The future of work is being redefined by the rapid adoption of generative AI, and data teams are at the forefront of this transformation. According to the 2024 AI at Wharton report, 72% of respondents are using generative AI at least once a week, with over 80% of respondents in IT, business intelligence, customer service, marketing, operations, and product development stating that it has a medium-to-high impact on their work. As a result, data teams must take the opportunity to provide more data services to departments adopting generative AI, focusing on data governance, operations, and products that make data reliable and discoverable for business users and use cases.

Data teams and specialists, including data scientists, engineers, architects, and data governance specialists, are critical in democratizing data access and ensuring a solid foundation for data-driven decisions. According to Irfan Khan, president and chief product officer of SAP Data & Analytics, "Data teams are transforming the future of work within their organizations by democratizing data access and ensuring a solid foundation for data-driven decisions. Through the management, governance, and analysis of data, they do more than automate calculations or create dashboards; they uncover deeper insights and help employees perform their tasks more efficiently while reducing the backlog of demands on resource-strapped IT departments."

To support data discovery and transformation for business teams adopting generative AI, data professionals can take five key actions. Firstly, they must make data security non-negotiable, ensuring that data access governance is a critical first step to protecting the organization as business teams aim to become more data-driven while leveraging large language model (LLM) capabilities. This includes discovering and cataloging all data assets, enforcing least privilege principles, and supporting zero trust and minimizing risks to valuable and sensitive information.

Secondly, data teams must extend data quality to LLM document processing, ensuring that unstructured data sources go through data cleansing, preparation, and cataloging as more business teams want to use them in RAGs and LLMs. This includes entity extraction, sentiment analysis, and bias detection, as well as employing AI at all levels of the data pipeline to jump-start new projects and get them to provide business value faster.

Thirdly, data teams should empower citizen data scientists by centralizing data, considering their data management strategies and how to enable easier and faster access to data sources. This includes using data warehouses, data lakes and lakehouses, and data fabrics, as well as fostering a self-service culture that equips every department to contribute to and act on data-driven decisions.

Fourthly, data teams should establish data marketplaces to simplify data discovery, using data catalogs and creating data dictionaries to enable broader data access. This includes cultivating an internal data marketplace that automates discovery and access, while still providing enterprise-grade governance and security.

Finally, data teams must develop data products that foster collaboration, considering their advanced dashboards, machine learning models, LLM capabilities, and AI agents as data products and managing them as product development initiatives. Each product has a defined customer segment, value proposition, and strategic objective, which can be defined in a vision statement and managed through a product roadmap.

As the adoption of generative AI continues to accelerate, data teams are essential in industries where integrating several primary high-volume data sources is needed for many departmental use cases. Companies in manufacturing, construction, energy, and other industrials can use data catalogs and marketplaces to aggregate and simplify using real-time data sources for decision-making in marketing, field operations, supply chain, finance, and other departments.

In conclusion, the future of work requires data teams to lead with data governance, operations, and products that make data reliable and discoverable for business users and use cases. By prioritizing these areas, data teams can unlock the full potential of generative AI and drive business transformation.