The industrial sector has undergone a digital transformation, emphasizing the Internet of Things (IoT) and other advanced technologies. As a result, predictive maintenance, enterprise manufacturing, artificial intelligence (A.I.) and operational technology (OT) are on the rise.
But such applications face a common issue that could hinder growth: the abundance of unreliable data stored in OT systems and cloud warehouses. Too often the data used in business intelligence (BI) dashboards are inaccurate, inconsistent and unreliable. To get the full potential from popular dashboards—such as overall equipment effectiveness and energy management—data needs to be checked and proofed for reliability.
Industrial manufacturers face a significant challenge that must be addressed to achieve the next level of A.I. and digital maturity. Analysts emphasize the need for companies to invest more in data infrastructure to ensure reliability and suitability for its intended purpose. First-generation applications typically trigger the development of second-generation infrastructure, including Industrial and IoT DataOps—and data quality management is a key consideration.
Industrial DataOps is a relatively new field that delivers the necessary productivity and efficiency to help enable A.I. The concept emphasizes the importance of data quality management that involves identifying and addressing data defects to ensure reliability. With careful monitoring, companies can avoid high-impact situations caused by defects and sensor data integrity issues.
There is a direct relationship between data defects and operational problems; poor data quality can lead to abnormal situations that can have a significant impact on operations. To avoid such situations, companies must invest in quality management. And high-quality data is crucial for driving and protecting the ROI of data. Companies that invest in data infrastructure, without considering its quality management, risk making such expenditures ineffective.
In an industrial context, companies must address three broad types of reliability issues: data quality, sensor data integrity and data that isn’t fit for purpose. Examples of data quality issues include missing metadata, compression, duplicated values and NaN (not a number) values.
Sensor data integrity issues are often caused by degradation, incorrect placement and broken or stuck instruments. Such problems can be detected by observing performance characteristics such as oscillations, drifts, interquartile range anomalies, staleness and improper calibration. Data unfit for purpose is identified by tracking information loss and aggregation errors. Left unchecked, these issues can significantly reduce reliability with data defects becoming a liability.
BI dashboards have become essential tools for companies to monitor their operations and make data-driven decisions. However, effectiveness is highly dependent on data reliability. In many cases, dashboard data is subjected to aggregation, segmentation, summarization, grouping and averaging, ballooning the impact of defects.
As a result, users often don’t trust their dashboards. How can you trust the data if you’re unsure of the underlying assets? Trust in data and dashboards are critical if companies want to evolve to data-driven decision making—they go hand in hand. Implementing appropriate quality controls and governance processes can help improve the reliability of dashboard data.
Before focusing on sexier advanced analytics and A.I., companies need to ensure their dashboards are reliable. This means manufacturers must invest in data infrastructure, monitor and address data defects, and implement appropriate data quality controls and governance processes.
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