encourage our readers and listeners to consider the information presented and make their own informed decisions.

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