Why Industrial Data Needs Advanced Analytics
Industrial teams often have access to real-time and historical operations data, but visibility alone does not always explain what is likely to happen next. Dashboards can show current conditions, trends, and alarms, but advanced analytics helps move the conversation from what happened to what is changing, what may happen, and what action should be taken.
AVEVA Advanced Analytics is built for this next step. As a no-code, cloud-based SaaS solution, it uses AI and machine learning to transform industrial operations data into actionable insights. The solution helps teams generate real-time predictive and prescriptive insights, detect anomalies, and support corrective recommendations related to production efficiency, asset performance, quality, energy efficiency, and throughput.
What Is AVEVA Advanced Analytics?
AVEVA Advanced Analytics is a cloud-native, no-code SaaS solution designed to help industrial teams turn raw operations data into actionable insights. Instead of requiring teams to build custom analytics models from scratch, the solution applies AI and machine learning to industrial data so users can detect anomalies, understand process deviations, and receive corrective or prescriptive recommendations.
The solution is closely connected to AVEVA’s broader industrial data ecosystem and can work with CONNECT, AVEVA PI Data Infrastructure, and AVEVA Operations Control to support real-time predictive analytics. Through CONNECT data services, teams can aggregate operational data from PI Servers, legacy historians, edge devices, and event streams such as PI Event Frames into a cloud environment, while REST and GraphQL APIs can support data access and integration with downstream applications and analytics workflows. This connected approach helps teams use Advanced Analytics to move beyond monitoring operational data by predicting process outcomes, identifying quality or energy issues, and supporting better decisions around throughput, sustainability, and asset performance.
How AVEVA Advanced Analytics Works
AVEVA Advanced Analytics helps industrial teams move from operational data to predictive intelligence by organizing the machine learning workflow around a few basic building blocks. The platform includes other capabilities as well, but the core foundation starts with data sources, twins, threads, and models. A guided wizard also supports users through the machine learning process, helping teams build and operationalize analytics without starting from a blank technical workflow.
Key building blocks include:
- Data sources: Data sources provide the operational information used for analysis, including time-series history, process data, asset context, events, and other relevant data made available through CONNECT.
- Twins: Twins provide a structured representation of the asset, process, equipment, or operating area being analyzed, helping users organize analytics around the real industrial environment.
- Threads: Threads connect related data, calculations, and process relationships so users can follow how different variables and conditions influence performance over time.
- Models: Models apply machine learning to the selected data and context, helping teams learn normal operating behavior, identify abnormal patterns, generate predictions, and support recommendations.
Together, these building blocks help teams connect industrial data with machine learning models, monitor process behavior, visualize results, and use predictive insights to support decisions around quality, throughput, energy efficiency, and asset performance.
Key Use Cases for AVEVA Advanced Analytics
AVEVA Advanced Analytics is positioned around practical operating outcomes, not only data science experimentation. Key use cases for AVEVA Advanced Analytics include predictive quality, predictive throughput, and predictive energy efficiency, all focused on helping teams detect process conditions earlier and act before losses, inefficiencies, or quality issues occur.
For quality, AVEVA describes Advanced Analytics as a way to use AI and machine learning to identify process patterns that may lead to off-spec production. This supports corrective insights and quality recommendations, helping teams reduce waste and improve production outcomes. For throughput, the solution is positioned to uncover opportunities for process optimization and provide real-time recommendations that support production rate improvements. For energy efficiency, AVEVA states that Advanced Analytics can identify patterns and conditions that affect energy use and help determine better operating conditions to reduce energy consumption.
Why Data Infrastructure Matters Before Advanced Analytics
AVEVA Advanced Analytics relies on industrial data that is trusted, organized, contextualized, and ready for analytics, which makes the underlying data foundation a critical part of the overall solution. CONNECT supports this foundation by serving as AVEVA’s industrial intelligence platform, bringing together industrial data, models, applications, AI, and analytics in a connected environment. By reducing information silos and improving access to operational context, CONNECT helps organizations create the conditions needed for faster decisions and more effective use of advanced analytics.
For organizations already using AVEVA PI System or AVEVA PI Data Infrastructure, Advanced Analytics can become a logical next layer in the industrial data strategy. PI System supports the collection, cleansing, storage, enrichment, and visualization of real-time operations data, while PI Data Infrastructure extends operations data management across the industrial edge, on-premises sites, cloud environments, and trusted external partners.
How MetaFactor Can Help
MetaFactor helps industrial organizations build the trusted data foundation needed to make advanced analytics practical, from connecting operational systems and improving historian structures to contextualizing asset data, integrating cloud-ready platforms, and preparing reliable information for AI-driven insights. As solutions such as AVEVA Advanced Analytics bring predictive recommendations closer to daily operations, MetaFactor can also help educate teams, demonstrate the solution, and align industrial data infrastructure with business goals so organizations can identify anomalies earlier, improve quality, optimize energy use, increase throughput, and support faster decision-making across production environments. Contact us to discuss how your industrial data environment can be prepared for advanced analytics or if you want to see a demo of AVEVA Advanced Analytics today.