Predictive Analytics and Data Mining

Excellent article by David in terms of the utilization of data mining and predictive modeling concepts. I believe that expectations and corporate strategy are not properly aligned in this area. Data mining, predictive modeling, and business intelligence give an enterprise the opportunity to build a decision support system which is the marriage of the best technology and science have to offer. It does not replaces intuition, but augments it. The best way that I can describe this enterprise system is:
1. A robust back end to handle large amounts of diverse and complex data;
2. Creation of client, industry, and business problem variables that can assist in determining patterns in the data;
3. Utilization of multiple data mining or predictive modeling algorithms to classify the data; and
4. Utilization of statistical techniques to help forecast, partition, or determine areas with common patterns.

On the Advantages and Disadvantages of BI Search

Stephen has written and easy to read article as to the challenges for the next-generation BI. Let me add that text-mining technologies are currently improving constantly. We have seen it with Yahoo and Google and their association algorithms when you start typing in the search bar. As individual PC, laptops, and portable handled devices become embedded with intelligent agents we will start seeing the future unfolding right before our eyes. At the same time, you will see servers with the capacity to analyze the information from the intelligent agents. This is exciting!

Paper Kills: Transforming Health and Healthcare with Information Technology

If you have a role in healthcare strategy or data mining this book is a must read. Thought leaders like Dr. Brandon Savage at GE Healthcare. Once medical records are transformed into digital form, the vision of the future of healthcare in the US is data mining and healthcare analytics are at the core of this vision. Hence, what we are working on today will be one of the building blocks of this vision.

Donald Farmer on Data Mining

Donald and his team at Microsoft are first class professionals in data mining. If you have not visited Donald's blog I would recommend you to do it.

Donald's blog: http://www.beyeblogs.com/donaldfarmer/

Look at his data visualization music video link! http://www.youtube.com/watch?v=KHEIvF1U4PM

Technology: Is Data Mining Misguided?

When I read this article I see the clear confusion regarding the expectations of data mining technologies and how they should interact with statistical methodologies. The purpose of data mining should be to create a classification (think of a list of items going in a particular order 1, 2, 3,4, 5...). This calssification is based on a value that is express as a probability. Once you have a good measurement tool (this is waht data mining should do for you), then you apply statistical techniques (distribution, cluster, cause and effect analysis, correlation) to determine the areas that should "group" together (using relevant discrete and numerical variables, including but not limited to the data mining value obtained). Once you have determine the areas you want to study, then you use the data mining value (and other variables) and statistical methods to make your recommendations. Again, the process is: 1. variables, 2. data mining models, 3. determination of areas of classification, 4. statistical methods, and 5. recommendations.

The change management is to get users of data mining to understand that it is a process and that for it to work you need to invest resources (mostly time and technology).

Google, Microsoft and the glacial healthcare revolution

Good article on ZDNet that explains how Microsoft and Google are competing in their strategic initiatives in the healthcare industry. I believe that the main issue is how to effectively aggregate and find value in the vast amount of healthcare data. I think that the solution is going to be a combination of predictive modeling, data mining, powerful servers, and artificial intelligence tools that are connected through the Internet. I am honor to be a participant in this effort.

Korean stem cell fraud masked a true advance

The stem cell fraud case in Korea shows how scientific fraud can actually hold back progress. If Dr. Hwang would have been careful in his methodology and reporting he will still be considered reputable scientist. Lesson to be learn: be careful in your methodology and even more careful in your reporting of finding.