Abstract
The exponentially growing usage and its benefits of digitizing data, as well as changes in data management practices, are continuously moderating the global economy and how organizations perform business operations. While data security is at its prime importance, organizations tend to move from traditional data silo models to inter-organizational data openness and sharing models where everyone who needs access to data is empowered to find, access, interoperate, and reuse (FAIR) data without gatekeepers. The phenomenon of ‘data democratization' has gained attention among practitioners and scholars in recent years as a process of managing inter-organizational open data and empowering employees to develop their innovative, creative, and decision-making skills. Thus, the objective of this book chapter is to focus on this new phenomenon of data democratization and understand how it can support the development of data-driven innovative skills among employees to generate social and economic values based on the results of an in-depth systematic literature review.
TopIntroduction
The value of the data depends on the context of use, use of data analytics and the extent to which data can be reused (Adikari et al. 2021; Smet et al. 2021). With the fourth industrial revolution (Industry 4.0), real-time operations with data-driven practices have gained more attention as an enabler for a fast and efficient way of resolving issues and improving the operations (Harland et al. 2022; Sedera et al. 2016; Sedera et al. 2022). According to NewVantagePartners (2022) “Data and artificial intelligence (AI) Executive Survey 2022,” among 94 Fortune 1000 companies who participated in the survey, 97% of organizations claimed that they are investing in data initiatives, which is an increasing trend compared to previous years (Lokuge and Sedera 2018). The advancement of technologies and the value of data have led to exponential growth to empower business leaders and decision-makers to make decisions based on facts, trends, and statistics by accessing the right information at the right time (Leonard 2018; Lokuge and Duan 2021; Lokuge et al. 2018). With this move, industries realized the importance of openness of data for decision-makers by removing data silos (Lokuge and Sedera 2020; Lokuge et al. 2020a). The necessity for this transformation has been further encouraged with the recent pandemic and the nature of business moving to a decentralized hybrid form such as “working from home.” As such, democratization of data has gained attention as a novel process of broadening access to data, using data, interacting, and sharing appropriate data for employees that are required to perform their job and make decisions with the help of data and analytics tools within the boundaries of legal, confidentiality and security limitations (Samarasinghe et al. 2022). In the traditional information technology (IT)-ownership model, for managing organizational data, usually limited access is provided for technical data users such as IT team, data engineers etc. However, with the introduction of novel concepts such as data democratization, both technical data users as well as non-technical users who work on the front-line of the business will have access to data and empowered for efficient and accurate decision-making, collaboration, and will enable to become a contributor of the knowledge-sharing culture (Awasthi and George 2020; Labadie et al. 2020; Lefebvre et al. 2021; Lokuge et al. 2020b; Lokuge et al. 2018).
One of the key benefits of data democratization is to empower employees to make decisions based on digitalization and digitization to achieve competitive advantage over traditional data management practices (Sedera et al. 2021; Török 2020). Empowerment fosters creativity and innovation by offering true ownership to employees for their work and contribution. Data in raw format is less informative and time consuming to sort, analyze, and make decisions. With the help of analytical solutions, both data and analytics together help organizations and employees for accurate decision-making and to optimize their performance (Lokuge and Sedera 2014b; Lokuge et al. 2019; Sedera and Lokuge 2019b; Sedera and Lokuge 2019c; Su et al. 2022).
Key Terms in this Chapter
Open Government Data: Government data that is free to use, reuse and re-distribute with any citizen to empower them to engage and contribute to policy decisions.
Data Democratization: A process of providing inter-organizational, open data access to both technical (i.e., data scientists, IT specialists etc.) and non-technical (i.e., marketing specialists, product teams etc.) employees to find, access, interoperate, share, and reuse data in their job within the appropriate legal, security, and confidentiality boundaries and using self-service analytic tools to make decisions and develop ideas to support business functions and resolve problems.
Citizen Data Scientist: In an organization which practices data democratization, a citizen data scientist is an individual who performs data analytics work but who does not necessarily have formal knowledge of data analytics or statistics or is not a data scientist in the profession.
Data: Data is a raw form of information and knowledge that can be available in formats such as texts, figures, numerals, symbols, or observations. It is also considered as a strategic asset to the organization.
Open Data: Data that is free to use, reuse and share with anyone to empower their engagement and contribution.
Data-driven Innovation: A form of developing and implementing new ideas with the use of data and analytics to develop or improve new products, services, processes, business models, and markets, by empowering employees at every level to use data and analytics to improve their innovative capabilities.
Innovation: Discovery and implementation of a new idea related to a product, service, process, or organization policy that is new or an improvement to the organization.