FLEETWIDE DATA VALIDATION

Automate data quality continuously and at scale

Streamline Data Quality

Low quality data is often seen as unavoidable, requiring large amounts of data to be collected and significant resources to correct. Imagine the impact the capability to automatically measure, track, and improve data quality could have on your fleet of assets. Done at scale and in real-time.

DataWise Your Operational Data

You can’t improve what you don’t measure.

Boost DQI

Drive DQI higher to reduce overall business risk.
[Target > 90%]

Reduce # Bad Tags

Lower # bad tags ensures reliable data fo end user apps. [Target <0.5%]

Shorten Event Duration

Shorten duration to reduce the risk to assets.
[Target < 1 min.]

Reduce Severity

Reduce the event severity to increase overall operational performance. [Target < 10%.]

Automate Data Quality Continuously and at Scale.

Use APERIO DataWise to automatically measure, improve, and track data quality continuously and at scale.

Improve Data Quality at Scale

Achieving high data quality enterprise-wide can optimize business value as customers can reduce resources and time spent cleaning data while reducing risk. Key metrics and smart workflows provide actionable insights for immediate remediation.

Guarantee the Quality of PI Data

Continuously monitoring PI data can deliver more centralized visibility while eliminating bad data. Addressing data issues at the source keeps people and processes running and in good health.

Complete the Digital Twin

Numerous groups working on a digital twin can cause incomplete, inaccurate data. Connecting to your digital twin and checking for data anomalies before taking it into operations can drive higher uptime with less variability.

Increased Visibility in Operations

Taking your digital twin into operations while connected to APERIO DataWise ensures all process data is of the highest quality in real time. Leverage workflows to consolidate production, maintenance, and analytics views into one.

The thing I’m most excited about with the ability to have a metric of data quality; something that we’re tracking, over time to see if we’re improving.

– VP, Energy Company

Case Study: Early Notification of Data Loss

Multiple users manage AVEVA PI historians globally. While working to streamline and aggregate industrial data, they manually moved interfaces and archives. This manual work unknowingly caused numerous configuration errors.

With hundreds of interfaces, the errors weren’t noticed until APERIO DataWise identified these in the heatmap.

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