
ISO/TS 8000-230:2026
ISO/TS 8000-230:2026 Data quality — Part 230: Sensor data — Guidelines for data cleansing
CDN $337.00
This publication was last reviewed and confirmed in 2026.
Data quality — Part 230: Sensor data — Guidelines for data cleansing
SKU: 93a9b68e1c6f
Categories: ICS:25.040.40, SUSTAINABLE_DEVELOPMENT_GOAL:1, SUSTAINABLE_DEVELOPMENT_GOAL:10, SUSTAINABLE_DEVELOPMENT_GOAL:11, SUSTAINABLE_DEVELOPMENT_GOAL:12, SUSTAINABLE_DEVELOPMENT_GOAL:13, SUSTAINABLE_DEVELOPMENT_GOAL:14, SUSTAINABLE_DEVELOPMENT_GOAL:15, SUSTAINABLE_DEVELOPMENT_GOAL:16, SUSTAINABLE_DEVELOPMENT_GOAL:2, SUSTAINABLE_DEVELOPMENT_GOAL:3, SUSTAINABLE_DEVELOPMENT_GOAL:4, SUSTAINABLE_DEVELOPMENT_GOAL:5, SUSTAINABLE_DEVELOPMENT_GOAL:6, SUSTAINABLE_DEVELOPMENT_GOAL:7, SUSTAINABLE_DEVELOPMENT_GOAL:8, SUSTAINABLE_DEVELOPMENT_GOAL:9
Description
This document specifies guidelines to improve data quality by cleansing sensor data anomalies that affect low inherent quality characteristics.
The following are within the scope of this document:
- principles for sensor data cleansing;
- the process for sensor data cleansing;
- implementation requirements for sensor data cleansing;
- list of data anomaly detection and repair methods (see Annex B);
- examples of sensor data cleansing (see Annex C).
The following are outside the scope of this document:
- algorithms or detailed methods to detect and repair data anomalies;
- the process of sensor data cleansing for real time processing.
Edition
1
Published Date
2026-06-18
Status
PUBLISHED
Pages
41
Format 
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Abstract
This document specifies guidelines to improve data quality by cleansing sensor data anomalies that affect low inherent quality characteristics.
The following are within the scope of this document:
- principles for sensor data cleansing;
- the process for sensor data cleansing;
- implementation requirements for sensor data cleansing;
- list of data anomaly detection and repair methods (see Annex B);
- examples of sensor data cleansing (see Annex C).
The following are outside the scope of this document:
- algorithms or detailed methods to detect and repair data anomalies;
- the process of sensor data cleansing for real time processing.
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