Preparing Your Company For Recession

This article on CFO.com clearly indicates the need for company to act quick and smart during a recession. It is important to look at the Hackett Group paper and see that it identifies forecasting and analytics as one of the three main areas to look during this time that could increase the efficiency of operations. I specifically agree with the recommendation of using predictive modeling for revenue and costs.

Mobile Business Intelligence - the Next Big Step

Cognos has taken the lead in the area of mobile business intelligence. This is a huge step!

Duke Plots Course Beyond the Smart Grid

This is one of the most foward thinking business intelligence projects in the world. Duke Energy is taking the steps to create a smart power grid. The creation of the intelligence real-time applications behind this concept will revolutionize the world.

VP, Decision Support Systems

I was contacted for a position as a VP, Decision Support Systems, in New York with a prestigious financial institution. Although I am not interested some of you may be interested in this position. If you are contact Heinz Bartesh at heinz@pcninc.com

Market Forecasting and Modeling for the Power System of the Future

This paper addresses the utilization of predictive modeling and forecasting in the power supply industry. The issues herein were identified a couple of years ago, but the implementation is occurring at this time. The challenge of a forecasting system in power supply is the many "what if" scenarios that different models will need to consider. These "what if" scenarios need to take into consideration:
1. physical assets
2. contract prices
3. economic forecasting

A modeling of this size and complexity could require the utilization of a combination of most of the tools and methodologies in the current data mining and predictive modeling market, plus the development of some new tools.

Predictive Planning for Supply Chain Management

This paper shows one methodology of using predictive modeling in planning and scheduling decisions in supply chain management. It is important to remember that the variables will be different depending on the industry and client-specific requirements.

F.B.I. Data Mining Reached Beyond Initial Targets

It seems that the definition of "community of interest" association will be the cluster results from I2 Notebook. I have used this tool many times and the results are impressive. If you are using this tool you may consider using additional analysis (logistic regression and decision tree) to further refine your results.

Frequent Doesn’t Mean Loyal: Using Segmentation Marketing to Build Shopper Loyalty

This is a classic article regarding the theory of how to translate customer loyalty to develop a "profitable differenciation".

Data Mining Analysis and Modeling for Marketing Based on Attributes of Customer Relationship

This article on data mining in CRM for the retail industry shows the utilization of cluster analysis, association rules, and linear regression in determining Attributes of Customer Relationship ACR

SUPERVISORY [BANK] RISK ASSESSMENT AND EARLY WARNING SYSTEMS

This 2000 paper from the Bank for Internaltional Settlements gives a good overall picture of the statistical modeles used to analyze and determine risk assessment in the banking industry. The mortage industry and associated lenders and market leaders should consider implementing these early warning system models to prevent the current mortage crisis to repeat itself in other areas.

Data Mining Applications in Higher Education

Good article about data mining applications in higher education.

Data Mining Technologies and Decision Support Systems for Business and Scientific Applications

The issue is whether or not you have the information or data anymore since a lot of companies and organizations have large amounts of data. "The challenge is to be able to utilize the available information, to gain a better understanding of the past, and predict or influence the future through better decision-making."

Integrating Customer Value Considerations into Predictive Modeling

This is a good article about how to measure "sucess" in applied predictive modeling. The example is in the telecommunications industry, but the "valuable customer" aproach can be used in any industry.

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.