1. What is Data Integrity ?
Data integrity is the assurance that the data is accurate and consistence, and that this assurance has been maintained throughout the data lifecycle. Also data validation is a prerequisite for data integrity.
In particular, in the pharmaceutical industry, this is subdivided and explained, so it is good to refer to it. : The requirements for data integrity are discussed comprehensively in the ISPE’s GAMP. You can check and read detail information from ISPE’s GAMP 5. (You can buy and read from this url! : https://ispe.org/publications/guidance-documents/gamp-5-guide-2nd-edition)
*ISPE : International Society for Pharmaceutical Engineering
*GAMP : Good Automated Manufacturing Practice
2. Data Integrity : Data Quality
It is related with Data Quality. Data quality relates to the data’s suitability to serve its intended purpose in a given context within a specified business or regulatory process. Data quality management activities address aspects including accuracy, completeness, relevance, consistency, reliability, and accessibility. So, It is closely related to data integrity
Also OECD defines data quality as: “Data quality is the assurance that the data produced are generated according to applicable standards and fit for intended purpose in regard to the meaning of the data and the context that supports it. Data quality affects the value and overall acceptability of the data in regard to decision-making or onward use. Data quality requires that the data is organized and able to be accessed, sorted, and searched to enable the business to effectively use the data.”
*OECD : Organisation for Economic Co-operation and Development
3. Data Integrity : ALCOA+
Data Integrity can also be referred to as ALCOA in other words. Because ALCOA+ is the acronym for the key concepts that can help to support record & data integrity. So according to below principles, DATA MUST BE..
| Principle | Data Expectation |
|---|---|
| Attributable | – Attributable to the person or system generating the data – Identify the person or system performing an activity that creates or modifies data |
| Legible | -Readable and permanent -Accessible throughout the data life cycle -Original data and any subsequent modifications are not obscured |
| Contemporaneous | -Recorded or observed at the time the activity is performed |
| Original | -Original data is the first recording of data |
| Accurate | -Just accurate data -Free from error -The extent to which the data is free of identifiable errors |
| Complete | -All data, and relevant metadata, including any repeat or re-analysis performed |
| Consistent | -All data, should be consistent without manipulation. |
| Enduring | -Recorded in a permanent, maintainable form for the retention period |
| Available | -Available and accessible for review, audit, or inspection throughout the retention period |
4. How to make Data Integrity?: Foundations to Data Integrity by Design
Well-designed data integrity is a quality-critical initiative for GxP regulated organizations that ensure public health, patient safety, and product quality. Collectively these, along with warning letters and other interventions, are acting as drivers to achieve the adoption of fully electronic recordkeeping (even if you do manual tasks). Also, If you build systems for data integrity, business efficiency gains that can be achieved with moving away from paper records.
In order to achieve an effective data integrity by design strategy, a high-level vision for record and data management, “what this might look like,” needs to be established at an organization level. In other words, DATA GOVERNANCE is important! The data governance framework provides the controls for data integrity and quality assurance. Throughout all activities, critical thinking must be leveraged within a quality culture to strive for operational excellence. About Data governance, I’ll cover this in another article.
5. Conclusion
Even if your factory is not a pharmaceutical factory(or company), if you refer to these regulations and guidelines, you can build a successful smart factory. Above all, data integrity is an indispensable condition for building a smart factory. What if someone manipulates the data and destroys the factory in the future? Need to think carefully
6. References
Also you can check about Data Lifecycle and Data Governance from another article.
WIKIPEDIA