• Awasthi and George [6] considered data democratization as a capability and successful implementation relies heavily on the development of the autonomy and trust of non-specialists who need to apply new practices regarding data in their working area. Providing continuous training is important. Successes in data democratization require a continuous improvement in employees’ data skills and responsibilities.

• Continuous learning is fundamental component of data democratization. Research has demonstrated that learning and know-how happens better through collaborative construction of knowledge in convivial social environments. In that perspective, Lefebvre et al. [50] suggested that Wenger’s [51] inspired communities of practice (CoP) can better foster data democratization. The authors stated that a CoP “focused on developing skills around tools and methods, specific data object or data domain, and on spreading general data awareness” are key to the data democratization in any organization.

• Ability to read and understand the data is a prerequisite in master analytics and derives meaningful insights [27] .

• Data democratization is a capability that requires data consumers users be data-literate [23] .

• Analytical tools strengthen data democratization opportunities [30] .

• Effective data democratization requires removing obstacles to data access and sharing the preauthorization [31] .

• Simply increasing access to data, will not enable or empower individuals to use those data to guide or inform their decision-making practice. Data democratization requires intentional attention to training and support for data consumers to develop their skills and ability to use data effectively [33] .

• Data sharing is a principal key dimensions of data democratization; to facilitate that, [36] demonstrated the need for an enterprise data marketplace (which the author defined as metadata-driven self-service platforms for trading data and data related services).

• For data democratization to succeed, organization must provide and raise active awareness among data consumers to use the right technology and right tool to find data ( [6] [37] ).

• The employees should have the appropriate capabilities to work with data and interpret them in the context of their domain [50] .

• Leveraging a multiple case study involving eight companies, Lefebvre et al. [31] proposes an analytical framework of five enablers of data democratization, which are: 1) Broader data access, 2) Self-service analytics tools, 3) Development of data and analytics skills, 4) Collaboration and knowledge sharing, and 5) Promotion of data value.

• Harland et al., said a key requirement for successful data democratization in an organization is that data are organized in a way that it is accessible for ad-hoc analyses [52] . An organizational structure that empowers employees to proactively improve their routines and initiate and implement improvements on their own

• Equally important for data democratization success is availability of high-quality data [52] .

• Successful data democratization implementation must start with strong data leadership constituting of technology, data culture and literacy, and change management [53] .

• Support from the leadership is vital to create value from data and operational level capabilities such as the data democratization [54] .

• Key dimension of data democratization is the culture (i.e., the commonly accepted set of values within the organization that guides the actions of employees) among the associates [55] .

• The process of data democracy starts with nurturing a data driven culture in an organization where the principle would be “data for everyone, acquire, process, leverage the value, and share structured, and reusable data legally” for multiple benefits [55] .

• Data democratization critical success factors to include (Data Management Policies and Practices, Data Sharing Culture, Data Management Trainings, Top Management Support, Availability and Access to Analytical Tools, Organizational Vision and Plan, Employee Willingness to Collaborate and Share Data, Establishment of Data Security and Privacy, and Shared Responsibility over Organizational Data) [56] .

• Data democratization requires strong governance for data and process management as well as a related culture, education, training, and tooling to enable this process irrespective of the actors’ domain of expertise and technical know-how [57] .