The following section presents evidence on the fundamentals of open finance, data democratization, and their intersection. First, the transition to open finance and the conceptual lens of data democratization are theoretically presented, using a step-by-step approach based on the selected documents as reported in the PRISMA algorithm. Then, the adoption of data democratization as a strategic lens to valorize data in an open finance ecosystem is addressed through empirical findings from survey respondents.
Transition to open finance
The phenomenon of open finance relies on the digitalization of the finance sector. This digitalization trend, led by advancements in information technologies, began in the early 2000s and evolved following the global financial crisis of 2007-2008, which gave rise to innovative waves under the terms of fintech and open banking (Zavolokina et al. 2016; Puschmann 2017; Buckley et al. 2020; Zeller and Dahdal 2021; Paul and Sadath 2021; Patki and Sople 2022; Standaert and Muylle 2022). In 2016, the Competition and Markets Authority in the United Kingdom reported that the market concentration of large banks was harming consumers and that new entrants had difficulty accessing the finance sector. As a result, major account providers are required to develop an open banking structure, laying the foundation for financial sector reforms worldwide (Klein and da Costa Farias 2023). The COVID-19 pandemic has contributed to digitally interconnecting multiple stakeholders, leading to the creation of a true financial ecosystem where data from various segments of the finance sector are used synergistically. This period has also been used across the globe to increase access to the financial system for unbanked individuals in both developed and developing countries, indeed facilitating the recovery of the economy in the postpandemic world (Klein and da Costa Farias 2023). Scholars have increasingly written about open finance. The seminal studies of Grassi et al. (2022) and Ferretti and Petkoff (2022) noted that open finance commences with open banking data and extends to all financial activity. Klein and da Costa Farias (2023) confirm this view and highlight the potential of open finance to contribute to economic growth, reduce inequality, and enhance macroeconomic policy effectiveness. By leveraging scholars’ definitions (Online Appendix F), we coded the concepts to synthetically present the transition from open banking to open finance (Fig. 2).
Open banking allows access, sharing, and reuse of banking data (accounts, transactional) through technological infrastructure in a banking ecosystem comprising customers, financial institutions, fintech startups, and third-party providers to enable innovative and competitive financial products, services, and business models. Customers own the control of data through their consent, and technological measures are undertaken to ensure data security. Open finance incorporates open banking by extending access, sharing, and reuse to other financial data (e.g., pensions, mortgages, insurance, and portfolios) in a financial ecosystem of financial and nonfinancial stakeholders (including techfin companies or bigtechs, developers, and nonfinancial third parties) as we move toward an open data economy. Within open finance, scholars emphasize the need to ensure a trustworthy ecosystem through the use of decentralized technologies. Open finance stakeholders must consider the ethical and equity implications of their initiatives to foster a more inclusive and sustainable ecosystem. The word cloud of key phrases confirms the relevance of the phenomenon and highlights the role of regulatory initiatives (Online Appendix C).
The transition to open finance involves various approaches and paces across the globe. In the European Union, the United Kingdom, Brazil, and India, initiatives are primarily regulatory driven; in the United States, China, Switzerland, and Canada, they are market driven; in Australia, they follow a hybrid approach; and in South Korea, Singapore, Hong Kong SAR China, and Japan, they are voluntary driven (Choi 2021; Zeller and Dahdal 2021; UNECE 2022; Patki and Sople 2022; Schneider 2023; Klein and Da Costa Farias 2023) (Table 1). Regulatory-driven initiatives often specifically target the financial sector or address it indirectly. For example, the United Kingdom is noted for its introduction of open banking initiatives, such as the open banking framework, and its call for feedback on open finance (FCA 2021; UNECE 2022). The European Union established the gold standard for personal data management through the General Data Protection Regulation (GDPR), subsequently introducing open banking with the Payment Services Directives (PSD, PSD2) and evolving toward open finance consultations (ASEAN 2021; AGTT 2022; European Commission 2022; Ferretti and Petkoff 2022; UNECE 2022; Standaert and Muylle 2022) to build an open finance framework (European Commission 2023; EY 2023). Market-driven initiatives, spearheaded by financial companies, are evident in countries such as Canada, China, and the United States (Zeller and Dahdal 2021; Patki and Sople 2022; Struwe 2023). Moreover, countries such as Singapore advocated a voluntary approach that originated from collaboration among financial companies and has been sustained by regulators (Zeller and Dahdal 2021). Australia adopts a hybrid strategy characterized as a “multi-institutional coalition that includes private and public sectors” (Zeller and Dahdal 2021, p. 11), positioning itself at the forefront of the transition to open finance, or more precisely, open data (Struwe 2023). A seminal implementation of the open finance concept emerged with the Fintech Open Platform initiative for fintech firms in South Korea in 2016, although its adoption was limited and completed in 2019 (Choi 2021). Other countries, such as Brazil and India, have either referenced already developed frameworks or facilitated interbank data sharing without specific regulations (Zeller and Dahdal 2021; MeitY 2022; RBI 2022; UNECE 2022; Gonçalves and Araujo 2022).
Moreover, the transition toward open finance appears fragmented in both developed and developing countries. In developed countries (e.g., European Union countries, the United Kingdom, and the United States), regulators and market players have accelerated initiatives to open data within the financial sector. However, access to public and customer data varies across countries and often remains localized to specific initiatives (Nguyen and Boundy 2017; Ziegler 2021; Schneider 2023; Stefanelli and Manta 2023). In contrast, Lynn et al (2020), Gupta et al. (2023), and Fang and Zhu (2023) highlight that developing countries (e.g., Brazil, India, China, Malaysia, Indonesia, and South Africa) are strategically adopting open banking and open finance initiatives to bridge existing gaps and, in some cases, outpace more developed countries.
Open banking and open finance initiatives require data to be findable, accessible, interoperable, and reusable (Van Praag and Muçi 2023). Scholars recognize data as a resource to be valorized, aligning these principles implicitly with data democratization. O’Leary et al. (2021) developed an assessment to measure the degree of data openness. Specifically, open finance initiatives involve the exchange of transactional data (e.g., payments, loans, investments, mortgages, pensions, savings accounts, and insurance), nonfinancial data (e.g., turnover on platforms, tax data, energy consumption, utility subscriptions, social security), public data (data disclosed to supervisory authorities), social data (e.g., lifestyle), and alternative data (e.g., weblogs) (Morvan 2020; Richardson 2020; Ashofteh and Bravo 2021; Grassi et al. 2022; Odorovic 2023; Van Praag and Muçi 2023). These data assets require an ecosystem where data must be standardized (McKinsey 2021b; European Union 2022; Dinçkol et al. 2023) with data formats of reference, compatibility, semantic, prescriptive, and performance standards (Dinçkol et al. 2023), enabling technological advancement, market adoption, and impact on stakeholders overall.
Technological advancement
Open banking sets the stage for connecting financial companies through advanced data-sharing technologies beyond proprietary interfaces. These technologies encompass application programming interfaces (APIs) that facilitate system access for third parties (Gozman et al. 2018; Omarini 2018; Rühl and Palomo Zurdo 2019; Lynn et al. 2020; Zetzsche et al. 2020; Choi 2021; Cortet et al. 2021; O’Leary et al. 2021; Zeller and Dahdal 2021; Ziegler 2021; Braine and Shukla 2022; Barman and Hallur 2022; Patki and Sople 2022; Standaert and Muylle 2022; McKinsey 2023; OECD 2023a; Kotarba 2023; Odorovic 2023; Hjelkrem and Lange 2023; Stefanelli and Manta 2023; Dinçkol et al. 2023; Koh 2023), mobile internet and 5G technology that support smartphone penetration and the adoption of mobile banking and digital payments (O’Leary et al. 2021; Paul and Sadath 2021; Barman and Hallur 2022), data analytics (Salmony 2018; Cortet et al. 2021; Paul and Sadath 2021), distributed ledger technologies such as blockchain and encryption (Gozman et al. 2018; Rühl and Palomo Zurdo 2019; Torun and Duygu 2020; Zetzsche et al. 2020; Paul and Sadath 2021; UNECE 2022; Barman and Hallur 2022; Schneider 2023; Odorovic 2023; Damiani and Tumelero 2023), cloud storage and computing (Fahey 2014; Lynn et al. 2020; Zetzsche et al. 2020; Paul and Sadath 2021; UNECE 2022; Barman and Hallur 2022), biometric technologies (Salmony 2018; Arner et al. 2021), sensors (Fahey 2014; Standaert and Muylle 2022), and virtual or augmented reality (Barman and Hallur 2022; Indriasari et al. 2022).
These technologies and infrastructures are at the core of the evolution of open finance and follow standard guidelines (e.g., licensed third party) and standard technological practices that together allow data liquidity (Wixom et al. 2023). However, enabling data liquidity should consider the implementation costs that should not surpass the actual benefits (Omarini 2018) while preserving the security and protection of data. The security of data can be enhanced through technological advancements related to public keys, private keys, and signature algorithms, which are found in the encryption, anonymization, or pseudonymity of the operations (Salmony 2018; Paul and Sadath 2021). Consequently, senior managers must carefully examine the relationships between data stakeholders and assess how technological applications can yield data-driven outcomes where benefits outweigh costs.
Technological evolution within developed countries typically progresses through three phases. First, ATMs and call centers are introduced to increase customer service. Second, infrastructure (e.g., APIs, cloud computing, networks) should be developed to eliminate barriers and support market adoption. Third, advanced technologies (e.g., artificial intelligence, blockchain, APIs, and robotic process automation), business architectures, collaborative approaches, and strategies for customer trust and acquisition are integrated to scale the business (Passi 2018; Omarini 2018; Melnychenko et al. 2020). In developing countries, financial conglomerates and techfin companies often build technological infrastructures to equip and catch up with more advanced economies (Lynn et al. 2020; Buckley et al. 2020; Barman and Hallur 2022; Iman et al. 2023).
Market adoption
The market adoption of open banking initiatives signifies a shift toward enhancing processes (e.g., score models for financial institutions, easy or quick application for customers, data portability, data interoperability) (Rühl and Palomo Zurdo 2019; O’Leary et al. 2021; Hjelkrem et al. 2022a; OECD 2023a; Perrazzelli 2023; Kotarba 2023; Odorovic 2023; Hjelkrem and Lange 2023; Dinçkol et al. 2023; Klein and da Costa Farias 2023), personalized products and services (Standaert and Muylle 2022; Kotarba 2023), APIs for reducing technological complexity and for monetization (McKinsey 2023), digital assets (O’Leary et al. 2021), tokenization (Zeller and Dahdal 2021), digital identity (Cortet et al. 2021; UNECE 2022), regulatory sandbox (O’Leary et al. 2021; Patki and Sople 2022; Perrazzelli 2023; Škrabka 2023; Stefanelli and Manta 2023), tools to control personal data (Cortet et al. 2021; Klein and da Costa Farias 2023), data portals (European Union 2022), ungraded products and services (e.g., digital payments, personal finance-supported tools, strong authentication, account aggregators, micro savings, credit file enhancement, debt advice, product comparison, investment tools, charitable giving, loans/alternative lending, crowdfunding, token offering, microloans, small and medium enterprise financial management, e-commerce payment, identity verification, dashboards, on-demand products and services) (Salmony 2018; Rühl and Palomo Zurdo 2019; Lynn et al. 2020; Cortet et al. 2021; O’Leary et al. 2021; Zeller and Dahdal 2021; Patki and Sople 2022; Standaert and Muylle 2022; OECD 2023a; Škrabka 2023; Schneider 2023; Kotarba 2023; Klein and da Costa Farias 2023), targeted advertising (Cortet et al. 2021), new channels (Omarini 2018; O’Leary et al. 2021; Kotarba 2023), and opening banking-as-a-service or banking platforms (Lynn et al. 2020; O’Leary et al. 2021; Paul and Sadath 2021; Barman and Hallur 2022; Schneider 2023; Stefanelli and Manta 2023).
The transition toward open finance, coupled with the full adoption of platforms and white labeling, enables the creation of innovative products and services that integrate multiple industries. Within the finance sector, banking services complement more diverse offerings, such as asset management (Choi 2021) or insurance. For example, securing insurance when a customer purchases a house, car, or travel ticket exemplifies such applications. Several scholars have identified data sharing in the mobility, housing, health, and energy industries (O’Leary et al. 2021; Standaert and Muylle 2022) as ideal for open finance applications, although the dynamics remain unexplored. Future open finance companies may evolve to provide all-in-one platform services (e.g., banks as services, banks as platforms) (Rühl and Palomo Zurdo 2019; Paul and Sadath 2021; Dinçkol et al. 2023), effectively becoming digital life partners.
The adoption of open banking and open finance services has progressed at a slower pace in developed countries, as populations in these regions may be more comfortable with traditional financial systems (Gupta et al. 2023). In developing countries, Fang and Zhu (2023) and Lynn et al (2020) highlight that open finance initiatives reduce the propensity to obtain loans from traditional banks and expand access to credit for previously excluded individuals.
Impact on stakeholders
Open banking and open finance initiatives significantly impact stakeholders in the financial ecosystem. The primary outcomes of open banking include heightened operational efficiency and agility (Patki and Sople 2022; Standaert and Muylle 2022; Hjelkrem et al. 2022a; McKinsey 2023; Kotarba 2023), empowered customer relationships and experience (Cortet et al. 2021; O’Leary et al. 2021; Paul and Sadath 2021; European Union 2022; Barman and Hallur 2022; Patki and Sople 2022; Standaert and Muylle 2022; Hjelkrem et al. 2022a; Kotarba 2023), improved financial analysis and planning (Rühl and Palomo Zurdo 2019), a broader spectrum of products and services (Gozman et al. 2018; O’Leary et al. 2021; Zeller and Dahdal 2021; Patki and Sople 2022; Standaert and Muylle 2022; Kotarba 2023), enhanced data ownership and security, and refined decision-making processes (O’Leary et al. 2021). These factors synergistically contribute to bolstering transparency and reducing information asymmetry (Gozman et al. 2018; O’Leary et al. 2021; Odorovic 2023; Dinçkol et al. 2023; Klein and Da Costa Farias 2023), improving security (Ziegler 2021; Barman and Hallur 2022; Patki and Sople 2022; Standaert and Muylle 2022; Škrabka 2023; Kotarba 2023), fostering customer empowerment and inclusion (O’Leary et al. 2021; Zeller and Dahdal 2021; Barman and Hallur 2022; Patki and Sople 2022; Standaert and Muylle 2022; Iman et al. 2023; OECD 2023a; Klein and da Costa Farias 2023), driving innovative and competitive business models (Gozman et al. 2018; Omarini 2018; Rühl and Palomo Zurdo 2019; Bär and Mortimer-Schutts 2020; O’Leary et al. 2021; Zeller and Dahdal 2021; Ziegler 2021; Paul and Sadath 2021; Patki and Sople 2022; McKinsey 2023; Škrabka 2023; Kotarba 2023; Dinçkol et al. 2023), ensuring sustained performance (Cortet et al. 2021; O’Leary et al. 2021; Paul and Sadath 2021), and cultivating strategic collaborations and partnerships (O’Leary et al. 2021; Stefanelli and Manta 2023). Nevertheless, there are significant risks on the horizon, notably cyber risk (Kotarba 2023; Van Praag and Muçi 2023), that could compromise stakeholders’ privacy and identity (Salmony 2018; Kotarba 2023; Van Praag and Muçi 2023).
Moving toward open finance, by integrating data from financial and nonfinancial industries, reflects both the benefits and risks inherent in this broader scope. At the ecosystem level, data sharing fosters greater coordination, communication, complementarity, and synergies for companies while offering customer-centric solutions. An open question for scholars is who will play the role of data custodian, responsible for the security and availability of data: a company such as a bank, a technology such as blockchain, or hybrid solutions. Banks may have an advantage by being the first to provide digital identity (Cortet et al. 2021), leveraging the quality data they already possess and the trust of consumers. While complete data openness is limitedly feasible when dealing with sensitive customer information, enabling relevant stakeholders to access and utilize specific data can facilitate the evolution toward open finance, or more broadly, toward an open data economy.
The impact of open finance initiatives on stakeholders varies between developed and developing countries. Developed countries benefit primarily from servicing and monitoring activities (e.g., improved workforce allocation, reduced friction in data intermediation), whereas developing countries gain advantages in decision-making and onboarding processes (McKinsey 2021b). Overall, the potential democratization of financial services for previously excluded individuals fosters financial inclusion (Nguyen and Boundy 2017; Klein and da Costa Farias 2023; Fang and Zhu 2023).
Data democratization
Data democratization is an emerging and promising paradigm (Awasthi and George 2020) established on companies adopting findable, accessible, interoperable, and reusable data principles (Labadie et al. 2020; Eichler et al. 2022) to gain a competitive advantage (Awasthi and George 2020; Labadie et al. 2020; Samarasinghe et al. 2022; Corinium 2023; Lefebvre et al. 2023). This concept incorporates the necessity of data sharing (Eichler et al. 2022) and extends further by integrating the data principles “within the boundaries of legal, confidentiality and security limitations by transferring data ownership and responsibility to empower users for efficient and accurate decision-making, promote collaboration, and create a knowledge-sharing culture in an organization” (Samarasinghe et al. 2022, p. 3). The concept of data democratization first emerged in the 2000s and has since evolved (Table 2). In the reviewed studies, scholars referred to data democratization as processes, organizational culture, capabilities, and governance.
Awasthi and George (2020), Hinds et al. (2022), and Fahey (2014) define data democratization as organizational processes that open organized data to as many employees as possible, enabling them to provide valuable insights. For this to be effective, data must be cleaned, categorized, contextualized, and stored in an accessible and usable repository. This ensures that employees with different data literacy levels can import, export, document, and analyze data, thereby embracing data-driven decision-making.
Scholars have also associated data democratization with building an organizational culture (Awasthi and George 2020; Hyun et al. 2020; Lefebvre et al. 2021; Harland et al. 2022). As suggested by Awasthi and George (2020), the management literature can explore this organizational culture in the fields of corporate management to enhance competitive advantage and resource capability to strengthen strategic positions, considering the resource-based view, resource-dependent theory, or employee empowerment. The concept of data democratization as an organizational culture involves companies providing access to and using data intraorganization (Awasthi and George 2020; Lefebvre et al. 2021; Samarasinghe et al. 2022) or interorganization (Treuhaft 2006; Bellin et al. 2010; Fahey 2014; Awasthi and George 2020; Lefebvre et al. 2021; Samarasinghe et al. 2022). When employees from different seniorities and departments access and use data, this is referred to as an intraorganizational approach. Conversely, when employees provide access and use of their own data to customers or third parties, strategies related to marketing, customization, and service-based strategies can be developed, and a better experience can be provided. Additionally, companies can explore mergers and acquisitions, joint ventures, and other interorganizational relationships to access and use data resources.
The concept of data democratization as capabilities is reflected in several initiatives. Lefebvre et al. (2021, 2023) identify five enabling initiatives: broader data access through catalogs, self-service analytics tools for business intelligence, the development of data and analytics skills with training, collaboration and knowledge sharing between data specialists and nonspecialists, and the promotion of data value by empowering data specialists within product teams. Several scholars support Lefebvre et al.’s findings (Labadie et al. 2020; Samarasinghe et al. 2022; Sterne 2023). These initiatives have similarly led to the adoption of data democratization practices in both born digital companies and traditional companies (Lefebvre et al. 2021). Hinds et al. (2022) also consider the possibility of accessing and using data to create more equitable outcomes.
However, data democratization as a capability introduces several concerns related to data quality, security, cybersecurity, and privacy, all of which require clear governance. Data governance ensures quality data-driven decision-making; oversees data resources during sharing, accessing, and reusing; and minimizes misuse in data dissemination. Embracing data governance ensures maintaining a strategic advantage through compliance measures (Lefebvre et al. 2021, 2023), centralized or decentralized architectures, and data literacy (Awasthi and George 2020; Lefebvre et al. 2021; Klein and da Costa Farias 2023). Compliance with regulatory and internal policies ensures proper data use and addresses legal issues such as portability, confidentiality, ethical and responsible use, and security (Awasthi and George 2020; Arner et al. 2021; Hinds et al. 2022; OECD 2023a). Centralized versus decentralized architectures ensure accountability, responsibility, transparency, and trust (Morrow and Zarrebini 2019). Centralized architecture provides ideas for removing silos and centralizing data to functions or authorities that control and monitor data principles. In contrast, decentralized architectures adopt distributed ledger technologies, tokenization, and encryption, entrusting data principles into protocols and smart contracts without a central authority (Rühl and Palomo Zurdo 2019; Morrow and Zarrebini 2019; Liu et al. 2022, 2023). Finally, employees need to follow appropriate training to empower them to leverage proprietary resources (e.g., data, data tools). This training enables more literate employees, such as developers or data scientists, to focus on more sophisticated tasks. Most sophisticated employees should become domain experts to contribute to department and company growth. Several scholars promote the concept of citizen data scientists, advocating that companies have employees proficient in data at various levels for genuinely making data-driven decisions (Nguyen and Boundy 2017; Awasthi and George 2020; Labadie et al. 2020; Lefebvre et al. 2021, 2023; Harland et al. 2022; Shapiro 2023).
Data democratization initiatives are often localized. For example, open data initiatives (e.g., digital access, literacy) supported by federal funds in major cities such as New York are not available nationwide. Some smaller cities, such as Boston, Chicago, Louisville, and Philadelphia, have expanded initiatives that include data training and public access to hardware and software, often targeting lower-income populations but on a smaller scale. Similarly, Stefanelli and Manta (2023) highlight disparities among European countries. In contrast, developing countries such as India are experiencing widespread penetration of digital tools in synergy with the growth of digital banking and financial technologies (Lynn et al. 2020; Iman et al. 2023). Thus, data democratization can enable the valorization of data through specific processes, organizational culture, capabilities, and governance initiatives at a broader scale. While many financial practitioners have begun to incorporate data democratization, to our knowledge, previous academic studies have limitedly addressed the managerial and strategic implications, particularly within an open finance ecosystem.
Data democratization and open finance
Although financial practitioners have started to incorporate data democratization in their activities, this academic study primarily addresses it as a theoretical lens to enable open finance initiatives. To complement theoretical knowledge, the financial practitioners who responded to the survey provided empirical evidence of the positive contributions of data democratization (Table 3). Specifically, the survey considers the perspectives of financial decision-makers on how data democratization may help organizations solve data challenges and improve a company’s performance, both financial and nonfinancial, in the short and long term. The responders were similarly divided into the United Kingdom (51%) and the United States (49%). Among the decision-makers, 54% of the respondents were in senior management positions (e.g., founders, CEOs, CROs, CFOs, CDOs, CIOs, executives, managers), and others were in more junior positions, allowing for multiple perspectives. The survey well incorporated decision-makers from different segments of the finance sector: commercial banking (29%), investment banking (23%), asset management (24%), insurance (23%), and others (17%). The size of the companies assessed spanned from large companies with 250 or more employees (61%) to medium companies with 50-249 employees (15%) to small companies with 1-49 employees (24%), indicating that the analysis comprehended not only more established institutions but also emerging players. In addition, 4% were companies with less than five years, 9% had between five and ten years, and the remainder had more than 10 years; thus, the survey captured companies in the start-up, scale-up, and corporation phases. Finally, the majority of companies were experienced in data practices. This latter confirmed the relevance of valorizing data in financial companies.
Decision-makers indeed confirm previously identified data democratization initiatives. Financial decision-makers confirm that data democratization is reliant on adopting initiatives encompassing processes, organizational culture, capabilities, and governance, which produce specific improvements in their organizations. Specifically, data democratization initiatives generate improvements in process efficiency (22%) and transparency (2%), which are crucial for fostering an open and competitive financial environment. Within organizational culture, these initiatives enhance the ability to serve customers (10%), develop products (6%), and maintain customer connections (5%), driving innovative service offerings and competitive differentiation. Capabilities are significantly improved, with notable enhancements in monitoring performance (18%), decision-making (17%), customer profiling (10%), understanding market trends (10%), identifying customer needs (5%), and strategic pricing (2%), all of which are vital for staying competitive in the evolving finance sector. Governance benefits from improved data management (11%), risk management (7%), compliance and reporting (4%), and data and financial literacy (2%), ensuring robust oversight and quality data outputs. These improvements collectively drive enhanced organizational performance, resulting in better short-term performance (8%), overall organizational results (7%), competitive advantage (6%), long-term performance (6%), and sustainable success (5%). Data democratization also fosters innovation (3%), improves financial performance (3%), and achieves nonfinancial performance outcomes (2%), emphasizing its essential role in the open finance ecosystem.
The analysis delineates the impacts of data democratization initiatives on processes, organizational culture, capabilities, and governance. Notably, efficiency emerges as a significant process enhancement, followed by contributions to insights for performance management and decision-making. Organizational culture shifts toward prioritizing customer service through enhanced products and services. Governance focuses on data management and risk management, emphasizing its role in safeguarding data assets in a sector where data are pivotal. These results elucidate data democratization initiatives within an open finance ecosystem. Although some aspects are more recurrent among informants, the qualitative nature of the survey also captures emerging aspects.
