7 Data Skills to Master for the AI Era
▼ Summary
– 82% of chief data officers are hiring for new data roles related to generative AI that did not exist the previous year.
– A majority of executives (77%) report struggling to fill these new data roles, with recruitment success rates dropping significantly.
– Only 26% of CDOs are confident their organization’s data is ready to support new AI-enabled revenue streams and AI agents.
– Key challenges limiting AI’s use of enterprise data include issues with accessibility, completeness, integrity, accuracy, and consistency.
– Investment in data strategy is increasing, with the typical IT budget allocation for it rising from 4% to 13%, and most CDOs advocating for integrated data and technology roadmaps.
The rapid emergence of new job titles like AI agent supervisors and generative AI data scientists underscores a fundamental shift in the modern workplace, driven by the urgent need to manage and prepare data for artificial intelligence. A staggering 82% of chief data officers report they are now hiring for data roles that simply didn’t exist a year ago, highlighting the breakneck pace of change. However, this hiring surge is met with a significant challenge: most executives are struggling to fill these critical positions, and only about a quarter are confident their organization’s data is truly ready to power AI agents effectively.
This skills gap presents a major obstacle. Research indicates that data readiness is a primary barrier to deploying AI successfully, with issues like accessibility, completeness, and accuracy plaguing enterprise datasets. Slow response times for data requests, high error rates, and inconsistent formats across systems are common problems that limit AI’s potential. Despite these hurdles, the market pressure to adopt AI is immense. A large majority of CDOs believe the potential benefits of deploying AI agents outweigh the risks, and companies are directing substantial resources toward this goal. IT budget allocations for data strategy have more than tripled in just a year, signaling a clear priority.
To navigate this landscape, professionals and organizations must focus on cultivating specific competencies. Mastery goes beyond traditional data analysis to encompass the entire lifecycle of data in an AI-driven environment.
First, leveraging AI to improve AI is becoming essential. AI agents can autonomously manage data by cleansing it, detecting anomalies, and validating it against standards, which significantly boosts accuracy and efficiency. These systems can also help predict future data requirements, creating a more proactive data management strategy.
Second, democratizing data access across the entire organization is a critical step. Prioritizing open data resources fosters innovation and experimentation, which are the bedrock of successful AI adoption. When teams can easily access and understand data, they can uncover new insights and applications.
Third, Developing a forward-looking data strategy. This involves understanding not only what data is currently available but also anticipating what will be needed next and identifying who requires it. This strategic planning must be done in partnership with business and technology leaders to ensure alignment with overall objectives.
The fourth area is learning to leverage unstructured data. The next wave of AI tools, including natural language processing and computer vision, can interpret text, images, video, and audio. Skills in multimodal integration, bringing together these diverse data types from various sources, will be highly valuable.
Fifth, establishing clear key performance indicators that link data initiatives to tangible business outcomes is crucial. Metrics should connect to results like increased sales, reduced customer churn, or operational cost savings. Implementing regular return-on-investment reporting quantifies the financial impact of data projects, making it easier to justify and secure ongoing investment.
Sixth, Advocating for widespread data literacy. In the AI era, every role has a data component. Ensuring leaders understand the strategic value of data and supporting data-driven decision-making at all levels improves engagement and operational efficiency across functions.
Seventh and Finally, investing in and utilizing intuitive data interfaces and user-friendly analytics tools is paramount. These systems simplify interaction with complex business data for non-technical users. An overwhelming majority of CDOs agree that for AI to be effective, the underlying data systems must be easy to access and use, breaking down technical barriers.
The trajectory is clear: data is the fuel for the AI revolution. Mastering these interconnected skills, from strategic governance and technical integration to literacy and accessibility, will define success for both individuals and organizations aiming to thrive in this new era.
(Source: ZDNET)
