Customers ask APERIO!


1. What is data integrity?

Data integrity ensures the accuracy and reliability of operational data across your industrial data value chain. It is the necessary software layer between the source of the data and the applications of that same data. Without accurate, trusted data, any function that uses this data is worthless and any operational or business decisions based on this data are unreliable. Learn more.

2. Is data integrity and data quality the same?

No. Although the terms are used synonymously or interchangeably, at APERIO we define data integrity as having four elements:

Data Quality: Ensure validated, reliable, clean data across the enterprise, as measured by DQI
Data Security: Identify security threats on equipment through asset data visibility in real time
Data Intelligence: Empower operators to trust the data and make better decisions with confidence
Data Value: Achieve sustainability and business goals (via optimization, predictive modeling, AI, etc.) based on validated data

APERIO offers more than just a data quality or asset health check. We ensure your data is accurate, reliable, and complete, before using it in any application. See Applications below to learn more.

3. Isn't inaccurate or unreliable data just a case of bad data entry?

The source of inaccurate or unreliable data is not necessarily the result of entering data into a system without controls. Beyond bad data, the quality of that data could also be in question. For example, it could be the structure of the data, different units of measurement, data that’s out of range, or abrupt changes to the data, to name a few. Review APERIO’s machine learning engines to see all types of anomalies it can detect.

4. What’s the top challenge companies encounter when they try to solve data quality issues on their own?

Companies have failed solving data integrity issues for decades because it’s such a huge problem. It’s just too difficult to automatically detect anomalies at scale and in real time while preventing false positives. We asked customers, with all the available technology today, why they can’t do it. They say it’s too expensive to manually manage and sensitize 1M–2M tags. And we don’t have the resources to do this at scale.

5. How do you measure data integrity/quality?

At APERIO, we calculate data quality by the Data Quality Index (DQI), which is comprised of six attributes:

6. What does the DQI measurement tell me?

DQI is a measure of the quality of your data according to the 20+ engines looking for anomalies in your data. The tool will alert you to the DQI of each data tag according to its severity. The end user can drill deeper into the data to get insight and recommended actions to correct. You can also prioritize actions according to their impact on performance. A DQI of 47% means that, for the timeframe in question, only 47% of the samples were of ‘good quality’; 53% of the time there were events on the signal.


7. My organization already has an industrial historian and visualization dashboards. Why do we need APERIO DataWise?

Most asset-intensive organizations use historians to acquire and store operational data, and visualization tools to contextualize it. However, historians alone cannot guarantee the authenticity of that data, nor can dashboards assure operators they can trust the data with any degree of confidence. Thus, the need persists for a solution that can guarantee data accuracy for every application, and every role, across the industrial data value chain.

8. What projects are most at risk without strong data quality?

Any application of data, from the control room to the corporate office, is at risk if the quality of operational data is poor. This could be an operator’s inability to assess the priority of alarms to make data-driven decisions in real time. Or good data upon which data scientists can build advanced models or AI tools to improve performance. See who needs APERIO DataWise.

9. Where can APERIO DataWise be applied?

Because APERIO DataWise is needed wherever operational data is stored or used, the use cases are numerous, including:

We have customers that use APERIO DataWise in a wide range of industries including oil & gas, chemicals, power & renewables, mining, food & beverage, pulp & paper, and pharmaceuticals, Use the links to view specific use cases in each industry. Learn more.

10. Does APERIO only look at the quality of sensor data?

No, APERIO can determine the DQI of all operational data, including process data, lab quality data, profile data, maintenance data, SCADA data, MES data, and ERP data. Any data stored in a historian, times series database, and/or data lake. See how International Paper is using APERIO DataWise for PI to assess the DQI of its PI historian data.

11. Is APERIO DataWise only applicable for data stored in OSISoft’s PI historian?

No. APERIO DataWise is completely agnostic when it comes to data source. It can connect to operational data stored in process historians, time series databases, data lakes, technologies, and platforms. OSISoft PI was selected as the first historian-based product, APERIO DataWise for PI, because of its wide use across our customer base.

Technical Specifications

12. What types of anomalies can APERIO detect?

APERIO currently offers 19 anomaly detection engines per these explanations:

13. How does APERIO DataWise work?

Patented Machine Learning defines normal time series behavior based on historical data via the ‘Fingerprinting’ process.

  1. Automatically builds the digital ‘fingerprint’ for each data input.
  2. Unsupervised ML engines monitor single and multi-sensor time series data streams.
  3. Anomaly Detection engines process live data streams and assesses authenticity against individual fingerprints.
  4. A fingerprint mismatch generates an alert in real time, pinpointing the problematic input and indicating the root cause of anomaly.
14. Is APERIO’s machine learning supervised or unsupervised?

By default, APERIO DataWise’s machine learning engines are unsupervised. This allows for a clean view of the data without added assumptions, predefined rules, or best practices. However, as you become more familiar with your process, you can make the engines more supervised per specific priorities or inherent knowledge. This can make the tool more intelligent according to your needs.

15. What’s the difference between APERIO DataWise and APERIO DataWise for PI?

APERIO DataWise for PI is a subset of machine learning anomaly detection engines specifically for identifying missing, stale, or bad data stored in PI historians. Learn more.

16. What types of anomalies can APERIO DataWise for PI detect?

As someone responsible for the proper operation of PI servers, you know that poor data integrity must be continuously addressed. Your challenges may include missing data, stale or dead tags, bad data, connection dropped/data buffering, and/or data out of range.

Unlock the value of superior data

The future of your industrial data value chain is at your fingertips. Improve data accuracy, security, and value, for smarter business decisions based on real-time, trusted, superior data.

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