Creating Value with Big Data Analytics (book review)

Verhoef, Kooge and Walk have written a detailed and technical book on the application of data analytics to Marketing. While not stated in the title, the subtitle makes it clear: the book is dedicated to people in Marketing and Sales.

The strong academic background of the authors is transparent in the book, which is full of references. In the book, Big Data means lots of data (nothing related to Big Data architectures). While written with an academic angle, the book is full of examples. Marketing topics covered include brand metrics, value to consumer/firm and customer lifetime value (CLV).

On the data science side, while not going deep in the subjects, the book discusses data quality, privacy & ethics, customer segmentation, regression models and time series. In the “Big Data” category, the books covers web analytics, A/B tests, dynamic targeting and social network analysis.

Creating Value with Big Data Analytics also discusses storytelling concepts. Finally, Big Data capabilities within companies are covered. The very last chapter provides several use cases. If you work in applying data science to Maketing/Sales, you will find Creating Value with Big Data Analytics useful. It provides both an overview of the field and a starting point for further readings.


Data Analytics for Internal Audit

This is a guest post from Marcel Baumgartner, Data Analytics Expert at Nestlé S.A.


Large publicly listed companies not only have external auditors who check the books, but often also a large community of internal auditors. These collaborators provide the company with a sufficient level of assurance in terms of adherence to internal and external rules and guidelines. This covers financial… Continue reading...


The academic tip: What is Deep Learning?

This is a guest post from Jacques Zuber, Data Science Teacher at HEIG-VD.

The commonly called deep learning or hierarchical learning is now a popular trend in machine learning. Recently during the Swiss Analytics Meeting Prof. Dr. Sven F. Crone presented how we can use deep learning in the industry in a forecasting perspective (beer forecasting for manufacturing, lettuce forecasting in… Continue reading...


Interview of Jerome Berthier, Head of BI and Big Data at ELCA

Data Mining Research (DMR): Can you tell us who you are and how you came to the field of Data Science?

Jerome Berthier (JB): My name is Jerome Berthier, I am an engineer in Computer Science and I have an MBA in management. After 10 years working in different roles for an IT provider (developer, sales representative, managing director), I joined… Continue reading...


Will Data Scientists be Replaced by Machines?

Data Science automation is a hot topic recently, with several articles about it[1]. Most of them discuss the so-called “automation” tools[2]. Too often, editors claim that their tools can automate the Data Science process. This provides the feeling that combining these tools with a Big Data architecture can solve any business problems.

The misconception comes from the confusion between the whole Data Science process… Continue reading...


Data Science Book Review: Statistics Done Wrong

If you read this blog, you are very likely to be involved in any kind of data collection, manipulation or analysis. When not performed wisely, your analysis will lead you to incorrect conclusions. Alex Reinhart, in his book Statistics Done Wrong, has listed several concepts that are key when analysing data, such as statistical power, correlation/causation and publication bias.

Data Science Book Review: Superforecasting

Superforecasting – by Tetlock and Gartner – explains the huge study performed by Tetlock about the ability of people to predict future events (mainly geo-political). The closed questions (i.e. choose between yes/no) are far from real numbers you will predict in business forecasting. Tetlock discusses skills that have been identified as driving accurate forecasts. The point of the authors is that forecasting… Continue reading...