dataPARC Industrial Data Platform
Frequently Asked Questions
dataPARC provides complete industrial data management, historian, visualization, and analytics, in one integrated platform for manufacturing and process industries. This comprehensive FAQ addresses the most common questions about capabilities, integration with existing systems, migration from legacy platforms like OSIsoft PI, deployment options, and implementation. These answers provide the technical and practical clarity you need to understand how dataPARC can transform your operations from data collection through analysis and reporting.
Platform Overview & Capabilities
dataPARC is used to collect, visualize, analyze, and report on industrial process data. Teams use it for troubleshooting, performance monitoring, reporting, compliance, and analytics across operations.
dataPARC offers a suite of industrial software products designed to help manufacturers collect, visualize, and analyze their process data.
dataPARC Historian: A high-performance time-series historian built for modern manufacturing environments. It stores and retrieves process data at scale, serving as the foundation for analytics, reporting, and AI-ready data infrastructure.
PARCview: A powerful visualization and analytics platform for time-series data that gives engineers and operators the ability to build displays, trend data, run reports, and perform advanced analysis directly from their process historian data.
Modeling (PARCmodel): An advanced analytics tool for building soft sensors and predictive models using historical process data. It allows manufacturers to gain deeper insight into their processes without additional instrumentation.
PARCview Nexus: The web-based version of PARCview, giving users access to their displays, trends, and process data from any device and any location.
dataPARC has two main products that together make a complete industrial data platform. These two products include the dataPARC historian and PARCview, which is an advanced visualization and analytics platform for time-series data. Unlike tools that specialize in only one area, or having to pick and choose analytics products, dataPARC Tools provides trending, dashboards, and reporting without requiring separate modules or products.
dataPARC is most commonly used in continuous and hybrid process industries, including pulp & paper, power generation, utilities and water/wastewater, food and beverage, chemicals and petrochemicals, mining and metals, oil and gas, batch, and fertilizer manufacturing. However, it can be adapted for discrete systems as well. Please request a demo to see how dataPARC can best fit for your site.
Yes, end users can develop their own dashboards using dataPARC’s intuitive interface. The platform is designed for operations and engineering teams to create and modify displays without requiring programming skills or involving IT. Training is available for operators, engineers, and administrators, and all training materials belong to the customer for internal reuse.
AI initiatives in industrial operations are only as good as the data feeding them. For many manufacturers, the biggest barrier to adopting AI isn’t the technology itself, it’s having process data that is clean, accessible, and structured enough to be useful.
dataPARC addresses this at the foundation. By connecting directly to plant-floor data sources across OT systems: historians, SCADA, DCS, and control systems. dataPARC consolidates process data into a single, accessible layer that IT and data science teams can work with. This bridges the gap between operational technology and the modern data infrastructure AI applications require.
Specifically, dataPARC supports AI initiatives by:
- Providing a reliable, high-quality data historian that captures and stores process data at the resolution and accuracy AI models need
- Enabling IT/OT convergence by making OT data accessible to enterprise systems, cloud platforms, and third-party analytics tools via REST API, SQL, and SDK integrations
- Delivering real-time visibility through PARCview and Nexus, so insights from AI models can be acted on at the operator level
Whether your team is just beginning to explore AI or actively building out a data infrastructure to support it, dataPARC provides the process data foundation that makes those initiatives possible.
Data Collection & Integration
dataPARC connects using standard industrial interfaces including OPC UA and OPC DA, native historian connectors, SQL databases, flat files and CSVs, and REST APIs. It is vendor-agnostic and works in mixed control environments.
dataPARC supports OPC (OPC UA and OPC DA), ODBC for database connections, REST APIs for web services integration, native connections to major historians, and flat file imports including CSV. This flexibility allows dataPARC to integrate with virtually any industrial data source.
Yes. dataPARC connects to enterprise systems through standard interfaces like ODBC, REST APIs, and database connections. This allows integration with ERP systems for production context, CMMS platforms for maintenance data, and LIMS systems for laboratory results alongside process data.
Yes. dataPARC can backfill historical data from existing historians like OSIsoft PI, Aspen IP.21, Honeywell PHD, and GE Proficy. This allows you to consolidate historical records when migrating or to maintain continuity when implementing dataPARC alongside existing systems.
dataPARC provides real-time data access with sub-second collection rates. The platform can capture and store data at frequencies matching your process dynamics, from milliseconds to minutes, ensuring you never miss critical process events while efficiently managing storage through intelligent compression.
Yes. dataPARC includes manual data entry for operator inputs, lab results, quality checks, and inspections. All entries are timestamped and auditable, making it useful for quality and compliance workflows where manual and automated data need to coexist.
Historian Integration & Migration
Yes. dataPARC can pull data from existing historians such as OSIsoft PI, Aspen IP.21, Honeywell PHD, and GE Proficy, allowing teams to modernize visualization and analytics without replacing everything at once.
Yes. dataPARC can fully replace PI Historian, PI Vision, and ProcessBook, or it can be deployed alongside PI during a phased migration. Many customers start by replacing PI Vision and ProcessBook first, then migrate the historian later.
dataPARC connects directly to PI to access historical & real-time data, as well as Asset Framework content. Organizations can use dataPARC for visualization and analytics while maintaining PI as the historian or gradually migrate historian functions to dataPARC over time. dataPARC can read from multiple PI servers simultaneously for enterprise-wide visibility.
Migrations can be phased or complete. Most customers start by replacing PI Vision and ProcessBook with dataPARC’s visualization while continuing to use PI as the historian. This is because ProcessBook has reached end-of-life. We have a PI migration utility that allows customers to transfer their PI displays into PARCview displays so the work does not have to be replicated. Once comfortable, they migrate data collection to dataPARC’s historian and backfill historical PI data. This approach minimizes disruption while demonstrating value quickly.
Analytics & Calculations
Yes. dataPARC includes a calculation engine that allows you to create virtual tags derived from existing measurements. These calculated tags can perform mathematical operations, statistical functions, and logical evaluations, appearing alongside raw data for trending and analysis.
Yes. dataPARC’s Run Browser provides similar functionality to PI event frames and Seeq capsules, allowing you to define, search, and analyze time-based events like batches, production runs, or process conditions. This makes batch-to-batch comparisons and contextual analysis straightforward.
dataPARC includes advanced visualization out of the box: real-time and historical trending, customizable dashboards and graphics, SPC and statistical charts, alarm and event visualization, and Excel-based reporting. No separate modules are required.
Data Export & Integration with Analytics Tools
Yes. dataPARC provides native Excel integration for both ad-hoc data exports and automated report generation. Users can pull dataPARC data directly into Excel for custom analysis or schedule reports that generate automatically. For those familiar with PI DataLink, the dataPARC Excel add-in functionality is similar.
Yes. dataPARC provides data access through REST APIs, SQL interfaces, and ODBC connections that enable integration with Power BI, Tableau, Python, R, and other analytics platforms. This allows data scientists and analysts to leverage dataPARC’s historian data in their preferred tools.
Deployment & Infrastructure
dataPARC is customer-hosted, either on-premises, in a customer-owned private cloud (Azure or AWS), or in a hybrid architecture. dataPARC does not host customer data, giving you complete control over your information.
Yes. dataPARC supports redundant server configurations for high availability. Store-and-forward capabilities ensure no data loss during network disruptions, and redundant architectures protect against hardware failures in mission-critical environments.
Customers provide their own servers and networking. dataPARC supplies specifications and can work with customer IT teams or local integrators to ensure proper sizing and configuration for your tag count and performance requirements.
Yes. PARCview Nexus provides browser-based access to dashboards and trends and works on desktops, tablets, and mobile devices without requiring separate applications or licenses.
Licensing & Implementation
dataPARC is most commonly licensed by tag count, not by user. This means unlimited users, unlimited dashboards and reports, and no per-user or per-client fees. We feel the best run plants shouldn’t decide who needs the tools to enable better decisions.
dataPARC can scale across a wide variety of customer sizes. From small systems in the 1000 tag range to large systems with millions of tags, dataPARC can meet your needs.
Once we get started, projects can go very quickly. In cases where an existing Historian is in place, we can have initial users going in the first week. If it’s greenfield Historian, data collection and storage can also start in the first week or two. The timeline depends on hardware readiness, network access, tag count, and project scope.
No. Most implementations are done remotely. Onsite support is optional if required by the customer.
Why Choose dataPARC
Common reasons include rising PI licensing and maintenance costs; ProcessBook or other systems reaching end-of-life; heavy reliance on Excel for reporting; difficulty integrating manual and automated data; and the need for open access to data for analytics and AI initiatives.
Typical next steps include a demo, tag count and scope validation, and a commercial proposal.