The development of the data science capability maturity model: a survey-based research

Mert Onuralp Gökalp · Ebru Gökalp · Kerem Kayabay · Altan Koçyiğit · P. Erhan Eren

44 citations0/0 files storedOnline Information Review

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Big Data and Business Intelligence Information Technology Governance and Strategy Knowledge Management and Technology

Abstract

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' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed. Findings It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management. Originality/value This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 44 citations exist upstream.

References

2004Design Science in Information Systems Research1 2004Design science in information systems research 2013Big Data : The Management Revolution 2004Validation Guidelines for IS Positivist Research 2009Developing Maturity Models for IT Management 2016A review and future direction of agile, business intelligence, analytics and data science 2016How Can SMEs Benefit from Big Data? Challenges and a Path Forward 2016How organisations leverage Big Data: a maturity model

Cited by

2023Evaluating maturity level of big data management and analytics in industrial companies 2023Knowledge management in data-driven business models during the digital transformation of healthcare organisations

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.