Abstract The Software Product Line (SPL) approach receives more attention due to the observed benefits, such as cost reduction, quality improvements, and reduced delivery time. Although organizations are aware of its potential benefits, they face some challenges while creating a clear road map to adopt this approach. Capability maturity models are developed to guide organizations by providing an extensive road map for improvement. Accordingly, we developed a capability maturity model, entitled SPL Capability Maturity Model (SPL‐CMM), to improve an organization's SPL capabilities by enabling the assessment of SPL‐specific processes and providing a guideline for process improvement. SPL‐CMM, developed based on SPICE‐ISO/IEC 330xx standard, has two dimensions of process and capability. The process dimension consists of 16 SPL‐specific processes defined under four process areas: business, architecture, technical, and organization, and the capability dimension has six capability and maturity levels from levels 0 to 5. It is a structured approach assisted by adequate measures with guidance on actions for improvement. A case study was conducted to check the applicability and usefulness of the proposed approach in a software company. The case study results show that SPL‐CMM is applicable for identifying the current state of the SPL process capability and the gaps for process improvement to the next level.

Abstract Today, data analytics plays a vital role in attaining competitive advantage, generating business value, and driving revenue streams for organizations. Thus, the organizations pay significant attention to improve their data analytics maturity. Nevertheless, the existing literature is dramatically limited in proposing a comprehensive roadmap to assist organizations for this scope. Thus, this study focuses on developing data analytics maturity assessment framework (DAMAF) that evaluates the organizational data analytics maturity in a staged manner from maturity level 0: incomplete to maturity level 5: optimizing. The DAMAF comprises the nine different data analytics attributes to address the specific needs of each data analytics maturity level. Accordingly, it aims to support organizations in assessing their current data analytics maturity, determining organizational gaps in data analytics, and preparing an extensive roadmap and suggestions for data analytics maturity improvement. In this research, we employed the DAMAF in an organization as a case study to evaluate its applicability and usefulness. The results showed that DAMAF properly reveals the data analytics gaps and provides a structured roadmap for continuously advancing the data analytics maturity of an organization.

Abstract The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data‐driven decision‐making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data‐driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple‐case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data‐driven organisation and providing a roadmap for continuously improving the data‐drivenness of organisations.