Tag business intelligence

The International Open Data Day and my role of Keynote Speaker for the 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023) 🎤🎤🎤

Anastasija Nikiforova Leave a comment

This post is dedicated to two very pleasant events for me, namely the international Open Data Day 🎉🍾🥂, and the announcement of the keynote talk that I was kindly invited to deliver at the 5th International Conference on Advanced Research Methods and Analytics (CARMA) organized by Universidad de Sevilla, Cátedra Metropol Parasol, Cátedra Digitalización Empresarial, IBM, Universitat Politècnica de València and 🥁 🥁 🥁 Coca-Cola – what a delicious conference!🍸🍸🍸

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.

And as almost each and every conference, CARMA expects to have keynotes, which are two – Patrick Mikalef, who will talk about Responsible AI and Big Data Analytics, and me, whose keynote talk will be devoted to the topics I studied in recent years titled “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?” Sounds interesting? (I hope so) Stay tuned to know more! And return back, since I plan to reflect on the content of both talks and the conference in general.

The CARMA 2023 conference will be held on 28 June – 30 June 2023 in the University of Seville.

CFP: The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2023)

Anastasija Nikiforova Leave a comment

On behalf of the organizers and as a publicity chair, I sincerely invite you to consider submitting the results of your recent research to The International Conference on Intelligent Data Science Technologies and Applications (IDSTA2023), which will be held in conjunction Kuwait Fintech and Blockchain Summit.

Huge amount of data is being generated and transmitted everyday. To be able to deal with this data, extract useful information from it, store it, transmit it, and represent it, intelligent technologies and applications are needed. The International Conference on Intelligent Data Science Technologies and Applications (IDSTA) is a peer reviewed conference, whose objective is to advance the Data Science field by giving an opportunity for researchers, engineers, and practitioners to present their latest findings in the field. It will also invite key persons in the field to share their current knowledge and their future expectations for the field. Topics of interest for submission include, but are not limited to:

💡Applied Public Affairs, incl. but not limited to Campaign Management, Mass Communication Politics, Political Analysis, Survey Sampling
💡Business Analytics, incl. but not limited to Stock Market Analysis, Predictive Analytics, Business Intelligence
💡Finance, incl. but not limited to Risk Management, Algorithmic Trading, Fraud Detection, Financial Analysis
💡Computer Science, incl. but not limited to Database Management Systems, Scientific Computing, Computer Vision, Fuzzy Computing, Feature Selection, Neural Networks, Deep Learning, Meta-Learning, Process Mining, Artificial Intelligence, Data Mining, Big Data, Web Analytics, Text Mining, Natural Language Processing, Sentiment Analysis, Social Media Analysis, Data Fusion, Performance Analysis and Evaluation, Evolutionary Computing and Optimization, Hybrid Methods, Granular Computing, Recommender Systems, Data Visualization, Predictive Maintenance, Internet of Things (IoT), Web Scraping
💡Sustainability, incl. but not limited to Datasets on Sustainability, Sustainability Modeling, Energy Sustainability, Water Sustainability, Environmental Sustainability, Risk Analysis
💡Cybersecurity, incl. but not limited to Data Privacy and Security, Network Security, Communication Security, Cryptography, Fraud Detection, Blockchain
💡Environmental Science, incl. but not limited to GIS, Climatographic, Remote Sensing, Spatial Data Analysis, Weather Prediction and Tracking,
💡Biotechnologies, incl. but not limited to Gnome Analysis, Drug Discovery and Screening and Side Effect Analysis, Structural and Folding Pattern, Disease Discovery and Classification, Bioinformatics, Next-Gen Sequencing
💡Smart City, incl. but not limited to City Data Management, Smart Traffic, Surveillance, Location-Based Services, Robotics
💡Human Behaviour Understanding
💡Semi-Structured and Unstructured Data
💡Pattern Recognition
💡Transparency in Research Data
💡Data and Information Quality
💡GPU Computing
💡Crowdsourcing

🗓️🗓️🗓️ IMPORTANT DATES

  • Paper submission:  March 15, 2023  
  • Acceptance notification:  May 20th, 2023
  • Full paper camera-ready submission: October 1st, 2023
    Conference Dates: October 24-26, 2023
IDSTA2023Download

All papers that are accepted, registered, and presented in IDSTA2023 and the workshops co-located with it will be submitted to IEEEXplore for possible publication. 
For any inquiries, contact intelligenttechorg@gmail.com.

Submit the paper and meet our team in Kuwait in October, 2023!
 

With best wishes,

IDSTA2023 organizers

Data Science Seminar - UT&Swedbank - Data lake vs Data warehouse vs Data lakehouse

UT & Swedbank Data Science Seminar “When, Why and How? The Importance of Business Intelligence”

Anastasija Nikiforova 1 Comment

Last week I had the pleasure of taking part in a Data Science Seminar titled “When, Why and How? The Importance of Business Intelligence“. In this seminar, organized by the Institute of Computer Science  (University of Tartu) in cooperation with Swedbank, we (me, Mohammad Gharib, Jurgen Koitsalu, Igor Artemtsuk) discussed the importance of BI with some focus on data quality. More precisely, 2 of 4 talks were delivered by representatives of the University of Tartu and were more theoretical in nature, where we both decided to focus our talks on data quality (for my talk, however, this was not the main focus this time), while another two talks were delivered by representatives of Swedbank, mainly elaborating on BI – what it can give, what it already gives, how it is achieved and much more. These talks were followed by a panel moderated by prof. Marlon Dumas.

Data Science Seminar - UT&Swedbank - GIGO
Data Science Seminar - UT&Swedbank - Data lake vs Data warehouse vs Data lakehouse
Data Science Seminar - UT&Swedbank - panel

In a bit more detail…. In my presentation I talked about:

  • “Data warehouse vs. data lake – what are they and what is the difference between them?” – in a very few words – structured vs unstructured, static vs dynamic (real-time data), schema-on-write vs schema on-read, ETL vs ELT. With further elaboration on What are their goals and purposes? What is their target audience? What are their pros and cons? 
  • “Is the Data warehouse the only data repository suitable for BI?” – no, (today) data lakes can also be suitable. And even more, both are considered the key to “a single version of the truth”. Although, if descriptive BI is the only purpose, it might still be better to stay within data warehouse. But, if you want to either have predictive BI or use your data for ML (or do not have a specific idea on how you want to use the data, but want to be able to explore your data effectively and efficiently), you know that a data warehouse might not be the best option.
  • “So, the data lake will save my resources a lot, because I do not have to worry about how to store /allocate the data – just put it in one storage and voila?!” – no, in this case your data lake will turn into a data swamp! And you are forgetting about the data quality you should (must!) be thinking of!
  • “But how do you prevent the data lake from becoming a data swamp?” – in short and simple terms – proper data governance & metadata management is the answer (but not as easy as it sounds – do not forget about your data engineer and be friendly with him [always… literally always :D) and also think about the culture in your organization.
  • “So, the use of a data warehouse is the key to high quality data?” – no, it is not! Having ETL do not guarantee the quality of your data (transform&load is not data quality management). Think about data quality regardless of the repository!
  • “Are data warehouses and data lakes the only options to consider or are we missing something?“– true! Data lakehouse!
  • “If a data lakehouse is a combination of benefits of a data warehouse and data lake, is it a silver bullet?“– no, it is not! This is another option (relatively immature) to consider that may be the best bit for you, but not a panacea. Dealing with data is not easy (still)…
Data Lake or Data Warehouse? Data cleaning or data wrangling? How to ensure the quality of your data?Download

In addition, in this talk I also briefly introduced the ongoing research into the integration of the data lake as a data repository and data wrangling seeking for an increased data quality in IS. In short, this is somewhat like an improved data lakehouse, where we emphasize the need of data governance and data wrangling to be integrated to really get the benefits that the data lakehouses promise (although we still call it a data lake, since a data lakehouse, although not a super new concept, is still debated a lot, including but not limited to, on the definition of such).

However, my colleague Mohamad Gharib discussed what DQ and more specifically data quality requirements, why they really matter, and provided a very interesting perspective of how to define high quality data, which further would serve as the basis for defining these requirements.

All in all, although we did not know each other before and had a very limited idea of what each of us will talk about, we all admitted that this seminar turned out to be very coherent, where we and our talks, respectively, complemented each other, extending some previously touched but not thoroughly elaborated points. This allowed us not only to make the seminar a success, but also to establish a very lively discussion (although the prevailing part of this discussion took place during the coffee break – as it usually happens – so, unfortunately, is not available in the recordings, the link to which is available below).

The recordings are available here.