用数据看见问题、用技术解决问题、用责任校准方向。大数据赋能高质量发展的时代浪潮，为青年成长 成才提供了前所未有的机遇，也赋予了青年沉甸甸的责任。作为新时代青年，我们要主动迎接变化、分 析变化，既在课堂、实验室、企业与田野中锤炼数据思维，也在真实场景中提升解决问题的能力；既敢 于用创新精神打开新空间，也始终把"向善""合规""求真"作为底线。唯有如此，才能以青春的热情耕耘 数字沃土，以过硬的本领把握发展机遇，以创新的实践破解发展难题，让个人成长与国家发展同频共 振，在数据赋能高质量发展的征程中放飞青春梦想。 （作者：张鹏洲，系中共贵州省委党校经济学教研部副教授） 大数据把新赛道摆在了青年面前。它正以颠覆性力量重塑时代机遇版图，不仅催生了数据挖掘、算法优 化、数字风控等新产业新赛道，还以很强的渗透性全面嵌入传统产业，不断赋能传统产业转型升级，推 动农业、制造业、服务业等领域实现数字化转型。大数据的迅猛发展不断重构人才需求格局与价值创造 逻辑，倒逼人才结构向复合型、创新型升级。一方面，大数据的广泛应用对重复性、流程化岗位形成不 小的替代压力，让部分传统行业从业者面临转型的压力与挑战；另一方面，数据分析师、首席数据官 ...

Group 1: Impact of AI on Employment and Industry - Meta's CEO Mark Zuckerberg announced plans to automate mid-level software engineering tasks, which may lead to job losses in the tech sector [1] - The rapid adoption of AI is causing widespread concern about the future of job roles, as AI development outpaces reskilling efforts [1] - AI is seen as a potential "turbocharger" for industrial transformation, enhancing resource efficiency and sustainability in sectors like renewable energy and electric vehicles [2] Group 2: Data Collaboration and Sustainability - Sharing data among industrial enterprises can address challenges related to talent and energy transitions, without compromising data security through techniques like federated learning [3][4] - Establishing a reliable data collaboration platform can improve energy management and reduce carbon emissions by allowing real-time sharing of energy consumption data [6] - Cross-industry collaborations can foster energy innovations, such as steel companies working with renewable energy firms to optimize energy usage [6] Group 3: Data Quality and Employee Empowerment - The effectiveness of AI systems relies on high-quality data, which is becoming a strategic resource for companies [7] - Data cooperatives can enhance data quality and provide continuous, valuable data resources to businesses while creating revenue for data providers [7] - Empowering employees with data literacy is essential for optimizing data collection processes and improving AI system accuracy [7][10] Group 4: Human-Machine Collaboration - Companies need to empower employees to master human-machine collaboration skills while clearly defining the roles of AI and humans [11] - In the transitional phase, employees should learn to identify tasks suitable for AI and those requiring human intervention [12] - In the mature phase, a clear division of labor will emerge, with machines handling repetitive tasks and humans focusing on emotional and creative endeavors [13]

Core Insights - The financial industry is navigating a challenging environment, with top investment managers leveraging data analysis throughout the investment process to maintain a balance between risk and return [1] Group 1: Skills Development - The first step in skill enhancement involves solidifying foundational modules, with top managers moving beyond Excel to dynamic data dashboards, emphasizing the importance of continuous learning and analysis of financial reports [2] - The second step focuses on building a comprehensive resource network, where top professionals convert social activities into data management, utilizing CRM strategies to manage relationships with limited partners and industry peers [3] - The third step is the intelligent decision-making system, where advanced tools like self-developed due diligence systems are used to generate risk indicators efficiently, highlighting the importance of data cleaning and algorithm optimization [4] Group 2: Data-Driven Era Benefits - Recent research indicates that 82% of VC firms require data analysis reports at the project initiation stage, with top brokerage firms mandating data analysis certification for project managers, underscoring the growing importance of data skills in investment roles [5] - The CDA data analysis certification is becoming a key credential in the investment community, covering essential modules that align with equity investment due diligence, and is preferred by leading firms like Deloitte and Sequoia Capital [5] Group 3: Career Advancement Strategies - Suggested pathways for skill enhancement include starting with financial modeling and industry research, progressing to data scraping and business analysis, and ultimately advancing to strategic decision-making and ecosystem building [6] - A case study of a city investment director illustrates the exponential impact of acquiring the CDA Level II certification, leading to significant career advancement and recognition [6] - The ability to harness intelligent tools in the face of big data is reshaping industry competition, indicating a shift from traditional methods to data-driven approaches in investment management [6] Group 4: Personal Development - Investment professionals are encouraged to create their own "capability investment portfolio," incorporating certifications like CDA as essential assets in the evolving quantitative capital market [7]