Draft:Data Strategy
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Comment: Likely notable but most of the sources are either not reliable (blogs - see WP:BLOGS, commercial sites offering products and services) or primary. For a topic like this there should be either books published by reputable authors/publishers and/or peer-reviewed journals. S0091 (talk) 19:26, 25 April 2025 (UTC)
Data strategy is an organisational plan that defines how data will be sourced, stored, governed, shared, managed and applied to achieve business objectives.[1] It aligns data-related investments and policies with enterprise strategy and provides the governance needed to transform raw data into trusted information assets.[2]
Definition
[edit]Analysts describe a data strategy as a “dynamic process” that orchestrates people, processes, and technology across the data life-cycle.[1] Academic research positions it as a ‘‘policy-like’’ instrument that guides data from creation through archival.[3]
Core components
[edit]Widely used frameworks converge on five recurring pillars: vision and value-cases, governance, architecture, people & culture, and processes & metrics.[1][3]
- Vision and value-cases — linkage to corporate objectives and prioritised data products.
- Data governance — policies, roles, stewardship, and quality controls as codified in the *DAMA-DMBOK2* wheel.[4]
- Architecture and tooling — platforms, integration patterns, security, and emerging paradigms such as data mesh.[5]
- People and culture — literacy, operating model, incentives; reinforced by DataOps practices.[6]
- Processes and metrics — agile delivery, data-quality KPIs and continuous improvement guided by ISO 8000.[7]
Frameworks and methodologies
[edit]- DAMA-DMBOK2 – A body of knowledge covering 11 data-management knowledge areas; often visualised as a wheel and used to benchmark capability maturity.[4]
- DCAM (Data Management Capability Assessment Model) – An open-industry framework from the EDM Council that links strategic objectives to measurable capabilities.[8]
- Gartner DASOM (Data & Analytics Strategy and Operating Model) – A consulting blueprint that connects business drivers to data-driven outcomes.[9]
- Data Mesh – A domain-oriented, “data-as-a-product” architecture with federated computational governance.[5]
- DataOps – A set of 18 principles emphasising continuous integration/continuous delivery (CI/CD) and cross-functional ownership for analytics pipelines.[6]
- FAIR principles – Guidelines to make data Findable, Accessible, Interoperable and Reusable, increasingly adopted by research and public-sector strategies.[10]
Development and implementation
[edit]Implementation generally starts with a maturity assessment (e.g., DCAM scoring or DMBOK wheel heat-maps) and a roadmap that sequences high-value use-cases.[8] Practitioner guides emphasise small “lighthouse” projects, agile delivery, and strong executive sponsorship.[11]
History and evolution
[edit]- 1990s–2000s — Formal data-warehousing and master-data programmes spur the first corporate data strategies.[4]
- 2010s — Big-data platforms, cloud elasticity and open data initiatives extend scope and scalability.[10]
- 2020s — AI governance, data mesh, and “data-as-a-product” mind-sets reshape strategy; DataOps and FAIR gain mainstream traction.[5][6]
Benefits and challenges
[edit]Effective strategies accelerate decision-making, improve regulatory compliance, and increase the return on analytics and AI investments.[2] Case studies show cost savings of 20–30 % and double-digit revenue uplift when data products are aligned to measurable business outcomes.[11]
Common obstacles include poor data quality, siloed ownership, talent shortages, and ethical concerns such as privacy and algorithmic bias.[7] Critics warn that strategies become “slideware” if not linked to clear metrics and executive accountability.[3]
Standards and reference models
[edit]- ISO/IEC 8000 (data-quality management).[7]
- ISO/IEC TR 20547 (big-data reference architecture).
- NIST Big-Data Reference Architecture.
- OECD OURdata Index for open government data.
- National data strategies such as the U.S. Federal Data Strategy and the U.K. National Data Strategy.
See also
[edit]References
[edit]- ^ a b c "Data Strategy (IT Glossary)". Gartner. Retrieved 25 April 2025.
- ^ a b "A Better Way to Put Your Data to Work". Harvard Business Review. July–August 2022. Retrieved 25 April 2025.
- ^ a b c "Building a Winning Data Strategy: An MIT SMR Executive Guide". MIT Sloan Management Review. 21 September 2020. Retrieved 25 April 2025.
- ^ a b c "DAMA-DMBOK 2 Overview". DAMA International. Retrieved 25 April 2025.
- ^ a b c Zhamak Dehghani (2019). "Data Mesh Principles and Logical Architecture". martinfowler.com. Retrieved 25 April 2025.
- ^ a b c "The DataOps Manifesto". DataOps Manifesto. 10 May 2021. Retrieved 25 April 2025.
- ^ a b c "ISO 8000-1:2022 — Data quality — Part 1: Overview". International Organization for Standardization. 2022. Retrieved 25 April 2025.
- ^ a b "Data Management – DCAM Framework". EDM Council. Retrieved 25 April 2025.
- ^ "Key Success Factors in Any Data and Analytics Strategy". Gartner. Retrieved 25 April 2025.
- ^ a b "FAIR Guiding Principles". GO FAIR. Retrieved 25 April 2025.
- ^ a b "The Complete Guide to Data Strategy for Data Professionals". Data Driven Daily. 2023. Retrieved 25 April 2025.
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