The Industrial Data Value Chain

Begin your journey towards better operational data quality today

The Industrial Data Value Chain

The Industrial Data Value Chain 2560 1280 APERIO

The industrial data value chain describes the progression of data from collection to processing, sharing, analyzing, and using the data for decision-making. While the value chain can be applied to all types of data, it is particularly useful in gaining an understanding of operational data. However, poor data quality, which is defined as times when data is inaccurate, missing, or otherwise unreliable, inhibits good decision-making based on data that cannot be trusted.

Quantifying the Impact of Bad Data

The industrial data value chain can be used to show the complexity of data from its creation to its application. Understanding the types of bad data that are discoverable within each stage of the industrial data value chain, and their potential impact as data propagates further down the data pipeline, sets the stage for a methodology that can remediate them, thus improving the business value while reducing risk.

01  Collecting Data. Many unintentional errors can occur during the collection of raw data. Poor data entry and inconsistencies are the largest sources of error. This is augmented when issues arise from challenges in scaling.

02  Processing Data. During the processing of data, many quality issues may be detected that affect the integrity of the data value chain, including data that is misconfigured, out of range, or breaks correlations. While much time can be spent “fixing” data during this stage, it’s a necessary step towards fully trusting the decisions based on this data.

03  Sharing Data. It’s during this stage that data losses from unresolved bad data start showing their impact on performance. Gaps in sharing data across roles and departments require strong governance. Without it, decisions based on this data may prove worthless because of competing agendas.

04  Analyzing Data. Accurate and reliable data is needed to efficiently derive value from building dashboards, visualization, and models. Without it, decisions based on this data are untrustworthy.

05  Using Data. Trusting applied data is where the rubber meets the road. Inherently “bad” data can cause poor decision-making, weak advanced models, faulty reporting, and increased business risk.

The Approach to Better Data Quality

A comprehensive approach for identifying, prioritizing, and remediating a broad set of quality issues found in operational data is required to systemically improve data quality. Following a Connect-Quantify-Prioritize-Remediate methodology will allow you to derive the greatest value at each step on your journey to improved data quality across the industrial data value chain.

SaaS-based APERIO DataWise can automatically detect data quality issues identified across all stages of the industrial data value chain. Whether it’s finding issues within your historian data or ensuring you plug accurate data into your models, APERIO DataWise employs unsupervised AI machine learning to quantify “bad” data, with metrics, smart workflows, and root cause analyses for easy remediation.

About Livia

Livia Wiley has more than 25 years of industrial software experience at AspenTech, Honeywell, and AVEVA, focusing on strategy, customer centricity, driving growth, and value storytelling. Her engineering background is underpinned by broad industry expertise and applications of industrial automation software, having worked for AVEVA, Schneider Electric, Honeywell, and Aspen Technology. Livia holds a B.Sc. in Chemical Engineering from Queen’s University and a M.Eng in Chemical Engineering from the University of Houston.

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