An Evolutionary Strategy for Leveraging Data Risk- Based Software Development for Data Integrity

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 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. Taking into consideration findings from data element risk analysis during the software development life cycle (SDLC) adds a higher level of confidentiality, integrity and protection controls to organizational data and business-critical processes. This can be implemented by proactively incorporating the results of data element risk analysis into user requirements, functional requirements, customization of coding standards, data modelling, system architecture and database design. Organizations operating in industries such as pharmaceuticals, finance and clinical research are routinely exposed to situations in which they must invent innovative products and systems while remaining compliant with regulations. Updating the traditional software development methodology to include data risk-based development enables organizations to avoid any penalties that may be imposed due to a lack of controls and noncompliance with regulations and laws, which makes data element risk-based development a highly lucrative approach for information systems development in many industries in the public and private sectors