An Evolutionary Strategy for Leveraging Data Risk- Based Software Development for Data Integrity
Sasidhar Duggineni
Abstract
Organizational decision-making is largely dependent on the availability of adequate supporting data, data elements or records. These are perhaps the most important elements that can aid enterprises in making critical decisions promptly and efficiently. For example, healthcare organizations rely on electronic health and medical records to inform important decisions. However, in many organizations, when it comes to product code and software development and design models, data elements do not appear to carry equal importance compared to functionality. But there has been a gradual rise in recognition of the importance of data risk-based development across industries. Data risk-based development highlights the importance of critical data points and attributes in a highly scrutinized environment to significantly limit risk. It recognizes that less data risk is linked with the objective of systems development with the least possible risk. Routine traditional software development methodology does not serve as a one-size-fits-all approach for every industry. For example, organizations specializing in life sciences and banking have certain key data elements that play critical roles in enterprise processes and are highly scrutinized via regulations and laws. Therefore, giving equal importance to identified critical data elements as functional specifications during the design and development phases results in effective system development and helps avoid the cost of subsequent additional development activities to address data quality and regulatory findings.