Dr. Bart Baesens has recently published a book “Analytics in a Big Data World: The Essential Guide to Data Science and its Applications”. The book discusses how analytics can be used to create strategic leverage and identify new business opportunities.
The focus of this book is not on the mathematics or theory, but on the practical application. Formulas and equations will only be included when absolutely needed from a practitioner’s perspective. It is also not our aim to provide exhaustive coverage of all analytical techniques previously developed, but rather to cover the ones that really provide added value in a business setting.
It is available at the following link-
Dr Baesens talk to us more about his book.[divider]
[dropcap size=”2″]BB[/dropcap]Prof. Dr. Bart Baesens: I wanted to write a book which is relevant to decisions that all businesses will need to make in the coming years. As the number of practical applications for data skyrockets, learning how to extract business value from big data becomes a competitive requirement. Big data sets are assets that can be leveraged quickly and inexpensively, if tackled wisely!
My book Analytics in a Big Data World addresses this seemingly Herculean task of coming to grips with multiple channels of data and sculpting them into quantifiable value. This book is for business professionals who want a focused, practical approach to big and data analytics. I hereby focus on case studies, real-world application, and steps for implementation, using theory and mathematical formulas only when strictly necessary!
AIM: How is this book different from other similar books in market today?
BB: The book provides a comprehensive, end-to-end process overview of how to put analytics to work to solve concrete business problems. Many current text books only focus on discussing various predictive techniques with too much theoretical focus, hereby losing the general picture. Building upon my experience in both academia and business, the book provides a unique blend of a state of the art, scientific approach and a clear practitioner focus. The book also has plenty of tricks and tips on how to successfully apply analytics and includes (performance) benchmarks in a variety of different business contexts (e.g. credit risk management, marketing, fraud detection, web analytics).
AIM: How did you start your career in analytics?
BB: After my MSc studies in Business Engineering at KU Leuven (Belgium), I started a PhD which I completed in 2003 with the title “Developing Intelligent Systems for Credit Scoring using Machine Learning techniques”. Soon afterwards I got hired as an assistant professor at KU Leuven (Belgium) and guest lecturer at the University of Southampton (United Kingdom). I continued my research on analytics for credit scoring and at the same time expanded my focus towards other application areas such as marketing analytics (response and retention modeling), fraud analytics, and web analytics.
I currently have a research team of about 10 PhD students and 2 postdocs working on these topics. See www.dataminingapps.com for an overview of our activities. Next to my academic research, I regularly tutor, advise and provide consulting support to international firms with respect to their analytics strategy. I also currently teach 4 courses on the topic of analytics to businesses, which are offered both in classroom as well as webinar format.
AIM: What did it take to have a book published?
BB: Well, throughout the past few years, I have done lots of (strategic) consulting for many firms across the globe. Throughout my experience and activities, I learned that a first key step to fully leverage and unleash the power of analytics, is to create awareness about the topic with all the business stakeholders involved across all levels of decision making. To my biggest frustration, I found no adequate background material to help me achieve this. Hence, this inspired me to write this book whereby my key aim is to bridge the gap between analytics and the business!
AIM: Are you working on any other book right now?
BB: Yes, in fact, I am working on two more books at the moment. The first one is entitled: “Predictive Analytics: Techniques and Applications in Credit Risk Modelling” to be published by Oxford University Press in 2015. It is the second book out of a series of three and it goes into much detail about how to construct, backtest, benchmark and stress test high performing analytical models for credit risk assessment.
The second book I am working on is in a totally different area. It is entitled Basic Programming in Java and will also be published by Wiley. It is based upon a postgraduate course I teach and the experiences we gained by using Java in our analytical research.
AIM: What do you suggest to new graduates aspiring to get into analytics space?
BB: First of all, I would strongly encourage graduates to pursue a career in analytics.
According to a recent McKinsey report, the US alone faces a shortage of 140,000 to 190,000 people with deep analytical skills and another 1,5 million managers capable of making decisions based on big data and analytics. On top of that, it is a fascinating world with lots of new developments and challenges. In order to be a good analyst, one needs to have a multidisciplinary profile. Hence, I believe graduates should first have a sound and solid quantitative background in statistics.
Next, they should also make sure they possess deep business knowledge and communication/presentation skills. Especially the latter are also very important in order to bridge the often observed communication gap between the business and the analyst which we talked about earlier.
AIM: How do you see Analytics evolving today in the industry as a whole? What are the most important contemporary trends that you see emerging in the Analytics space across the globe?
BB: Well, let me discuss some trends which I consider important based upon both my industry and research experience. First of all, analytics is about being actionable and simple. It’s not about complex numbers, black box models or statistics. In our collaborations with firms, we have found that simple analytical models (e.g. regression models, decision trees) typically perform well in many settings.
Hence, the best investment firms can make to boost the performance of their analytical models is by investing in data and improving data quality. That’s why in my book I also devoted a whole section to this topic.
From a technical perspective, next to the analytical models themselves, firms should also thoroughly consider how to appropriately monitor, backtest and integrate these models with other (e.g. marketing, risk management) applications. I believe these activities currently pose quite a bit of challenges for which more practice-focused research is necessary!
From a software perspective, an important trend is the emergence of open source solutions. However, at this stage, many of these solutions are not scalable and focus too much on the technical aspects of analytics instead of providing solutions to business problems.
Finally, data and analytics is everywhere and all around. It speaks for itself that this creates huge challenges from a privacy perspective. Firstly, data about individuals can be collected without these individuals being aware about it.
Secondly, people may be aware that data is collected about them, but have no say in how the data is being analyzed and used. Hence, regulatory authorities have to think about new regulations, whereas researchers should focus more on the development of privacy friendly analytical techniques.
AIM: Anything else you wish to add?
BB: I really hope people enjoy reading my book, and I very much look forward to receiving their feedback and hearing about their experiences in the wonderful world of analytics![divider] [spoiler title=”Biography of Prof. Dr. Bart Baesens” style=”fancy” icon=”plus-circle”]
Professor 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.[/spoiler]
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