Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach
Download
IET Software - 2021 - G kalp - Assessment of process capabilities in transition to a data‐driven organisation A.pdf
Date
2021-06-01
Author
Gökalp, Mert Onuralp
Gökalp, Ebru
Koçyiğit, Altan
Eren, Pekin Erhan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
247
views
117
downloads
Cite This
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.
URI
https://hdl.handle.net/11511/91579
Journal
IET SOFTWARE
DOI
https://doi.org/10.1049/sfw2.12033
Collections
Graduate School of Informatics, Article
Suggestions
OpenMETU
Core
A process assessment model for big data analytics
Gökalp, Ebru; Gökalp, Mert Onuralp; Kayabay, Kerem; Gökalp, Selin; Koçyiğit, Altan; Eren, Pekin Erhan (2022-03-01)
Big data analytics (BDA) grasp the potential of generating valuable insights and empowering businesses to support their strategic decision-making. However, although organizations are aware of BDAs’ potential opportunities, they face challenges to satisfy the BDA-specific processes and integrate them into their daily software development lifecycle. Process capability/ maturity assessment models are used to assist organizations in assessing and realizing the value of emerging capabilities and technologies. Ho...
Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization
Kayabay, Kerem; Gökalp, Mert Onuralp; Eren, Pekin Erhan; Koçyiğit, Altan (2022-01-01)
Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological,...
The development of the data science capability maturity model: a survey-based research
Gökalp, Mert Onuralp; Kayabay, Kerem; Koçyiğit, Altan; Eren, Pekin Erhan (2021-09-01)
Purpose The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way. Design/methodology/approach This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' co...
Optimization of an online course with web usage mining
Akman, LE; Akkan, B; Baykal, Nazife (2004-02-18)
The huge amount of information existing in the World Wide Web constitutes an ideal environment to implement data mining techniques. Web mining is the mining of web data. There are different applications of web mining: web content mining, web structure mining and web usage mining. In our study we analyzed an online course by web usage mining techniques in order to optimize the navigation paths, the duration of the time spend on each page and the number of visits throughout the semester of the course. Moreove...
Large-Scale Renewable Energy Monitoring and Forecast Based on Intelligent Data Analysis
Özkan, Mehmet Barış; Küçük, Dilek; Buhan, Serkan; Demirci, Turan; Karagöz, Pınar (IGI Global, 2020-01-01)
Intelligent data analysis techniques such as data mining or statistical/machine learning algorithms are applied to diverse domains, including energy informatics. These techniques have been successfully employed in order to solve different problems within the energy domain, particularly forecasting problems such as renewable energy and energy consumption forecasts. This chapter elaborates the use of intelligent data analysis techniques for the facilitation of renewable energy monitoring and forecast. First, ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. O. Gökalp, E. Gökalp, A. Koçyiğit, and P. E. Eren, “Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach,”
IET SOFTWARE
, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/91579.