From the evolution of public data ecosystems to the evolving horizons of the forward-looking intelligent public data ecosystem empowered by emerging technologies

Slides for the talk delivered as part of EGOV-CeDEM-ePart 2024 (EGOV2024) conference. Recognizing the multifaceted nature of Public Data Ecosystems, and informed by a a theoretical six-generation Evolutionary Model of Public Data Ecosystems (EMPDE) proposed by the previous research, this study validates it to enhance its practical applicability. The model is validated through a real-life examination in five European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This empirical validation provides insights into PDEs dynamics and variations of implementations across contexts, particularly focusing on the 6th generation of forward-looking PDE generation named “Intelligent Public Data Generation” which represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence (AI), Natural Language Processing tools, Generative AI, and Large Language Models with potential to contribute to both automation and augmentation of business processes within these ecosystems. By transcending their traditional status as a mere component, evolving into both an actor and a stakeholder simultaneously, these technologies catalyse innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.

Read the paper here -> https://link.springer.com/chapter/10.1007/978-3-031-70274-7_25

1. ANASTASIJA NIKIFOROVA , University of Tartu, Estonia MARTIN LNENICKA , University of Hradec Králové , Czech Republic PETAR MILIC , University of Pristina – Kosovska Mitrovica, Kosovska Mitrovica, Serbia MARIUSZ LUTEREK , University of Warsaw, Warsaw, Poland MANUEL PEDRO RODRÍGUEZ BOLÍVAR , University of Granada, Granada, Spain EGOV2024 – IFIP EGOV- CeDEM -EPART 2024 FROM THE EVOLUTION OF PUBLIC DATA ECOSYSTEMS TO THE EVOLVING HORIZONS OF THE FORWARD-LOOKING INTELLIGENT PUBLIC DATA ECOSYSTEM EMPOWERED BY EMERGING TECHNOLOGIES

2. The public sector faces complex societal, regulatory, and technical challenges due to the growing volume of data and the need to optimize its use for greater efficiency [1-2] Understanding how data-related elements influence system development is crucial for alignment with goals [3] Over the past decade, the ecosystem perspective has become increasingly important, highlighting the interconnected and evolving nature of social, technological, and information systems, particularly in Public Data Ecosystems [4-10]. BACKGROUND

3. a dynamic network comprising interconnected elements that enable a range of data-related activities that encompass the entire data lifecycle, from collection and management to sharing and reuse, involving diverse stakeholders with varied objectives PUBLIC DATA ECOSYSTEM

4. Understanding the dynamics and evolution of PDEs , including their key characteristics and interrelationships, is crucial for their stability and the success of data-driven projects [22] This allows to trace the ecosystem's development step-by-step and pinpoint key turning points [12] , which may be both internal and external factors, e.g., technological developments such as cloud computing, the Internet of Things, big data and Artificial Intelligence that drive the digital transformation at different levels [23] BACKGROUND

5. proposed a typology of PDEs, conceptual model with the elements that make up these ecosystems identified six evolutionary generations, conceptualizing them into a theoretical Evolutionary Model of Public Data Ecosystems (EMPDE) In our previous study, we:

6. proposed a typology of PDEs, conceptual model with the elements that make up these ecosystems identified six evolutionary generations, conceptualizing them into a theoretical Evolutionary Model of Public Data Ecosystems (EMPDE) In our previous study, we: Designed as a result of a systematic literature review spanning three decades, this model can be considered theoretically robust, h owever (!!!) it requires empirical validation to enhance its practical applicability

7. to validate the theoretical Evolutionary Model of Public Data Ecosystems through a real-life examination in European countries THE AIM OF THIS STUDY

8. an expert assessment questionnaire with European national experts in sample countries a questionnaire / protocol developed , in which national experts were asked to examine : METHODOLOGY the validity of the identified generations by determining their existence and relevance, the start and end time of each generation as well as its duration constituting a temporal analysis , their opinion on the potential influence of regional or local-level "generations" and how citizens at different levels interact with these generations of PDEs, the potential additional country-specific generations their country's PDE can be characterized by, the existence and importance assessment of meta-characteristics of these generations what is expected to influence PDEs in the future Find protocol on  Nikiforova, A., Lnenicka , M., Milic , P., Luterek , M., & Bolívar , M. P. R. (2024). The evolution of public data ecosystems . EGOV2024 – IFIP EGOV- CeDEM -EPART 2024 (IFIP-EGOV), Belgium . Zenodo . https://doi.org/10.5281/zenodo.11146461

9. reviewed the relevant documents that provided countries assessments in : METHODOLOGY: SAMPLE COUNTRIES (1) data infrastructures and data-related services represented in e-government, digital, and ICT development indices , such as the E-Government Development Index, ICT Development Index, Network Readiness Index, and EU's eGovernment Benchmark ; (2) open (government) data, and indices of data availability and impacts , such as the Open, Useful and Re-usable data Index ( OURdata ), Open Data Maturity Index, and Open Data Inventory ; (3) technologies and approaches shaping work with public data , such as the Global Cloud Ecosystem Index, Global Cybersecurity Index, AI Index, and Government AI Readiness Index . To ensure diversity, selection encompassed both highly competitive and less competitive countries Latvia, Serbia , Spain , Czech Republic , Poland

10. EXISTENCE AND IMPORTANCE ASSESSMENT OF SIX GENERATIONS OF PDES all six generations are present across the sample countries, with a few exceptions for individual countries - the absence of the initial (1st and 2nd) and the most advanced (5th and 6th) generations in Serbia and Spain ; the absence of the 6th generation aligns with our earlier expectations h igh importance assigned to the 4th and 5th generations across most countries

11. GENERATIONAL PERIODS OF P UBLIC DATA ECOSYSTEMS Temporal Differences: st art and end dates of PDE generations varied between countries Earlier adoption of 4th and 5th generations in countries with advanced digital infrastructure T he 2nd generation existed in all countries until 2011 as an independent generation & continues to exist today , albeit in a different form - as a specific subtype of PDEs, such as a local government open data ecosystem or a smart city data ecosystem

12. AN IMPORTANCE ASSESSMENT OF GENERATIONS’ META-ATTRBIUTES IN SELECTED COUNTRIES v - very important, i – important, l - low importance, n – not important red – rather not important, yellow – rather little important, blue – rather important, green – rather very important COMPONENT IMPORTANCE: Technology - i ncreasing importance in later generations, particularly in the 5th and 6th Governance s tructures are rucial across all generations but evolving in complexity KEY STAKEHOLDERS AND ACTORS: Transition from government-centric models to multi-stakeholder ecosystems Active involvement of private sector and civil society in 4th and 5th generations DATA TYPES AND PROCESSES: Shift from raw data (1st and 2nd generations) to value-added and AI-generated data (5th and 6th generations) Enhanced focus on data interoperability and reusability in the 6th generation

13. A VALIDATED SIX-GENERATION MODEL OF THE EVOLUTION OF PUBLIC DATA ECOSYSTEMS

14. KEY ADJUSTMENTS TO THE MODEL GENERATION 1 (FOUNDATIONAL PDE): establishing of basic digital infrastructure r einforced role of foundational governance mechanisms GENERATION 2 (DATA WAREHOUSING AND INTEGRATION): data integration and management practices GENERATION 3 (OPEN DATA INCEPTION): transparency and public access to data i ntegration of user feedback loops for continuous improvement GENERATION 4 (OPEN DATA MATURITY): the transition to user-centered data services interoperability and cross-sector data collaboration GENERATION 5 (SMART DATA): e xpanded role of big data analytics and IoT integration advanced data processing techniques (e.g., machine learning) data privacy and security measures GENERATION 6 :

15. THE 6TH GENERATION KEY CHARACTERISTICS: AI and machine learning as central components Real-time data processing and predictive analytics Development of autonomous decision-making systems POTENTIAL IMPACTS: Enhanced decision-making capabilities and predictive governance Personalization of public services based on intelligent data insights Increased stakeholder engagement through AI-driven platforms CHALLENGES AND CONSIDERATIONS: Addressing ethical concerns around AI use. Ensuring data transparency and avoiding bias in AI-driven decisions .

16. THE 6TH GENERATION 1. CONTINUED FOCUS ON CORE COMPETENCIES AND INFRASTRUCTURE Data competencies - e mphasis on skills, dynamic processes, and policies Infrastructure: « advanced » data structures, such as data lakes and spaces 2. INCLUSION OF INTELLIGENT DATA AND EMERGING TECHNOLOGIES Sources e xpands beyond public agencies to include businesses, enthusiasts, and networks Key technologies: i ntelligent algorithms, NLP tools, AI, ML, and Generative AI Importance of addressing ethical and legal challenges associated with AI 3. AI AND GENERATIVE AI AS GAME-CHANGERS Automation & augmentation: AI tools for processing, recommendations, and analysis Personalized interaction & e nhanced user engagement facilitating data exploration for non-experts 4. AI’S ROLE IN IMPROVING DATA LIFECYCLE Enhanced data documentation: AI- augmented metadata creation , datasets ’ annotations & context Improved data usability - data tagging and description to enhance findability & understandability

17. THE 6TH GENERATION 5. CHALLENGES AND PARADOXES WITH NEW TECHNOLOGIES Data quality – capable to improve but also requires for effective AI & Generative AI implementation—focus on accuracy, reliability, and metadata Privacy and ethics - heightened importance of anonymization & protection against de-identification 6. TRANSPARENCY AND EXPLAINABLE AI (XAI) Importance of XAI: Ensures accountability and builds trust in AI-driven decision-making Need for interpretability: Reviving focus on the understanding of AI mechanisms, especially in public data ecosystems 7. COMMUNITY ENGAGEMENT AND COLLABORATION Collaborative approach fostering dialogue among users, developers, and stakeholders Enhancing data ecosystem broadening the utility and applicability of AI through community input.

18. AI and emerging technologies as drivers of intelligent data ecosystems Emphasis on real-time data analytics, predictive governance, and personalized public services SIX-GENERATION MODEL OF THE EVOLUTION OF PUBLIC DATA ECOSYSTEMS ADJUSTED BASED ON EMPIRICAL DATA TO BETTER REFLECT CURRENT TRENDS AND FUTURE DIRECTIONS CONCEPTUAL SIX-GENERATION MODEL OF THE EVOLUTION OF PUBLIC DATA ECOSYSTEMS DESIGNED BASED ON THE SLR

19. CONCLUSIONS The study traces the development of Public Data Ecosystems across six generations  e mpirical validation of EMPDE led to adjustments in the model, particularly with the emergence of the 6th generation The revised model provides a framework for policymakers to guide the transition towards more advanced PDEs The study raises some c hallenges and c onsiderations : * s tress on the need for high-quality data as a foundation for effective AI implementation *i mportance of addressing ethical and privacy concerns , particularly with AI and Generative AI *i mportance of active collaboration between public agencies, businesses, and the community to maximize the benefits of AI *t he necessity of Explainable AI (XAI) to maintain trust and accountability in AI-driven processes The PDE and AI landscape will continue to evolve, requiring ongoing research and adaptation There is a n eed for continued testing of the revised model in different contexts , e xpanding research to include broader public data sub-ecosystems & i dentifying future scenarios where AI can be leveraged to enhance the PDE framework * * WHILE AI OFFERS SIGNIFICANT POTENTIAL, IT MUST BE IMPLEMENTED THOUGHTFULLY TO AVOID RISKS  B ALANC E INNOVATION AND CAUTION

20. THANK YOU FOR YOUR ATTENTION! Contact information: https://anastasijanikiforova.com/ [email protected] https://www.linkedin.com/in/anastasija-nikiforova-466b99b3/

21. Some references… 1. Clausen , T. H., Demircioglu , M. A., & Alsos , G. A. (2020). Intensity of innovation in pub-lic sector organizations : The role of push and pull factors . Public Administration , 98(1), 159-176. 2. Valle- Cruz , D., & García-Contreras , R. (2023). Towards AI- driven transformation and smart data management : emerging technological change in the public sector value chain . Pub-lic Policy and Administration , https://doi.org/10.1177/09520767231188401 3. Zuiderwijk , A., Janssen, M., & Davis , C. (2014). Innovation with open data : Essential ele-ments of open data ecosystems . Information Polity , 19(1-2), 17-33. 4. Harrison , T. M., Pardo , T. A., & Cook , M. (2012). Creating open government ecosystems : A research and development agenda . Future Internet, 4(4), 900-928. 5. Lnenicka , M., Nikiforova, A., Luterek , M., Azeroual , O., Ukpabi , D., Valtenbergs , V., & Machova R. (2022). Transparency of open data ecosystems in smart cities : Definition and assessment of the maturity of transparency in 22 smart cities . Sustainable Cities and Society , 82, 103906. 6. Martin, S., Turki, S., & Renault, S. (2017). Open data ecosystems : Introducing the stimula- tor function . In A. Kő , & E. Francesconi (Eds.), International Conference on Electronic Government and the Information Systems Perspective , EGOVIS 2017 ( pp . 49-63). Cham, Springer International Publishing . 7. Othman , R., binti Ku Mahamud , K. R., & bin Abu Bakar , M. S. (2017). Public private col-laboration model in spatial data infrastructure : Potential elements. International Journal of Supply Chain Management , 6(3), 325-331. 8. Reggi , L., & Dawes , S. S. (2022). Creating Open Government Data ecosystems : Network relations among governments , user communities , NGOs and the media . GIQ, 39(2), 101675. 9. Shah , S. I. H., Peristeras , V., & Magnisalis , I. (2021). Government big data ecosystem : Definitions , types of data , actors , and roles and the impact in public administrations . ACM Journal of Data and Information Quality , 13(2), 1-25. 10. Van Loenen , B., Zuiderwijk , A., Vancauwenberghe , G., Lopez-Pellicer , F. J., Mulder , I., Alexopoulos , C., ... & Flores , C. C. (2021). Towards value-creating and sustainable open data ecosystems : A comparative case study and a research agenda . JeDEM-eJournal of eDemocracy and Open Government , 13(2), 1-27. 11. Geisler , S., Vidal , M. E., Cappiello , C., Lóscio , B. F., Gal, A., Jarke , M., ... & Rehof , J. (2022). Knowledge-driven data ecosystems toward data transparency . ACM Journal of Data and Information Quality (JDIQ), 14(1), 1-12. 12. Heimstädt , M., Saunderson , F., & Heath , T. (2014). From toddler to teen : Growth of an open data ecosystem . JeDEM-eJournal of eDemocracy and Open Government , 6(2), 123-135. 21. Lnenicka , M., Nikiforova, A., Luterek , M., Milic , P., Rudmark , D., Neumaier , S., Kević , K., Zuiderwijk , A., Rodríguez Bolívar , M. P., Understanding the development of public data ecosystems : from a conceptual model to a six-generation model of the evolution of public da-ta ecosystems (2023), SSRN, http://dx.doi.org/10.2139/ssrn.4831881 22. Aaen , J., Nielsen , J. A., & Carugati , A. (2022). The dark side of data ecosystems : A longitudinal study of the DAMD project . European Journal of Information Systems , 31(3), 288-312. 23. Beverungen , D., Hess, T., Köster , A., & Lehrer , C. (2022). From private digital platforms to public data spaces : Implications for the digital transformation . Electronic Markets , 32(2), 493-501.