🗓️ 🗓️🗓️ Save the date – September 6th – for “PPPS’2023 – Proactive and Personalised Public Services: Searching for Meaningful Human Control in Algorithmic Government” workshop as part of International Federation for Information Processing (IFIP) EGOV-CEDEM-EPART2023 organized in cooperation with Digital Government Society, where we – Anastasija Nikiforova(University of Tartu (Faculty of Science and Technology, Institute of Computer Science, Chair of Software Engineering) & European Open Science Cloud Task Force “FAIR Metrics and Data Quality”),Nitesh Bharosa (Delft University of Technology, Faculty of Technology, Policy and Management & GovTech), Dirk Draheim (TalTech University), Kuldar Taveter (University of Tartu (Faculty of Science and Technology, Institute of Computer Science, Chair of Computer-Human Interactions)) – will initiate a discussion about personalised and proactive public services, discussing them and learning from the EGOV community about the practice of applying proactive and personalised services in different countries 🎯🎯🎯
As part of this workshop: 💡 we will talk about the concepts of public services, reactive and proactive models of public services, and models of their personalization; 💡considering the regulatory frameworks of two countries with forward-looking digital governments – 🇪🇪 Estonia and 🇳🇱 the Netherlands – we will compare design choices across key variables for proactive public services and reflect on the outcomes for citizens. Good and bad public service delivery practices from 🇪🇪 and 🇳🇱 will be presented with a focus on lessons learned from both kinds of experiences; 💡we will ask participants to share their views on public services and the levels of proactivity and personalisation of these services aiming to develop concepts for holistic proactive and personalised public service delivery 💡will try to jointly identify novel innovative and best practices for designing and setting up proactive and personalised public services. It is expected that this will lead to a clearer vision of the “as-is” model and the necessary transition to the “to-be” model, their underlying factors, as well as pitfalls of which governments should be aware when designing, developing, and setting up proactive and personalised public services.
The purpose of this workshop is to explore and discuss the challenges and conditions for proactive and personalized public service delivery. The long-term objective is to come up with the framework of proactive and personalized public services expected to be applicable (1) to evaluate proactive public services already in use, thus identifying their gaps (if any), and identifying an agenda for the improvements, (2) to develop public services, ensuring they are qualitative and best practices-compliant by design.
Sounds interesting? Register for EGOV and join us – 6th September, 2023, Budapest, Hungary.
The week was full of impressions (positive) from the the 24th Annual International Conference on Digital Government Research in the charming Gdańsk (Poland), which started with our workshop, followed by the keynote talk delivered by the former President of Poland & Nobel Prize laureate Lech Wałęsa, until the very last session & coming to my hometown and meeting John Malkovich there! Although this was already the 3rd working trip in the last 4 weeks and the 5th from the very beginning of June, during which I delivered two keynote lectures, presented two papers (with two more papers presented by my colleagues at other conferences), chaired the workshop with several more events & activities, it was still an absolutely great experience, where we finally had the 3rd edition of our workshop “Identification of high value dataset determinants: is there a silver bullet for efficient sustainability-oriented data-driven development?” as part of dg.o2023, which brought more than 20 participants, with whom we jointly tried to understand:
💡What can be the country-specific HVD determinants (aspects)? Incl. who should be the expected beneficiary of the availability of HVD? what are the current approaches towards HVD determination?
💡What mechanisms or methods can be put in place to determine them?
💡Can this be done (semi-)automatically?
💡How a framework for determining country-specific HVD could look like?
As part of the workshop, we also validated the results of our Towards High-Value Datasets determination for data-driven development: a systematic literature review (Anastasija Nikiforova, Nina Rizun, Magdalena Ciesielska, Charalampos Alexopoulos, Andrea Miletič) paper we expect to present to EGOV community, which has been already named a “sound in the noise” (we work hard to correspond to this characteristic!). As part of the above, we verified whether the findings from the literature are relevant, valid & complete discussing:
💡What can be data-centered characteristic of HVD? (and should they be predefined?) 💡What are the expected characteristics of the HVD determination indicators, i.e. (1) ex-ante / ex-post / both?, (2) qualitative / quantitative / both? (3) Internal (such as usage statistics) / external (e.g., report, indices, charters) / both?, (4) SMART / not necessarily? Diving then into the above questions, as well as evaluating relevance of indicators identified previously (lit-re & current practices / ad-hoc approaches & previous workshops)
Many thanks to everyone who participated in the ICEGOV, ICOD or DGO workshops (more than 60 people in total), as well as thanks to Maria, who was part of the ICOD workshop!
Thanks to the organizers, including but not limited to the local organizing committee – Gdansk University, Digital Government Society, Emerald Publishing – for giving an opportunity to have a good time and to finally meet the colleagues in person (some of whom never met before despite a relatively long collaboration)!
June 28 I had the honor to participate in the opening of CARMA2023 – 5th International Conference on Advanced Research Methods and Analytics “Internet and Big Data in Economics and Social Sciences” delivering my keynote “Public data ecosystems in and for smart cities: how to make open / Big / smart / geo data ecosystems value-adding for SDG-compliant Smart Living and Society 5.0?” in the spectacular city of Sevilla, Spain 🇪🇸 🇪🇸 🇪🇸. What a honor to open the conference, immediately after the inaugural speech by organizers and sponsors, including representatives of Joint Research Center, European Commission (JRC), who even mentioned the topics I covered in my keynote (not limited to them, of course) as those that make this conference an event to attend and to learn from!!!
In this talk, as the title suggests, I:
elaborated on the concepts of public /open data (incl. OGD), smart city and SDG and how are they related?
introduced the concept of Society 5.0 and how is it related to open data?
and finally, and more importantly, public/ open data ecosystem – what it is? what does it consist of?
I then dived into (1) data-related aspects of the public data ecosystem, i.e. what are the data-related prerequisites for a sustainable and resilient data ecosystem? (2) data portal / platforms as entry points and how to make it sufficiently attractive for the target audience? (3) stakeholder engagement – how to involve the target audience? what are the benefits of their involvement? and some more things.
CARMA is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges with main focus on the topics such as Internet and Big Data sources in economics and social sciences including Social media and public opinion mining, Web scraping, Google Trends and Search Engine data, Geospatial and mobile phone data, Open data and public data, Big Data methods in economics and social sciences such as Sentiment analysis, Internet econometrics, AI and Machine learning applications, Statistical learning, Information quality and assessment, Crowdsourcing, Natural Language processing, Explainability and interpretability, the applications of the above including but not limited to Politics and social media, Sustainability and development, Finance applications, Official statistics, Forecasting and nowcasting, Bibliometrics and sciencetometrics, Social and consumer behaviour, mobility patterns, eWOM and social media marketing, Labor market, Business analytics with social media, Advances in travel, tourism and leisure, Digital management, Marketing Intelligence analytics, Data governance, and Digital transition and global society, which, in turn, expects contributions in relation to Privacy and legal aspects, Electronic Government, Data Economy, Smart Cities, Industry adoption.
In addition to the regular sessions, poster session and two keynotes, a Special JRC session (EC) took place, during which Luca Barbaglia, Nestor Duch Brown, Matteo Sostero and Paolo Canfora presented projects they work on.
Great thanks goes to organizers and sponsors of CARMA2023 – Universidad de Sevilla, Cátedra Metropol Parasol, Cátedra Digitalización Empresarial, IBM, Universitat Politècnica de València, Joint Research Center – European Commission and Coca-Cola, who made this event a true success. Enjoyed this experience very much! Excellent venue! Great audience! ¡Muchas gracias!
Nikiforova, A., Rizun, N., Ciesielska, M., Alexopoulos, C., & Miletič, A. (2023). Towards High-Value Datasets determination for data-driven development: a systematic literature review.
This time I would like to recommend for reading the new paper “Overlooked aspects of data governance: workflow framework for enterprise data deduplication” that has been just presented at the IEEE-sponsored International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). This “just”, btw, means June 19 – the day after my birthday, i.e. so I decided to start my new year with one more conference and paper & yes, this means that again, as many of those who congratulated me were wishing – to find the time for myself, reach work-life balance etc., is still something I have to try to achieve, but this time, I decided to give a preference to the career over my personal life (what a surprise, isn’t it?) 🙂 Moreover, this is the conference, where I am also considered to be part of Steering committee, Technical Program committee, as well as publicity chair. During the conference, I also acted as a session chair of its first session, what I consider to be a special honor – for me the session was very smooth, interactive and insightful, of course, beforehand its participants & authors and their studies, which allowed us to establish this fruitful discussion and get some insights for our further studies (yes, I also got one beforehand one very useful idea for further investigation). Thank you all contributors, with special thanks to Francisco Bonilla Rivas, Bruck Wubete, Reem Nassar, Haitham Al Ajmi.
And I am also proud with getting one of four keynotes for this conference – prof. Eirini Ntoutsi from the Bundeswehr University Munich (UniBw-M), Germany, who delivered a keynote “Bias and Discrimination in AI Systems: From Single-Identity Dimensions to Multi-Discrimination“, which I heard during one of previous conferences I attended and decided that it is “must” for our conference as well – super glad that Eirini accepted our invitation! Here, I will immediately mention that other keynotes were excellent as well – Giancarlo Fortino (University of Calabria, Italy), Dofe Jaya (Computer Engineering Department, California State University, Fullerton, California, USA), Sandra Sendra (Polytechnic University of Valencia, Spain).
The paper I presented is authored in a team of three – Otmane Azeroual, German Centre for Higher Education Research and Science Studies (DZHW), Germany, myself – Anastasija Nikiforova, Faculty of Science and Technology, Institute of Computer Science, University of Tartu, Estonia & Task Force “FAIR Metrics and Data Quality”, European Open Science Cloud & Kewei Sha, College of Science and Engineering University of Houston Clear Lake, USA – very international team. So, what is the paper about? It is (or should be) clear that data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this (incl. but not limited due to the growing volume of data, incl. due to the adoption of cloud technologies, use of multiple different sources, the proliferation of connected personal and work devices in homes, stores, offices and supply chains), but the result of non-unique or duplicate records is a degraded data quality, which, in turn, ultimately leads to inaccurate analysis, poor, distorted or skewed decisions, distorted insights provided by Business Intelligence (BI) or machine learning (ML) algorithms, models, forecasts, and simulations, where the data form the input, and other data-driven activities such as service personalisation in terms of both their accuracy, trustworthiness and reliability, user acceptance / adoption and satisfaction, customer service, risk management, crisis management, as well as resource management (time, human, and fiscal), not to say about wasted resources, and employees, who are less likely trust the data and associated applications thereby affecting the company image. This, in turn, can lead to a failure of a project if not a business. At the same time, the amount of data that companies collect is growing exponentially, i.e., the volume of data is constantly increasing, making it difficult to effectively manage them. Thus, both ex-ante and ex-post deduplication mechanisms are critical in this context to ensure sufficient data quality and are usually integrated into a broader data governance approach. In this paper, we develop such a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in medium to large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way, while the presented framework is integrated into one of the most popular data quality tools – Data Cleaner.
In short, in this paper we:
first, present methods for how companies can deal meaningfully with duplicate data. Initially, we focus on data profiling using several analysis methods applicable to different types of datasets, incl. analysis of different types of errors, structuring, harmonizing, & merging of duplicate data;
second, we propose methods for reducing the number of comparisons and matching attribute values based on similarity (in medium to large databases). The focus is on easy integration and duplicate detection configuration so that the solution can be easily adapted to different users in companies without domain knowledge. These methods are domain-independent and can be transferred to other application contexts to evaluate the quality, structure, and content of duplicate / repetitive data;
finally, we integrate the chosen methods into the framework of Hildebrandt et al. [ref 2]. We also explore some of the most common data quality tools in practice, into which we integrate this framework.
After that, we test and validate the framework. The final refined solution provides the basis for subsequent use. It consists of detecting and visualizing duplicates, presenting the identified redundancies to the user in a user-friendly manner to enable and facilitate their further elimination.
With this paper we aim to support research in data management and data governance by identifying duplicate data at the enterprise level and meeting today’s demands for increased connectivity / interconnectedness, data ubiquity, and multi-data sourcing. In addition, the proposed conceptual data governance framework aims to provide an overview of data quality, accuracy and consistency to help practitioners approach data governance in a structured manner.
In general, not only technological solutions are needed that would identify / detect poor quality data and allow their examination and correction, or would ensure their prevention by integrating some controls into the system design, striving for “data quality by design” [ref3, ref4], but also cultural changes related to data management and governance within the organization. These two perspectives form the basis of the wealth business data ecosystem. Thus, the presented framework describes the hierarchy of people who are allowed to view and share data, rules for data collection, data privacy, data security standards, and channels through which data can be collected. Ultimately, this framework will help users be more consistent in data collection and data quality for reliable and accurate results of data-driven actions and activities.
Sounds interesting? Read the paper -> here (to be cited as: Azeroual, O., Nikiforova, A., Sha, K. (2023, June). Overlooked aspects of data governance: workflow framework for enterprise data deduplication. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). IEEE (in print))
International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023) is collocated with The International Conference on Multimedia Computing, Networking and Applications (MCNA2023), which are sponsored by IEEE (IEEE Espana Seccion), Universitat Politecnica de Valencia, Al ain University. Great thanks to the organizers – Jaime Lloret, Universitat Politècnica de València, Spain & Yaser Jararweh, Jordan University of Science and Technology, Jordan & Marios C. Angelides, Brunel University London, UK & Muhannad Quwaider, Jordan University of Science and Technology, Jordan.
References:
Azeroual, O., Nikiforova, A., Sha, K. (2023, June). Overlooked aspects of data governance: workflow framework for enterprise data deduplication. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). IEEE (in print).
Hildebrandt, K., Panse, F., Wilcke, N., & Ritter, N. (2017). Large-scale data pollution with Apache Spark. IEEE Transactions on Big Data, 6(2), 396-411
Guerra-García, C., Nikiforova, A., Jiménez, S., Perez-Gonzalez, H. G., Ramírez-Torres, M., & Ontañon-García, L. (2023). ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards Data Quality by Design. Data & Knowledge Engineering, 145, 102152.
Corrales, D. C., Ledezma, A., & Corrales, J. C. (2016). A systematic review of data quality issues in knowledge discovery tasks. Revista Ingenierías Universidad de Medellín, 15(28), 125-150.
June 5, I was delighted to be invited to be a keynote at the HackCodeX Forum, delivering a keynote titled “Data Quality as a prerequisite for your business success: when should I start taking care of it?“ in my hometown – Riga, Latvia. HackCodeX Forum is a one-day event where international experts share their experience and knowledge about emerging technologies and areas such as Artificial Intelligence, Security, Data Quality, Quantum Computing, Sustainability, Open Data, Privacy, Ethics, Digital Services (with a keynote from CEO of SK ID Solutions – one of the solutions that make Estonia the #1 digital nation) etc. This time I was invited to cover the topic of Data Quality and I was happy to do so, especially considering the fact that the HackCodeX Forum is an event that closes one of the leading hackathons in Europe, which Riga was fascinated and passionated about, and this is evidenced by the rich list of advertisement we all saw in the last weeks and months (Delfi, Haker.lv, kripto.media, kursors.lv, labsoflatvia.lv to name just a few), which this year held in Latvia and brought together around 500 developers, designers and entrepreneurs to create and innovate, solving 5 challenges of this year:
🏆 ATEA challenge: Minimise manual work and drive data-powered decision-making
🏆 Emergn challenge: Improve the quality of life for people with disabilities
🏆 UI.COM & Riga TechGirls challenge: Help shoppers make more sustainable purchasing decisions
🏆 Game Changer Audio (GCA) challenge: Identify each individual note by listening to notes being played real-time
🏆Ministry of Education and Science challenge: Help make education hackable again!
Form me, in turn, yet another audience, yet another experience.
In short, in this Star Wars-style presentation (yes, I am a fan, and given the number of DQ memes in this style, I am not an exception and cannot say that I am a geek or a weird person, but rather a normal DQ/IT person), I urged “help R2D2 save the galaxy!“.
In a bit more detail, I elaborated on the importance and the relevance of the data quality regardless of the age of this topic [that is older than me], data quality management and the factors the DQM approach depends on. The popularity and importance of the topic is undoubtfully due to the amount of the data we are dealing with and the fact that we are living in the data-driven world, where data are everywhere – they are generated continuously, by multiple sources, which is not only about our devices, or sensors, but also about ourselves (however, with the help of the two above). This led to the fact that some time ago data have been claimed to be a new oil. Have you heard this? I am sure you were. But have you thought about this statement? is it true? false? something in between? Bingo! While there are commonalities between data and oil, they are rather small in number. One interesting reading devoted to this comes form Forbes. I.e. they admit that both artifacts – oil and data – can be seen as similar since both are “power”, including being the power of those, who own them. In other words, they compare data owners such as Alibaba, Google, Twitter, Facebook etc. to oil barons (100 years back from now). But, otherwise, more in-depth comparative analysis reveal mostly differences. To name just a few:
💡 oil is a finite resource, while data are not. Instead, data are effectively infinitely durable and reusable and treating them like oil, i.e. storing in siloes, reduces their value, usefulness and potential as whole;
💡another difference is in transportation, where oil requires huge amounts of resources to be transported to where and when it is needed, while for the data – they can be replicated indefinitely and moved around the world at very high speeds and, more importantly, at very low costs;
💡 Yet another difference lies in the usability of both – oil and data – when they have been already used once. While for the oil, when it is used, its energy is being lost (as heat or light), or permanently converted into another form such as plastic, data usefulness, in contrast, tend to increase with their actual usage, i.e. new uses arose, data are turned into training data at the very end etc.;
💡 as the world’s oil reserves dwindle, extracting it become increasingly difficult and expensive, while for the data – they are becoming increasingly available, incl. but not limited due to the technology advances as well as due to a high number and amount of data producers;
💡 and the last but not the least, oil drilling involves causing damage to the natural environment and exploitation of finite natural resources, while data mining doesn’t – at least there is no intrinsic damage to the environment and exploitation of finite natural resources. Of course, here we do not mention (but should not forget about) the electricity used to run the system and relatively low tendency of green computing (aka sustainable computing) for their further processing.
Thus, as Forbes suggests, if we want to talk about the data as a power source or fuel, it make much more sense to compare them with renewable sources 🌎🌎🌎 such as the sun ☀️, wind 💨 and tides 🌊. All in all, data can be seen to be more than oil. Hence the popularity and importance of the data quality topic.
The factors that can affect the DQM approach, in turn, can be different, starting with those implying from the relative nature of the data quality as a phenomenon, i.e., the definition, variety of (and non-ambiguity of) data quality dimensions, to which the data quality metrics are expected to be selected, DQ dynamism, dependence on the user and use-case etc. (some of the above are discussed in “Towards a data quality framework for EOSC“ and “Definition and Evaluation of Data Quality: a user-oriented data object-driven approach to data quality assessment”), as well as the data artifact whose quality is under analysis. In other words, is this about the data object or dataset? Database? Data repository? Information system?
If it is a data object, the next “level” of factors is data owner – known or unknown (third-party data such as open data), and their structure – structured, semi-structured, unstructured data?
While for the Information Systems / Software, I find that “think data quality first” and “data quality by design” are two mantras to be kept in mind. The later, however, is something we have studied together with my colleagues from Mexico , coming up with this modification of “quality by design” principle into “data quality by design”. I reported on the respective study before – “ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards data quality by design” (read here), where we proposed DAQUAVORD – a Methodology for Project Management of Data Quality Requirements Specification, which is based on the Viewpoint-Oriented Requirements Definition (VORD) method, and the latest and most generally accepted ISO/IEC 25012 standard, whose main idea was to start thinking of data quality as soon as the development of the system start to make sure that some data quality level is ensured by the design, i.e. transformed into both functional and non-functional requirements.
Alternatively, it can be done not necessarily before, but also during the development or even when the system is already in production. Some solutions exist here, but I typically use the opportunity to self-advertise previous projects and studies that I worked on, especially this one since it was based on the results of my PhD thesis, which is summarized “Definition and Evaluation of Data Quality: a user-oriented data object-driven approach to data quality assessment”, namely, Data Quality Model-based testing approach (DQMBT) for testing information systems that uses the data object-driven data quality model as a testing model, which was presented in the context of e-scooter system and Insurance System. Both, however, are rather ad-hoc approaches, whose main value lies in the conceptual idea, not the implementation, at least at this point.
For the repository, in turn, whether it is about the data warehouse, data lake? Or maybe even data lakehouse? For the later two, metadata and data governance become “must” to avoid GIGO (garbage in – garbage out effect) and turning the data lake into a data swamp, which is slightly addressed in “Combining data lake an data wrangling for ensuring data quality in CRIS“, incl. but not limited elaborating on why data wrangling should be given the preference over data cleaning.
The importance of both metadata and data governance was then emphasized, where for the later, the support from Elon Musk has been asked 😀 He was rather mentioned to support the speculations of data governance importance, which was once mentioned by him as a key to improve the product you are delivering, and I just wanted to make my words a bit more authoritative, i.e. he is seen to be more or less successful businessman, isn’t he? 😀
Big thanks to both the organizers – Helve, and supporters, who made both the hackathon and the forum a success. More precisely, Techchill, techhub, Lift 99, #RigaTechGirls, justjoin.it, Oradea.Tech.Hub, RTU design Factory, Startup Lithuania, Kaunas Technology University, Stratup Estonia, Spring Hub. kood / Johvi, Technopol, Enterprise Forum CEE, Slush, Aaltos, AWS (Amazon Web Services), Google for Startups, Junction, Bird Incubator, EdTech Estonia. Sphere,it, Codecamp, Nine brains, Draper Startup House, Eiropas Digitālās inovācijas centrs, 28Stone.
Anastasija Nikiforova, PhD 