How do you ensure data quality and integrity throughout the manufacturing data analytics lifecycle?

1 Define data requirements

Before you collect, process, and analyze data, you need to define what data you need, why you need it, and how you will use it. This will help you set clear and consistent data quality standards and criteria, such as completeness, validity, accuracy, timeliness, and relevance. You also need to identify the sources, formats, and methods of data collection, and ensure that they are reliable, secure, and compliant with regulations and policies.

2 Implement data governance

Data governance is the framework that defines the roles, responsibilities, processes, and policies for managing and using data across the organization. It helps to ensure that data is properly stored, accessed, shared, and protected, and that data quality and integrity are maintained and monitored throughout the data lifecycle. Data governance also involves establishing data quality metrics, indicators, and reports, as well as defining data quality issues, risks, and remediation actions.

3 Use data quality tools

Data quality tools are software applications that help you perform various tasks related to data quality and integrity, such as data profiling, cleansing, validation, enrichment, matching, deduplication, and transformation. Data quality tools can help you automate and streamline data quality processes, detect and correct data errors and inconsistencies, enhance and standardize data values and formats, and integrate and consolidate data from multiple sources.

4 Apply data quality techniques

Data quality techniques are methods and practices that help you improve and maintain data quality and integrity, such as data auditing, data lineage, data masking, data anonymization, and data security. Data auditing involves checking and verifying the data quality against the predefined standards and criteria, and reporting and resolving any issues or gaps. Data lineage involves tracing and documenting the data flow and transformation from the source to the destination, and ensuring that the data is not altered or corrupted along the way. Data masking involves replacing sensitive or confidential data with fictitious or modified data, while preserving the data structure and format. Data anonymization involves removing or obscuring any data that can identify or link to an individual or entity, while retaining the data utility and value. Data security involves protecting the data from unauthorized access, use, modification, or disclosure, using encryption, authentication, authorization, and other techniques.

5 Validate and verify data analytics results

After you perform data analytics on your data, you need to validate and verify the results to ensure that they are accurate, reliable, and meaningful. Validation involves checking whether the data analytics methods, models, and algorithms are appropriate, consistent, and robust for the data and the objectives. Verification involves checking whether the data analytics results are correct, complete, and coherent with the data and the expectations. You can use various techniques to validate and verify data analytics results, such as cross-validation, testing, debugging, peer review, and feedback.

6 Continuously monitor and improve data quality and integrity

Data quality and integrity are not static, but dynamic and evolving. As data changes over time, so do the data quality standards, criteria, and expectations. Therefore, you need to continuously monitor and improve data quality and integrity throughout the data analytics lifecycle, using the data quality metrics, indicators, and reports, as well as the data quality issues, risks, and remediation actions. You also need to review and update the data requirements, data governance, data quality tools, and data quality techniques, as well as the data analytics methods, models, and algorithms, to ensure that they are aligned with the data and the objectives.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?