As a new production factor, data is a crucial foundation for driving digital and intelligent transformation and fostering new productive forces, while data governance is a key measure to activate the value of data elements and ensure the implementation of digital and intelligent strategies. Taking Oil and Gas Field Enterprise A as the research object, this paper constructs a three-chain analysis framework of "business chain-responsibility chain-value chain" based on the Data-Information-Knowledge-Wisdom (DIKW) value transformation model in knowledge management theory, and systematically analyzes the practical status, core obstacles and deep-seated contradictions of the enterprise’s data governance. The research shows that Enterprise A has achieved phased results in data governance, but still faces prominent problems such as the lack of full-life-cycle management in the business chain, unimplemented multi-level system in the responsibility chain, and blocked value transformation path in the value chain. These problems are further condensed into four core contradictions, including the mismatch between the urgent demand for digital and intelligent transformation and the weak data governance foundation, and the conflict between the inherent requirements of systematic governance and the traditional block-based management model. In response, this paper proposes optimization strategies of strengthening top-level design from the perspective of connecting the three chains, which provides theoretical reference and practical experience for traditional energy enterprises to empower digital and intelligent transformation through data governance, and accumulates replicable and promotable practices for the same industry’s data governance.
| Published in | Science Innovation (Volume 14, Issue 3) |
| DOI | 10.11648/j.si.20261403.15 |
| Page(s) | 90-95 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
DIKW Model, Data Governance, Digital and Intelligent Transformation, Three-chain Perspective, Energy Enterprises
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APA Style
Li, S., Li, H., Wu, Y., Wang, L. (2026). Data Governance Optimization of Energy Enterprises Based on DIKW Model and Three-Chain Perspective: A Case of Enterprise A. Science Innovation, 14(3), 90-95. https://doi.org/10.11648/j.si.20261403.15
ACS Style
Li, S.; Li, H.; Wu, Y.; Wang, L. Data Governance Optimization of Energy Enterprises Based on DIKW Model and Three-Chain Perspective: A Case of Enterprise A. Sci. Innov. 2026, 14(3), 90-95. doi: 10.11648/j.si.20261403.15
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Download@article{10.11648/j.si.20261403.15,
author = {Shuangying Li and Han Li and Yueyang Wu and Le Wang},
title = {Data Governance Optimization of Energy Enterprises Based on DIKW Model and Three-Chain Perspective:
A Case of Enterprise A},
journal = {Science Innovation},
volume = {14},
number = {3},
pages = {90-95},
doi = {10.11648/j.si.20261403.15},
url = {https://doi.org/10.11648/j.si.20261403.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20261403.15},
abstract = {As a new production factor, data is a crucial foundation for driving digital and intelligent transformation and fostering new productive forces, while data governance is a key measure to activate the value of data elements and ensure the implementation of digital and intelligent strategies. Taking Oil and Gas Field Enterprise A as the research object, this paper constructs a three-chain analysis framework of "business chain-responsibility chain-value chain" based on the Data-Information-Knowledge-Wisdom (DIKW) value transformation model in knowledge management theory, and systematically analyzes the practical status, core obstacles and deep-seated contradictions of the enterprise’s data governance. The research shows that Enterprise A has achieved phased results in data governance, but still faces prominent problems such as the lack of full-life-cycle management in the business chain, unimplemented multi-level system in the responsibility chain, and blocked value transformation path in the value chain. These problems are further condensed into four core contradictions, including the mismatch between the urgent demand for digital and intelligent transformation and the weak data governance foundation, and the conflict between the inherent requirements of systematic governance and the traditional block-based management model. In response, this paper proposes optimization strategies of strengthening top-level design from the perspective of connecting the three chains, which provides theoretical reference and practical experience for traditional energy enterprises to empower digital and intelligent transformation through data governance, and accumulates replicable and promotable practices for the same industry’s data governance.},
year = {2026}
}
TY - JOUR T1 - Data Governance Optimization of Energy Enterprises Based on DIKW Model and Three-Chain Perspective: A Case of Enterprise A AU - Shuangying Li AU - Han Li AU - Yueyang Wu AU - Le Wang Y1 - 2026/05/29 PY - 2026 N1 - https://doi.org/10.11648/j.si.20261403.15 DO - 10.11648/j.si.20261403.15 T2 - Science Innovation JF - Science Innovation JO - Science Innovation SP - 90 EP - 95 PB - Science Publishing Group SN - 2328-787X UR - https://doi.org/10.11648/j.si.20261403.15 AB - As a new production factor, data is a crucial foundation for driving digital and intelligent transformation and fostering new productive forces, while data governance is a key measure to activate the value of data elements and ensure the implementation of digital and intelligent strategies. Taking Oil and Gas Field Enterprise A as the research object, this paper constructs a three-chain analysis framework of "business chain-responsibility chain-value chain" based on the Data-Information-Knowledge-Wisdom (DIKW) value transformation model in knowledge management theory, and systematically analyzes the practical status, core obstacles and deep-seated contradictions of the enterprise’s data governance. The research shows that Enterprise A has achieved phased results in data governance, but still faces prominent problems such as the lack of full-life-cycle management in the business chain, unimplemented multi-level system in the responsibility chain, and blocked value transformation path in the value chain. These problems are further condensed into four core contradictions, including the mismatch between the urgent demand for digital and intelligent transformation and the weak data governance foundation, and the conflict between the inherent requirements of systematic governance and the traditional block-based management model. In response, this paper proposes optimization strategies of strengthening top-level design from the perspective of connecting the three chains, which provides theoretical reference and practical experience for traditional energy enterprises to empower digital and intelligent transformation through data governance, and accumulates replicable and promotable practices for the same industry’s data governance. VL - 14 IS - 3 ER -
Economics and Technology Research Institute, PetroChina Liaohe Oilfield Company, Panjin, China
Economics and Technology Research Institute, PetroChina Liaohe Oilfield Company, Panjin, China
Economics and Technology Research Institute, PetroChina Liaohe Oilfield Company, Panjin, China
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