Bernanke: Banks must get better at foreseeing risk

I have been writing about this issue since March. I have proposed a comprehensive forecasting system that can be available to everybody within a financial company, and can be deploy in three months. For more details about the business analytics model that I propose see the following articles that I have published since March 2008.
1. Financial Services Business Analytics: Evidence-Based Model
2. Three-Dimensional Business Analytics: How Deep is the Ocean?
3. Business Case for Analytics: Explaining Cluster Analysis
4. Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system
5. Business Competency Model: Turning Around in a Quarter
6. Default Risk, Asset Pricing and Debt Control
7. DEVELOPING RICH INSIGHTS ON PUBLIC INTERNET FIRM ENTRY AND EXIT BASED ON SURVIVAL ANALYSIS AND DATA VISUALIZATION
8. People would rather live with a problem they cannot solve than accept a solution they cannot understand
9. The Financial Market Crisis and Risks for Latin America
10. Kaizen and Analytics: The Power of Each Employee to See Data

If you need assistance in implementing the financial services evidence-based model please contact me at Hewlett-Packard: alberto.roldan@hp.com

Technical Director and Project Manager Looking for a Job

I have a friend that has over 20 years experience as a technical director and project manager, as well as business development and sales. He has extensive knowledge in a variety of industries including: Industrial & Municipal Waste Water Treatment, Food & Beverage, Utilities Monitoring, Automotive and Process Industries. He is an expert consultant with expertise in automation. He has a lot of experience managing IT and technical staff. I have known Carlos since we were at the University of Michigan. If you would want to know more information please contact him directly at: carlosrod@mindspring.com

Feel free to use my name when you contact him.

The Future of Business Analytics: Microsoft vs. Google

The battle over dominance of the Internet will be fought in the realm of business analytics. Microsoft and Google understand this and are investing in making their products the flagship of analytics. The reason is that analytics, like the Internet, have the potential to unleash the creative power of billions of minds.

Microsoft is depending on the combination of Excel and Analysis Services. In the business world people are used to Excel as a way explore trends. Adding the capabilities of Analysis Services allow individuals to gain insight into analytics in an incrementing manner. This is Microsoft version of kaizen analytics. Donald Farmer explains Microsoft’s vision in a recent article, Microsoft Sets Sights on Data Mining Dominance.

Google Analytics is been offered as a free software to wean people out of their Excel dependency. Google understand that Microsoft’s monopoly in Excel needs to be counteracted with a free and robust alternative. In the next few months Google will move into different industries using Google Analytics to unleash the power of the mind of billions of people. This is Google version of kaizen analytics. The users of Google product are associating the word “analytics” with Google Analytics.

Interestingly, both Google and Microsoft products have the capacity to separate or partition the data into categories or clusters. The next incremental step would be to add an automated vector analysis to clarify the driving factors in the data. I expect the competition to be head-to-head in the healthcare and Life Sciences industry in the next 12 months.

Kaizen and Analytics: The Power of Each Employee to See Data

Do companies really know how to unleash the power of employees to be the leaders in their industry? Toyota has shown a different approach to innovation, kaizen or continuous improvement approach rather than a technology leap approach. Instead of great technological breakthroughs, this approach goes for involving the entire workforce in a continuous improvement process. Hence, most of the improvements are small and process oriented (like making shelves more easily to reach) but the involvement of the entire workforce rather than a selected few keeps a vibrant and innovative enterprise. The best measurement of how this work is that the Toyota workforce gives managements one hundred times more suggestions for improvement than other auto manufacturers.

Businesses that want to improve their analytics capabilities should follow the kaizen approach and make business analytics available throughout the entire organization. It seems that in some companies analytics is only within the purview of the few like statisticians, physicians, molecular engineers, and actuaries. The concept behind this thinking is that analytics technology is expensive and difficult to interpret. This premise is no longer applicable since in the last three years mathematical science and computer technology have advanced to such a degree that this technology is now inexpensive and available to interpretation to anyone within an organization.

This technology is the work of dedicated professionals and scientist that over many years have worked to make this possible. The issue now has become whether companies want to institute a continuous improvement process that includes enterprise analytics or whether they want to leave business analytics in the hands of the few.

If you want to know how to do kaizen analytics in your company let Hewlett-Packard help you. Our Technology Services Group had over $30 billion in revenues last year, or contact me at alberto.roldan@hp.com

Financial Services Business Analytics: Evidence-Based Model

This diagram is a representation of an universal evidence-based business analytics model for the financial services industry. This model takes into account data from customers, investments, deposits, and compliance issues. It is flexible so it can adapt to any situation. It is important to notice that predictive modeling and data mining is done in a separate server than the enterprise data warehouse (EDW), otherwise it would slow the EDW to a crawl. On the other hand, this model complements business intelligence reports. This models uses regression analysis, vector analysis, and clustering (partition) analysis. It takes into consideration that data is three-dimensional (3D). It is flexible enough to add association rules and time-series analysis. The front end incorporates depth-analysis in order to leverage human-computer interaction (HCI) at the enterprise-level (i.e., anybody within the enterprise should be able to use it. It should take three months to deploy this model. If you want assistance implementing this model please contact me at Hewlett-Packard: alberto.roldan@hp.com

Evidence-based Enterprise Business Analytics Model: Turning Around in a Quarter

Companies can implement in 90 days a fixed cost and evidence-based decision making systems that would allow them to become flexible in this economic climate. Science and technology has improved to such a degree in the last three years that companies can use off-the-shelf software to do business analytics which used to be reserved to a few highly skilled professionals within an organization.

The diagram above represents an example of the architecture for an enterprise business analytics module in the healthcare and biotechnology industries. This same business analytics module can be used in any industry or company. Small and medium sized businesses can implement this business analytics architecture using an analytics-as-a-service model. Large companies can implement the same model in-house.

Three-Dimensional Business Analytics: How Deep is the Ocean?

Business data, like our universe, is three-dimensional. Nevertheless, business analytics tends to be flat or two-dimensional like an Excel table or chart. The difference between a two-dimensional analysis and a three-dimensional analysis is depth. Depth perception allows an individual to accurately determine the distance to an object. It is a characteristic mostly seen in higher species.

In analytics, depth is referred to as dimensional analysis. Dimensional analysis is used in engineering, physics, and chemistry to understand the characteristics of multi-dimensional data. It is used to formulate hypotheses about the data, which are later tested in more detail. In business analytics we can create a three-dimensional variable that allows the end-user to “see the depth” of the data. This variable is called a three-dimensional vector analysis. This variable, when combined with cluster analysis and a visualization tool, answers the proverbial business question: how deep can I go into my data and see patterns in which sound business decisions can be made?

Business Case for Analytics: Explaining Cluster Analysis

The issue for business analytics is how to explain the return of investment by implementing enterprise analytics. One of the mathematical concepts involved in business analytics is cluster analysis. Cluster analysis is the utilization of a mathematical algorithm to group data that has similar characteristics.

Most datasets contain information that is three-dimensional (3D). For example, medical data include multiple values: diagnosis, medical procedures, prescription medicines, tests results, age, gender, and length of stay. The issue with most large datasets is how to analyze and visualize 3D data in a two-dimensional format. Cluster analysis allows the partition or segmentation of 3D data.

The best example that I have found to explain cluster analysis is brain imaging. In the example below, you can three images of the brain. The first image is of the top of the brain, the second of the side, and the third one is from behind. You would use a cluster analysis, to classify what areas of the brain is grey matter, white matter, or fluid. In our brain images, clustering analysis grouped the grey matter as red, the white matter as blue, and the brain fluid as green.







The advantage of cluster analysis is that it allows anyone within a company to make decisions based on a clearer picture of its 3D data.