BIG DATA;
DATA ANALYTICS;
MATURITY;
MODEL;
MANAGEMENT;
D O I:
10.1049/sfw2.12033
中图分类号:
TP31 [计算机软件];
学科分类号:
081202 ;
0835 ;
摘要:
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.
机构:
Univ Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
Graphic Era, Dept Comp Sci & Engn, Dehra Dun 248002, India
LAETA, Associate Lab Energy Transports & Aerosp, P-4200465 Porto, PortugalUniv Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
Pecas, Paulo
John, Lenin
论文数: 0引用数: 0
h-index: 0
机构:
Univ Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, PortugalUniv Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
John, Lenin
Ribeiro, Ines
论文数: 0引用数: 0
h-index: 0
机构:
Univ Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
LAETA, Associate Lab Energy Transports & Aerosp, P-4200465 Porto, PortugalUniv Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
Ribeiro, Ines
Baptista, Antonio J.
论文数: 0引用数: 0
h-index: 0
机构:
LAETA, Associate Lab Energy Transports & Aerosp, P-4200465 Porto, Portugal
INEGI Inst Sci & Innovat Mech & Ind Engn, P-4200465 Porto, PortugalUniv Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
Baptista, Antonio J.
Pinto, Sara M. M.
论文数: 0引用数: 0
h-index: 0
机构:
LAETA, Associate Lab Energy Transports & Aerosp, P-4200465 Porto, Portugal
INEGI Inst Sci & Innovat Mech & Ind Engn, P-4200465 Porto, PortugalUniv Lisbon, IDMEC, Inst Super Tecn, P-1049001 Lisbon, Portugal
机构:
China Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China
Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R ChinaChina Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China
Li, Hao
Qiao, Ying
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R ChinaChina Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China
Qiao, Ying
Lu, Zongxiang
论文数: 0引用数: 0
h-index: 0
机构:
Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R ChinaChina Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China
Lu, Zongxiang
Zhang, Baosen
论文数: 0引用数: 0
h-index: 0
机构:
Univ Washington, Dept Elect & Comp Engn, Seattle, WA 98195 USAChina Elect Power Planning & Engn Inst, Beijing 100120, Peoples R China