Organizations that are data-driven use data science to base every business choice on verifiable information derived from data.

Organizations can accelerate the growth of their data-driven decision-making skills by using data science. It is a multidisciplinary field that necessitates substantial proficiency in the areas of mathematics, computer science, statistics, and particularly software engineering in order to draw insightful conclusions from data and enhance the decision-making abilities of businesses. Therefore, it requires the cooperative efforts of workers from various backgrounds, disciplines, and business divisions. To effectively manage and coordinate data science initiatives across the company, it is necessary to have sustainable organizational capabilities.

On the other hand, the most difficult drivers on the journey to successfully adopting data science and becoming a data-driven organization are organizational capabilities, including a cultural shift toward a data-driven culture, building the right multidisciplinary team, and aligning the organization in a strategic business direction. Even though most organizations use big data technologies and data science methodologies to extract insights from data, most of these organizations still base their business decisions on the knowledge derived from data rather than on the experience or intuition of their managers due to organizational issues, such as a lack of a data-driven culture. In order to help organizations in their transformation efforts from intuition-driven to a data-driven organization, there is a need to systematically and thoroughly examine, define, and manage organizational processes, practices, and capabilities. To assess and comprehend how to implement the value of relatively new technologies and capabilities in an organizational context, process capability maturity models (MMs) and standards, such as Capability Maturity Model Integration (CMMI) and Software Process Improvement and Capability dEtermination (SPICE), are used. They outline key trends in the evaluation of process skills and offer suggestions for development. They can be used to evaluate the present process capability level in a descriptive manner. They also have prescriptive goals that outline the actions organizations should take to enhance their existing process capabilities. The advantages of CMMI and ISO/IEC 330xx in the software development field have led to their adaptation to other fields, such as the government and the automotive sector. Additionally, the use of an ISO/IEC 330xx based process capacity MM in the field of data analytics. As a result, this strategy is appropriate for assisting organizations in understanding the possible advantages of data science and data-drivenness. By offering guidance in determining the current organizational processes capability level, presenting opportunities for improvement to move to the next capability level, this study seeks to develop a process capability MM to evaluate organizational management processes for a successful transition to a data-driven organization.

The proposed Data Drivenness Process Capability Determination Model (DDPCDM) examines organizational data-drivenness from a comprehensive interdisciplinary standpoint and has six capability levels, ranging from Level 0: Incomplete to Level 5: Innovating. Based on the well-known ISO/IEC 330xx standard family, which replaces SPICE, we specified key procedures, techniques, and capabilities to help our organization become data-driven. We also used a multiple-case study methodology to assess the applicability and usage of the DDPCDM. The findings show that the DDPCDM is capable of assessing the advantages and disadvantages of an organization that is transitioning to a data-driven organization and offers a roadmap for enhancing and coordinating its organizational management skills over time.