Analytics in a Big Data World: Interview with Prof. Dr. Bart Baesens about his new book

1.    Why did you decide to write a book on Big Data and Analytics?

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!

2.    How does this book differentiate from other books on the topic?

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. marketing, fraud detection, credit risk management, web analytics).

3.    What are the most important trends and challenges in marketing analytics?

Well, let me discuss some trends which I consider important based upon both my industry and research experience.  First of all, I believe marketing analytics is about being actionable and simple.  It’s not about complex numbers, black box models or statistics.  In our marketing analytics projects, we have found that simple analytical models (e.g. regression models, decision trees) typically perform well in many settings such as response and retention modeling, customer lifetime value modeling and segmentation.  Hence, the best investment firms can make to boost the performance of their marketing analytical models is not by buying expensive software and trying out complex techniques, but rather 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 marketing analytical models themselves, firms should also thoroughly consider how to appropriately monitor, backtest and integrate these models with their other marketing applications such as advertisement, new product development, next best offer campaigns, ….  Closing this loop poses quite a bit of challenges which are also addressed in the book!  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.

4.    What advice you have for people pursuing a career in (marketing )analytics?

First of all, I would congratulate you!  Analytics is a fascinating world to work in with lots of new developments and challenges.  On top of that, there is a huge industry demand for analysts/data scientists and it is likely to continue for quite some time.  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.  In order to become a good data scientist, one needs to have a multidisciplinary profile.  Hence, I believe aspiring data scientists should continuously perfection their analytical skills by keeping up to date with new developments in quantitative modeling and/or statistics.  Next, they should also make sure they elaborate 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 typically observe in industry!

5.    Any chances to get a free book?

Just send your most funny, exciting or useful ‘analytics’ quote(s) to by the end of May and win one of three books. The winners and their quotes will be announced by all BAQMaR channels.

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