Competitive Intelligence (CI) is the process of collecting, aggregating and analyzing external data for the benefit of a company. A good introduction to the subject can be found in Competitive Intelligence Advantage: How to Minimize Risk, Avoid Surprises, and Grow Your Business in a Changing World. I particularly appreciate the use cases showing the distinction between competitive intelligence and competitor analysis. The below image provides a good view of CI.
When it comes to making sense of a large amount of external data for decision making, data science is in the game. In overall, one can think of topics such as text mining, social network analysis and sentiment detection. Examples of applications in patent mining are given in the following article: Data Mining tools for technology and competitive intelligence.
The book Mining for Strategic Competitive Intelligence provides several potential applications of data mining in competitive intelligence. If you have any other references to share, please post a comment.
We recently started a Meetup group for the Swiss Association for Analytics: http://www.meetup.com/swiss-analytics
Feel free to join our Meetup to be informed about our free events in Switzerland. Recent event topics included analytics for CRM, fraud detection and text analytics.
We are always looking for speakers and sponsors, so feel free to contact us at firstname.lastname@example.org
Here is a guest post from Jake Goodman, freelance writer for online technology magazines.
With all the issues that have been surrounding global energy, a huge amount of pressure has been placed on the oil and gas industry to find new fields to explore as well as fully extract fossil fuels from wells. Regulations on production are constantly changing because of the rapid advances in the technology for extraction… Continue reading... | 3 Comments
Our next Swiss Analytics Event, June 18th 6pm in Lausanne, is about text mining. Here is the program:
Tailor-made vs. off-the-shelf – A simple method for personalization in information retrieval (Melanie Imhof)
Abstract: The ever-increasing amount of unstructured data makes it not only difficult to find relevant information but also to formulate specific, non-ambiguous queries. Information retrieval systems generally apply the “one size fits all” paradigm, where… Continue reading... | 8 Comments
There are very few books available discussing general aspects of ensemble methods. One of them is Ensemble methods from Seni, Elder and Grossmann. It provides a high level overview of ensemble learning. However, the book contains a lot of equations which make it hard to read from beginning until the end. You will rather pick a few sections and read them independently.
Richard Boire recently published Data Mining for Managers – How to use data (big and small) to solve business challenges. The particularity of this book is to bring a new (non-US) view of the field. What I mean by “new” is that examples and case studies are from Canada and thus not appearing in other data mining books.
Data Mining Research (DMR): Could you introduce yourself to the readers of dataminingblog.com?