Data democratization, the empowerment of a wide range of employees to engage with data, has become a strategic priority for organizations aiming to create value from data. Existing studies focus on removing organizational or technical barriers to accessing and using data, but offer limited insight into how data democratization materializes in practice. We draw on the concept of communities of practice (CoPs) to examine how data practices are developed and scaled within organizations to meaningfully integrate data and domain knowledge. From an analysis of 45 communities at 21 companies, we identify three types of CoPs that foster data democratization: (1) CoPs that embed data into work practices, (2) CoPs that develop data practices around tools and methods, and (3) CoPs that foster collective data awareness. These CoPs interact through distinct boundary interactions that stimulate practice exchange across the organization. We theorize three enablers that facilitate their interactions: the embeddedness of spanners-in-practice, the malleability of boundary objects, and the supportive governance structures. This study advances information systems research and the emerging field of data studies by unpacking how cross-functional learning and collaboration enable data democratization. For practitioners, we offer guidance for orchestrating and nurturing CoPs to foster data-driven value creation.

Open data initiatives have long focused on motivating governmental bodies to open up their data. The number of open datasets is growing steadily, but their adoption is still lagging behind. An increasing number of studies assess open data portals and open data quality to shed light on open data’s current state. Since prior research addressed neither datasets’ content, nor whether it met enterprises’ data needs, our study aims to address this gap by investigating the extent to which open data is ready for use in the enterprise context. We focus on open corporate registers as an important segment of open government data with high relevance for enterprises. Our findings confirm that open datasets are heterogeneous in terms of access, licensing, and content, which makes them difficult to use in a business context. Our content analysis reveals that less than 50% of analyzed registers provide companies’ full legal addresses, while only 10% note their contact information. We conclude that open data in corporate registers has limited use to its lack of required attributes and relevant business concepts for typical use cases.