盘活遗留数据，释放AI潜能——油气行业数字化转型

Core Insights - For energy companies, data is as crucial as oil and gas reserves, yet often fragmented and of low quality, hindering the effective use of advanced technologies like AI [1] - A two-step approach is essential for rebuilding data foundations and establishing a sustainable data environment [2] Step 1: Transforming Mindsets with Solid Business Cases - Many business issues can be addressed through advanced technologies like AI, but there is often a lack of clarity on which solutions will yield the greatest end-to-end impact [3] - It is recommended to align data strategies with valuable AI applications, such as predictive maintenance and supply chain optimization, to demonstrate how data management supports business objectives [3] - Focus should be on identifying necessary data and its storage locations, with targeted data cleaning efforts to enhance quality [3] Step 2: Establishing a Sustainable Data Environment through Global Transformation - Continuous improvement in data management requires attention to processes, governance, and personnel, embedding data quality into the organization as a long-term practice [4] - Standardization of processes is the goal, but many energy companies face challenges with data being stored in various forms across different functions [4] - Integrating data can create a single source of truth, addressing issues like employee distrust in inventory data that leads to over-ordering and inefficiencies [4][5] Key Challenges in Data Management - Data fragmentation and redundancy lead to inefficiencies and confusion [5] - Outdated and low-quality data decreases reliability and operational efficiency [5] - Lack of unified standards results in inconsistent data input and additional conversion needs during system integration [5] - System silos hinder data flow across departments, limiting visibility [5] - Insufficient external collaboration with third-party systems affects end-to-end visibility [5] - Absence of clear governance responsibilities leads to unresolved long-term data quality issues [5] Importance of Governance and Cultural Change - Effective governance is crucial for maintaining the direction of data transformation, preventing a return to old habits [6] - A cultural shift supported by cross-functional collaboration is necessary to create a sustainable data-driven environment [6] Key Success Factors for Data Transformation - Clearly define the vision for data empowerment in the supply chain, ensuring all stakeholders understand the goals and paths to achieve data-driven insights [10] - Challenge the status quo to identify improvement points, emphasizing the need for data-supported decision-making [10] - Appoint change agents to lead the transformation, ensuring clear communication of its importance [10] - Early wins can build momentum and support for data quality initiatives [10] - Recognize that transformation involves not just data and technology, but also people, processes, governance, and cultural shifts [10] Benefits of High-Quality Data Foundations - Establishing reliable, high-quality data enables energy companies to gain visibility and transparency for informed decision-making across operations [11] - Improved understanding of processes leads to better planning, optimization, and risk mitigation throughout the supply chain [11] - High-quality data supports the creation of a "command center" for tracking and predicting every aspect of the supply chain [11] - For instance, the National Pipeline Group in China utilized over 130,000 industry data points to achieve a 96% accuracy rate in daily natural gas consumption forecasts [11] - Ultimately, centralized, high-quality data allows energy companies to shift from reactive to proactive operations, preparing them for advanced technologies [11]