Breaking Silos: Pairing InfluxDB 3 with Your Historian for Better Insights

By Allyson Boate / Developer
May 28, 2025

Industrial systems constantly generate time series data—streams of time-stamped values like temperature, flow rate, vibration, or power load. This data powers real-time monitoring, performance tracking, and long-term forecasting across critical infrastructure, energy systems, and manufacturing environments.

To manage this flow, many organizations use historians: local systems designed to collect, store, and surface time series data from PLCs, SCADA platforms, and edge computing devices. Historians are great for fast, local data collection, but present challenges when it comes to long-term analytics, cross-platform integration, and scalability. As data needs grow, historian systems often become both costly and isolated. Their restrictive licensing, limited scalability, and integration barriers reduce data access, slow response times, and make it harder for businesses to scale or make informed decisions. In response to these challenges, InfluxDB 3 works alongside your historian to enable scalable data sharing, real-time analysis, and long-term optimization without vendor lock-in. With object storage and SQL support, InfluxDB offers open data access at a fraction of the cost, connecting local systems to cloud-based services and enterprise tools, enabling seamless integration for better scalability, analysis, and operational efficiency.

To fully understand the value of this pairing, we need to explore a common challenge: data silos.

What are data silos?

Data silos are isolated pockets of data that stay locked within specific systems, tools, or departments—often without visibility or access across the organization. They form when teams adopt technologies that solve local problems but don’t integrate with a broader data strategy. Over time, these systems create barriers that limit collaboration, obscure insights, and slow decision-making.

Data silos block collaboration, hide critical insights, and slow down decision-making. Without a connected view, teams can’t detect system-wide issues, align on performance goals, or respond quickly to changing conditions. Breaking down silos means connecting data sources so everyone has the context they need to act smarter and faster.

How data silos hurt performance

Limited Analytics

Disconnected systems block system-wide visibility. Take a regional water utility provider. Each treatment plant may use its own historian system to log pump performance and flow rate data. But without integration between these systems, operators can’t see issues unfolding across multiple facilities. A team at one plant might respond to a pressure surge without realizing that the same problem occurred at another location the previous day. Lacking that shared context, teams troubleshoot in isolation, leading to higher energy costs, repeated investigations, and missed chances to uncover systemic problems.

Slow Insights

Silos delay decisions. One energy provider faced this challenge after modernizing more than 2,000 substations. Their legacy AVEVA Wonderware system couldn’t support real-time data at scale, forcing teams to rely on manual .csv exports and weeks-long analysis cycles. Without timely insights, they couldn’t catch outages early, adjust to surging demand, or avoid wider grid disruptions.

During peak events—such as summer heat waves—control centers need a clear, real-time view across the entire grid to prevent overloading, reroute power efficiently, and avoid blackouts. By pairing InfluxDB with their existing infrastructure, the organization now supports real-time visibility across the grid and has begun developing machine learning models to predict outages and maintenance needs before they escalate. Real-time data only makes an impact when it’s accessible, shareable, and trusted across the system, when it counts most.

Duplicated Effort

Teams often duplicate tasks when they don’t have access to shared data. Imagine a large automotive manufacturing plant where the maintenance team records vibration readings using a spreadsheet system. Meanwhile, the engineering team collects the same data for diagnostics but stores it separately in a proprietary tool. Since neither team sees the other’s records, they both end up collecting and analyzing the same data, just in different ways. This duplication wastes time, increases storage costs, and creates confusion over which dataset is the most accurate or up-to-date.

Restricted Forecasting

Forecasting tools depend on long-term, unified data to spot trends and anticipate issues. In oil and gas operations, pipeline monitoring stations along a transmission network often log pressure and flow rate data locally. Each site may store its data in a separate historian system with no shared access across locations. When that data remains siloed, analysts working at a central operations center can’t piece together the full picture. They miss early warning signs of pipeline fatigue or pressure irregularities that only show up when comparing performance across sites. That lack of visibility results in surprise failures, emergency repairs, and costly downtime, all of which you can avoid with centralized, time-stamped data that supports long-range forecasting.

Why pair a time series database with a historian?

Historians and SCADA systems collect and store process data to monitor and control local operations, but they often fall short when businesses need broader insights, deeper analysis, and strategic oversight. Pairing them with a time series database like InfluxDB 3 bridges that gap by making operational data accessible, scalable, and actionable.

InfluxDB 3 turns static, isolated data into a dynamic business asset. For example, a global food and beverage company can unify sensor readings from multiple factories to identify energy-saving opportunities. A renewable energy provider can merge local turbine performance logs into a central dashboard to optimize output across a wind farm. A transportation network can combine rail sensor data from across the country to predict maintenance needs and prevent service disruptions.

This combination empowers companies to:

• Unlock enterprise-wide analytics: Compare trends across plants, systems, or regions.
• Enable AI and automation: Feed unified data into advanced models for smarter automation.
• Scale efficiently: Store years of detailed data affordably with object storage.
• Make faster, data-informed decisions: Act on real-time and historical views of operations.

How InfluxDB 3 connects and consolidates data

InfluxDB 3 breaks down data silos by connecting historian systems, edge devices, and cloud tools into a unified, enterprise-wide data infrastructure. This architecture enables:

• Consolidation of disparate systems into one central, queryable platform supporting cross-functional collaboration and data consistency
• Seamless streaming from the edge using Telegraf, which allows real-time data to flow from remote assets into centralized storage
• Scalable long-term storage using Apache Parquet in object storage, reducing costs while maintaining high-resolution data
• Combined real-time and historical access, providing immediate insights and long-term analysis for trend detection, forecasting, and machine learning applications

By creating a cohesive data layer, InfluxDB 3 ensures that information is accessible when and where needed, reducing delays, eliminating data gaps, and laying the foundation for smarter, faster operations.

How to partner InfluxDB 3 with your historian

Pairing InfluxDB 3 with your historian is straightforward and scalable:

• Set your deployment model: Use InfluxDB Cloud for a managed solution or InfluxDB Enterprise for on-prem or hybrid needs.
• Install Telegraf: Deploy the agent to collect data from your historian, PLCs, or edge systems.
• Ingest and store: Stream data to InfluxDB 3, where it’s organized in Parquet format for fast querying and long-term retention.
• Query and visualize: Use SQL to explore trends, monitor KPIs, or feed dashboards, AI models, and business systems.

This approach enables full visibility into your operations without overhauling existing infrastructure.

The bottom line

Partnering InfluxDB 3 with your historian turns localized, hard-to-access data into a unified source of truth. By blending the historians’ real-time strengths with InfluxDB 3’s scale, flexibility, and analytics capabilities, companies gain a strategic edge.

Eliminating data silos unlocks greater organizational visibility, improves collaboration between teams, and accelerates access to critical insights. By removing barriers between systems and departments, companies can consolidate data streams, avoid duplicate efforts, and respond to issues with greater agility. Whether in energy, utilities, manufacturing, or logistics, breaking down silos supports predictive maintenance, optimized resource allocation, and smarter decision-making across operations. The result is a more resilient, efficient, and insight-driven enterprise.

Ready to get started?

Whether you want to improve visibility, cut costs, or prepare for AI initiatives, InfluxDB 3 is the missing link between your historian data and business growth.

Ready to get started? Contact the InfluxData team for guidance or begin exploring with a free download of InfluxDB 3 Core OSS or InfluxDB Enterprise.