Data Management Strategy Based on Use-Cases for Ensuring Data Integrity and Quality
In the digital age, data has become a crucial asset for businesses and organizations alike. However, the quality of data often poses a significant challenge. Poor data collection practices, missing data, inaccessible storage mechanisms, and a lack of standards are just a few of the issues that hinder data readiness.
The problem, it seems, lies in rushing to address data quality without proper planning. Traditionally, software quality has been primarily addressed through its readiness for consumption, focusing on functional and non-functional aspects. But what if we could apply similar principles to data?
Enter the concept of data readiness. A data readiness approach, modeled on software testing principles, aims to improve data quality for specific use cases by systematically validating data throughout its lifecycle. This involves automated, rule-based checks, profiling, cleansing, and ongoing monitoring to catch anomalies and maintain data integrity.
Like software testing, data readiness applies end-to-end validation, covering data ingestion, integration, transformation, and storage, to detect errors early and continuously. Automation and AI-driven tools accelerate quality checks such as outlier detection, deduplication, missing value handling, and standardization, reducing manual error and improving efficiency.
Aligning data readiness to particular use cases involves understanding the decision-making context, ensuring data is relevant and representative for that scenario, and tailoring validation rules accordingly. For advanced AI systems, data readiness means preparing datasets to be AI-ready—cleaned, labeled, normalized, and feature-engineered—to prevent issues like bias, underfitting, and overfitting, which are critical for specific scientific or business applications.
The data readiness artifact serves as an authentic information source, highlighting the data quality operations performed and how they ensured the data quality, meeting specific use case requirements. The artifact, similar to a data dictionary, is cataloged to capture the use cases supported and readiness assessment results for each use case.
The proposed approach is to leverage the data readiness approach to establish specific data consumption context through a specific use case to improve data quality. This approach could reduce repetitive data exploration and analysis, enable easy data auditing, and increase data accountability and trustworthiness.
The data readiness-driven approach changes the dialog among the stakeholders and sheds a new perspective on how data is viewed and how data quality is managed. It's no longer about the highest quality of data, but for what use cases data is readied to enable data consumption.
Data readiness levels were discussed by Lawrence (2017) in a publication on the Cornell University ArXiv Computer Science Database, and the data readiness artifact was presented at the 2021 IEEE International Conference on Smart Data Services (SMDS) by Afzal et al. (2021). The Cloud data governance maturity model was presented at the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) by Cheng et al. (2017).
It's important to note that data quality is a prevalent issue across all sectors, not limited to one industry. By adopting a data readiness approach, we can ensure that our data is ready for the specific use cases it needs to serve, ultimately increasing operational efficiency, reducing risk, and enhancing decision-making outcomes.
Technology, particularly data and cloud computing, plays a significant role in addressing the challenge of ensuring data quality. The data readiness approach, modeled on software testing principles, is a technology-driven solution that improves data quality for specific use cases and applications by systematically validating data throughout its lifecycle.
