Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is also author of the books Credit Risk Management: Basic Concepts, published by Oxford University Press in 2008; and Analytics in a Big Data World published by Wiley in 2014.
His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.
For more information, see Bart’s new e-learning course, "Advanced Analytics in a Big Data World," at https://support.sas.com/edu/schedules.html?id=2169&ctry=US.
Articles by Bart Baesens
Many organizations nowadays are struggling with finding the appropriate data stores for their data, making it important to understand the differences and similarities between data warehouses, data marts, ODSs, and data lakes. All these data structures clearly serve different purposes and user profiles, and it is necessary to be aware of their differences in order to make the right investment decisions.
Posted September 26, 2018
Although security is often related to privacy, they are not synonymous. Data security can be defined as the set of policies and techniques to ensure the confidentiality, availability, and integrity of data at all times. Data privacy refers to the fact that the parties accessing and using the data do so only in ways that comply with the agreed-upon purposes of data use in their roles. These purposes can be expressed as part of a company's policy, but are also subject to legislation. In this way, several aspects of security can be considered as necessary instruments to guarantee data privacy.
Posted May 11, 2018
Many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Common causes of bad data quality include multiple data sources; limited computing resources: Lack of sufficient computing resources; changing data needs; and different processes using and updating the same data.
Posted March 26, 2018
Nowadays, many firms are already using big data and analytics to manage and optimize their customer relationships. Both technologies can also prove beneficial to leverage a firm's other key assets: its employees! Various HR analytics (also called workforce analytics) examples can be thought of.
Posted September 20, 2017
Big data and analytics are all around these days. Most companies already have their first analytical models in production and are thinking about further boosting their performance. However, far too often, these companies focus on the analytical techniques rather than on the key ingredient: data. The best way to boost the performance and ROI of an analytical model is by investing in new sources of data which can help to further unravel complex customer behavior and improve key analytical insights.
Posted May 15, 2017
While companies often view processes from their frame of reference, "cutting" processes up according to department, business objective, or other internal aspect, customers obviously do not act according to the same taxonomy and—from the perspective of the company—appear to jump from process to process, from department to department, and from channel to channel, making it difficult for businesses to truly follow a customer through his or her whole journey.
Posted April 07, 2017
The rise of big data with new sources of data for analytics represents new opportunity to put data to work in organizations for a wide range of uses. A developing use case for leveraging data analytics on large datasets is fraud discovery.
Posted October 05, 2016
Comparing Commercial Versus Open Source Software for Analytics
Posted June 08, 2016
The emergence of big data, characterized in terms of its four V's—volume, variety, velocity, and veracity—has created both opportunities and challenges for credit scoring.
Posted April 04, 2016
A typical organization loses about 5% of its revenues to fraud each year. The total cost of non-health insurance fraud in the U.S. is estimated to be more than $40 billion per year. These numbers stress the importance and need of finding sophisticated tools to both detect and prevent fraud. Big data and analytics offer a new valuable toolkit in the fight against fraud.
Posted November 13, 2015
Translated to an analytical setting, Ockham's principle, also known as Ockham's razor, basically states that analytical models should be as simple as possible, free of any unnecessary complexities and/or assumptions.
Posted April 08, 2015
A good analytical model should satisfy several requirements, depending on the application area. In order to achieve business relevance, it is of key importance that the business problem to be solved is appropriately defined, qualified, and agreed upon by all parties involved at the outset of the analysis.
Posted June 17, 2014