On the Visualization of Mathematics (Analytics)

While reading articles on analytics and mobile devices, it occurred to me that I need to be clearer on explaining my vision on the visualization of mathematics. I believe that the visualization of mathematics, by using analytics techniques, will transform the way we do business the same way the invention of the printing press transformed the world. Mathematics is the universal language, and its visualization is a form of expression. This is where business, science, and technology meet.

Before the printing press was invented, knowledge was in the purview of a few individuals who knew how to read. Once the press was invented, knowledge and communication became available to all. With the advent of word processing software, more people could not only read but write within their own sphere of influence. Of course, the ability to read and write does not mean that everyone will be a Shakespeare, Neruda, or Solzhenitsyn. The same is true with the visualization of mathematics. The analytics power users will not disappear; if anything, they will become more visible within an organization.

Sometimes we tend to equate Excel reports with analytics. This is a mistake. Reports have a limited capacity to represent the full spectrum of analytics. The alphabet is the fundamental of writing, but only represents a very limited spectrum of the capacity of expression of written communication. A poem by Robert Frost, a novel by Garcia Marquez or J.K. Rowling can create thousands of similar but slightly different mental pictures to different readers.

It has been said that a picture is worth a thousand words. We are living in a world of large datasets. Companies, governments, and organizations are challenged everyday in storing large datasets that contain structured and unstructured data. We have mobile devices that have enlarged real-time communications in ways that 20 years ago were unimaginable. Science has progressed to bring us the capacity to store and analyze large volumes of data. We can combine the advances of business, science, and technology with the capacity of the human brain by visualizing analytics.

The human brain is made to perceive, understand and analyze three-dimensional geometrical configurations. See, The Attention System of the Human Brain at: http://atomai.blogspot.com/2008/05/attention-system-of-human-brain.html. Mathematics is the foundation of analytics, as the alphabet is the basis for written communication. Analytics can be turned into three-dimensional geometrical configurations that will change the way we make decisions by giving us an evidence-supported decision support system within our own sphere of influence. Analytics is not a report. The power of the visualization of analytics will allow each person to “see” the same analysis but will allow each person to have a slightly different interpretation of how to apply that knowledge in her own life. The visualization of analytics will transform the world by having the power of mathematics at the fingertips of everyone.

Delivering Software as a Service

This McKinsey Quarterly article is right on point as to this emerging business model. See, http://atomai.blogspot.com/2008/03/software-as-service-overview.html. The three areas that software vendors must pay attention in this area are:

1. Adjust the Revenue Model – Although initial sales costs are higher, a SaaS model target small and medium size businesses which are the driving force of revenues and profits during an economic recession. See, Tools During Economic Recession: Forecasting & Business Analytics; and Preparing Your Company For Recession.


2. Build a Platform – Do not take too long to recognize SaaS as a profit center otherwise you could make the Microsoft mistake of not recognizing the Internet as a powerful media and advertisement generating revenue center. The catch up business model has proven to be an inefficient way to growth a business.


3. Improve Internal Capabilities – “The biggest capability gap for software companies embracing the new model is in the operational and customer service skills necessary to deliver software online. The operational challenge is to host the software rather than shrink-wrap and ship it. Companies will have to develop capabilities to handle massive data center operations, systems and network monitoring, and billing.”Companies with a mature internet presence like Google, Microsoft, and Yahoo, have an advantage over smaller software vendors that do not the internal capabilities. On the other hand, the major companies must improve their decision making by making adjustments to those capabilities.

Business analytics allows companies to improve their internal capabilities efficiently. See, Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system. Adjusting the revenue model is going to require executive management to allocate investment in a SaaS model. See, Enterprise Analytics: A Business Decision. The role of IT is to build an Internet-driven platform that supports real-time analytics. See, HP Delivers Real-time Business Information with Enhanced Neoview Capabilities.

A SaaS model is fundamentally based on the principle that every person in an enterprise can contribute to improve revenues and profits by making small incremental changes in their areas of responsibilities. See, Kaizen and Analytics: The Power of Each Employee to See Data. The next frontier of SaaS will be business analytics using mobile devices. See, Mobile Devices and Business Analytics.

Unleashing the Power of the Mind™

Mobile Devices and Business Analytics

1. The next frontier of business analytics is the incorporation of mobile devices into an enterprise decision support system. The key is to have the following functionalities:
   (a) It must give a visual representation of the entire dataset; and (b) the screens in mobile devices are small, hence the visualization of large datasets requires the ability to see the data three dimensionally. There are two potential solutions to approach this problem:

1. Excel type of flat bar charts – Use clustering analysis to separate the clusters with similar characteristics, and include as a variable in the cluster a vector that that will clarify the driving factors. Microsoft, SAS, and Business Objects already have these functionalities (i.e., clustering and create a vector using a predict probability). The issue is whether they can have a universal interface with mobile devices for these charts. See, INSTINCT GAINS INTELLIGENCE; Three-Dimensional Business Analytics: How Deep is the Ocean?; and Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system

2. Three-dimension graphical representation – There are companies and academic institutions that are gravitating toward a 3D representation of all the data. I believe that in the future we will integrate this to the flat charts most currently in used nowadays. See as an example, http://thedmblog.wordpress.com/2008/06/04/visualizing-large-graphs/; and Real Time Scalable Visual Analysis on Mobile Devices

Another essential functionality is the ability to drill-down capability from a graph to a report – Some companies already has this functionality as part of the mobile device. See as an example, http://www.webalo.com/index.html

Unleashing the Power of the Mind™

Business Objects aims to predict the future with business intelligence

This is a powerful move for Business Objects and SAP. Business Objects have had for years the click-and-drag reporting capabilities, the functionality of standard deviation, and with the predictive analytics capabilities this will become a powerful force in business analytics. I have always been impressed by the products of these companies. The combination of these two companies, their technical functionalities and worldwide market reach put us closer to unleash the power of the mind by putting business analytics at the fingertip of every employee in a company.

Tools During Economic Recession: Forecasting & Business Analytics

The economic recession in the U.S., the housing and credit crisis, and the high oil prices are bringing business analytics to the forefront of companies. See Preparing your Company for a Recession at http://atomai.blogspot.com/2008/02/preparing-your-company-for-recession.html. In good economic times business analytics is important. In challenging economic times it may be the difference between success and surviving.

These are the best of times and the worst of time. Business, technology, and science have leap hundreds of years in the last couple of years. Risk and reward are closely intertwined with the vision of our business leaders and their ability to be at the forefront of change. See Evidence-based Enterprise Business Analytics Model: Turning Around in a Quarter. Companies must use all the resources available to become better at forecasting risk. See Bernanke: Banks must get better at foreseeing risk....

Those of us in business analytics have the ability and knowledge to incorporate forecasting and data mining techniques that separates the clusters of data and clarify the driving factors. See Business Case for Analytics: Explaining Cluster Analysis; and Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system. Those of us in business analytics, data mining, and forecasting must do better in explaining the business value to our companies. See Enterprise Analytics: A Business Decision.

We must be specific and present a complete strategy to executive management whether is a workplace reorganization of restructuring, a marketing plan, an investment strategy, or an operational strategy. We cannot just give "the numbers" and not look at the big picture. See Best-laid plans . . .at http://www.app.com/apps/pbcs.dll/article?AID=/20080606/SPORTS06/806060350/1002/SPORTS

We have the ability and responsibility to present solutions. We can make a difference. The future is now. See Intersection of Business, Science, and Technology: Business Analytics.

Unleashing the Power of the Mind™

Real-Time Business Analytics

A new discovery about the human brain gives us a good insight in how to improve real-time business analytics and predictive modeling. The discovery is that the brain compensates the neural lag that everyone experiences by giving us a glimpse of events one-tenth of a second before they occur. See Key to All Optical Illusions Discovered. The brain predicts what is going to occur based on available data and experience. See The Attention System of the Human Brain. Imagine the implication for the financial services industry. Banks, investment banks, and brokerage houses could profit or lose billions during stocks and commodities trading, if they had a one-tenth advantage over their competitors. See Financial Services Business Analytics: Evidence-Based Model. Envision a system that reminds customers about an item that they may have forgotten before leaving the store in the retail industry. Also, picture a system that reminds network administrators to check a specific system, server, or pathway. Depict a system that reminds physicians to check for the symptoms of a specific disease, recommend a particular test, or reminds a surgeon to check a particular area of the body.

In an interdependent and continuously changing global economy, companies need to accurately forecast and anticipate trends. Companies must use the latest advances in science, technology, and business to gain leverage and improve profitability. See Intersection of Business, Science, and Technology: Business Analytics; and Business Analytics Technique: Borrowing From Medical Imaging at http://atomai.blogspot.com/2008/05/new-business-analytics-technique.html.

The advances in technology and sciences allow these tools to be implemented rapidly and in a cost efficient manner. See High Performance Computing (HPC) is Changing the World; HP Delivers Real-time Business Information with Enhanced Neoview Capabilities; and Business Competency Model: Turning Around in a Quarter.

Business Analytics: Unleashing the Power of the Mind™

Analytic Culture – Does It Matter?

This is a good article that explains a difficult issue in change management in the area of business intelligence and change management: the importance of understanding the culture within a company if you want to achieve change. Dave Wells has done an excellent job at explaining how to identify different cultures. The key for change is to adapt our product and services to the existing culture. This is one reason that Microsoft BI stack, or the iGoogle predictive analytics tool could become the products of the future. See, The New ADAPA iGoogle gadget: Google Predictive Analytics; Microsoft SQL 2005 Analysis Services: Ten Best Practices© at http://atomai.blogspot.com/2007/03/data-mining-and-microsoft-sql-2005_23.html. These products appeal to the broader cultures: people who already use Excel, or people who want to limit their cost by using Software-as-a-Service. See, Software as a Service overview . It is important to remember that it is not just the algorithm but the amount of data that makes business analytics a reality today. See, More data usually beats better algorithms . SAS and KXEN have powerful algorithms and business models, but I think that those products do not take into account the culture within a company as well Google and Microsoft.

High Performance Computing (HPC) is Changing the World

This short video is a powerful tool to explain how science and technology are interacting for advacements beyond our imagination, and the concept of HCP. "Powerful Beyond Imagination". The International Conference on High Performance Computing, Networks, Storage, and Analysis will take place on November 15-21, 2008, in Austin, Texas. http://sc08.supercomputing.org/ This is the 20 years celebration of the super computing conference!

These two events may be fun:

• a Music Room, conveniently located in the convention center lobby, where conference attendees can sit and listen to other attendees play during scheduled times throughout the conference. Basic orchestral and woodwind instruments will be available, including a Baby Grand piano, but you are encouraged to bring your own. (A reservation must be made in order to secure a time for you to play in the music room. More about signing up will be provided later).
• Digital Vibrations: Is it Real?, where attendees will visit a kiosk, listen to a series of songs, and identify ones they think are computer generated versus those they think are produced with actual instruments. A prize will be awarded to the person who correctly identifies all songs.

HP Delivers Real-time Business Information with Enhanced Neoview Capabilities

These are the type of advances in technology that makes business analytics a reality today, by imitating the capabilities of the human brain. The enhanced Neoview capabilities for business intelligence are:

1. It efficiently processes large and small transactions simultaneously
2. Forecast traffic to avoid delays and improve performance (i.e., it avoids those instances when you see a person but forget their name, or
3. Make sure that real-time information is up-to-date (i.e., avoid those moments when you go to a room to do something and ask yourself, "why am I here?")
   

Intersection of Business, Science, and Technology: Business Analytics

We are living in the future. Some time ago business, science, and technology were three distinct and separate disciplines. The advancements in these three disciplines during the last five years have created a quantum leap in how each directly affects the others. Businesses, large and small, make global transactions on a daily basis. Technology allows people from around the world to communicate internationally and storage large volumes of digital data. Science allows accurate predictions using large databases around the world.

What is business analytics? Business analytics is the intersection of business, science, and technology that allow us to unleash the power of the brain. Historically, mankind has attempted to replicate the decision-making prowess of the brain in order to progress. The financial services industry has transformed from local lenders and borrowers, to complex international financial transactions involving international and local banks, investment bankers and insurance companies. The healthcare industry has worldwide specialty surgeries, and the pharmaceutical industry cooperative bio-molecular research and genome research, facilities. The chemical and energy industries have found new ways to find and convert raw materials. The media industry has gone from the books to the internet.

The common characteristics of these advances are the way that business, technology, and science have cooperatively intersected to promote common objectives. The next frontier of business analytics is to tap into the collective power of the individual and unleash the power of the human brain. Companies like GE, Microsoft, Hewlett-Packard, IBM, SAS Institute, Exxon, and Google are among the thousand of companies around the world that are working to unleash the power of the mind by intersecting business, technology, and science. Business analytics is the future, and the future is now.

Business Analytics Technique: Borrowing From Medical Imaging

A study published in the journal Nature shows a scanning technique developed by GE Healthcare to detect cancer that can be adapted to business analytics in any industry. This technique involves the concept of separating the clusters in the data and clarifying the driving factors. See, Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system.

The technique involves:
1. Cluster analysis to separate the data. See, Business Case for Analytics: Explaining Cluster Analysis.
2. Vector analysis using regression to clarify the driving factors. See, Three-Dimensional Business Analytics: How Deep is the Ocean?
3. A Human-Interaction Computer Visualization tool. See, Human Factors In Visualization Research; Visualization Projects for Data Mining; Information Visualization and Visual Data Mining.

Business analytics can adapt some of the state-of-the-art solutions from medical imaging techniques and use them for business forecasting, as well as to determine whether strategies are working well at an earlier stage. This would allow businesses to be flexible and adapt to changing an ever changing economic environment. See, Preparing Your Company For Recession; and Evidence-based Enterprise Business Analytics Model: Turning Around in a Quarter.

I envision in the future that business analytics techniques will be at the same level than medical imaging. See, NIH-NSF Visualization Research Challenges Report; and Kaizen and Analytics: The Power of Each Employee to See Data.

Unleashing the Power of the Mind!

iGoogle Predictive Analytics Gadget: Feedback

This is the feedback that I have gotten so far on this new predictive analytics tool

E. Harris - “I think the gadget is a boost to marketing campaign managers, small business website owners and SEO professionals. It gives an immediate ability to test and analyze content and campaigns real-time. I think there is a high degree of value here as the wait time for results will be Google dependent. There is also independence to the subscriber, if you will, to become less reliant on the high costs associated with statistic and ranking providers. We may also see an increase in the length of time remains on a website as it is evaluated predictively for value.”

D. Padmanabhan - “I tried a demo NN model and found it interesting. It will be interesting if the models can be shared among different users. I would also like to see additional outputs such as prediction errors or maybe even error charts.”

You can see my comments and Mike Zeller (CEO of Zementis that developed this iGoogle tool) response at: The New ADAPA iGoogle gadget: Google Predictive Analytics

The competition between Microsoft and Google in the area of analytics is unfolding. See, The Future of Business Analytics: Microsoft vs. Google. Microsoft SQL 2005 Server Analysis Services and the Excel 2007 add-in offer a proprietary model with the advantages that people are used to Microsoft products. The Google gadget for predictive analytics offers a new model of Software-as-a-Service, which could decrease the cost of predictive modeling in a company. See, Software as a Service overview; and Delivering Software as a Service.

The Attention System of the Human Brain

It is important to understand how the human brain functions in the area attention, at the cognitive and neuronal levels, so we can attempt to replicate these functions in any decision support and business analytics system. The concepts in this article cover the fundamentals that every person in the analytics field should understand. Among the interesting areas for practical application:

1. Data processing and analytics are separate functions that interact – A practical application is that we should not be processing and analyzing data in the same server.
   Orienting – Practical applications include: visualization tools, human-computer interaction (HCI), and the need for size, color and depth (right and left hemispheres) representation of the data. See, Business Case for Analytics: Explaining Cluster Analysis; Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system ; and Three-Dimensional Business Analytics: How Deep is the Ocean?
2. Pattern Recognition – Practical applications include: utilization of colors in the analytics visualization tools; utilization of forecasting vectors to determine complex patterns; and data does not need to be 100% clean since the brain will fill the missing parts.
3. Detection – Practical applications include: the ability to visually represent data in its totality, while having the functionality to dynamically select specific areas of the data for further study; interactivity for further study depends n alertness and this depends on robust and fast system of analytics (i.e., the fastest the report creation the higher our alertness and detection); and speed in the system is directly correlated to lower quality of data and produces a higher error rate.

Knowing the anatomical reasons for decision support and analytics in brain function gives a higher importance to create decision support systems (which include predictive modeling) and make it available to everyone within a company or organization. See, Kaizen and Analytics: The Power of Each Employee to See Data; and Business Analytics: Unleashing the Power from Within .

The New ADAPA iGoogle gadget: Google Predictive Analytics

After talking with Mike Zeller the CEO of Zementis I decided to re post this blog with Mike comments. In the interest of full-disclosure, Mike is a professional colleague and I always enjoy our conversations. In a nutshell, Mike wants to make the point that:
1. This product is for those who are knowledgeable in the field of analytics.
2. Import/Export functionality using "browse" capability is part of the product.
3. Import/Export functionality for Excel files is already part of this product.

The fact that Google is moving toward Software-as-a-Service in analytics is impressive and the correct direction. Also, Mike points out that this is the first step in a journey to put analytics capabilities in the hands of everyone. The future is here and it is exciting! Enjoy the post, and by all means give this product a try and let me know your opinion (I do not post anonymous opinions).

You can install the engine as an iGoogle gadget by following the link below:http://www.google.com/ig/adde?hl=en&moduleurl=hosting.gmodules.com/ig/gadgets/file/115640297026242314759/adapawidget.xml

I was looking at the new ADAPA iGoogle gadget and this is a tool that would mostly be used by people that have technical knowledge about predictive analytics. There are some basic improvements that must be made so that analytics is available to "everyone" (see my post Business Analytics: Unleashing the Power from Within).

Mike's comments: "Yes, you are correct that ADAPA is a tool for people who are somewhat familiar with predictive analytics. The intention of the ADAPA iGoogle gadget is not to teach "everyone" about predictive analytics, but to deliver a tool that makes it easy to deploy and use such models.

ADAPA bridges the gap between the scientists developing the models, the IT folks who have to integrate & deploy the models, and the business who ultimately want to use the results of the models to drive the business process.

Predictive analytics uses mathematical models that assume a certain technical knowledge and for many users that will always remain a black box. Only in the context of a business application, most users will understand the outcome of a particular model, e.g., that a high risk score means that the related credit application is bad for business.

Following your vision, we should strive towards building many of these iGoogle Gadgets in a form that focuses on the business user and "hides" the technical details.

Our ADAPA iGoogle Gadget was just an example to demonstrate that is actually possible to deliver predictive analytics as service and make it available via simple iGoogle gadgets. We wanted to empower the scientists and engineers to easily build business applications that leverage the potential of predictive analytics."

My wish list for this product:

Keep it simple!

1. The Demo is talking about PMML standards, etc. Most users do not care about the technical aspects and standards, they just want to dig in and make it happen. Do not get a technical person to explain how it works, get a sales person to explain how it works. We usually complaint that sales people over simplify technical issues, but this is a case that "sometimes less is better than more". Go back to Google home page and compare it with Yahoo and MSN to understand this concept.
   Mike's comments: "In order to understand the basics about predictive analytics, it is critical to know that there is a standard to represent such models. If you want to represent a web page, you need to know that there is something like HTML. If you want to represent a predictive model, you need to know that the common language is PMML and that there is such a file format.
   Unfortunately, we still have much work to do in order to evangelize the PMML standard. Many people don't know about this standard which allows models to be shared between various software tools from different vendors."
2. The converter should be behind the scenes. Again, just make it happen and do not bore us with the details.
   Mike's comments: "We made a conscious decision to keep the PMML converter a separate product, so users can choose to only convert various PMML versions, but not be locked into using our ADAPA deployment platform. It is our support to the PMML community and will remain a free service."
3. Explanation for models should be simple. For example: "Linear regression takes numerical values only. Numerical means that if you can add those values and it means something. You can represent months in numbers but if you add those numbers it does not mean anything. Numerical representation of months are not a numerical value."
   Mike's comments: "'Explanation' are supposed to explain what the model does, this is an element of the PMML standard (description). Maybe it could be more descriptive, but it is not really the place to explain how a linear regression works."
4. I think that the future of analytics is to allow everyone to see the clusters in the data and clarify the driving factors. See, Kaizen and Analytics: The Power of Each Employee to See Data

Google must overcome the basic rule: People would rather live with a problem they cannot solve than accept a solution they cannot understand . I am sure that the science behind the models are good. On the other hand, the science behind this type of algorithms have been out in the market for a long time. The differentiators are: that is free and that it could be used by a company as a Software-as-a-Service (SaaS). See, Software as a Service overview.
Mike's comments: "We are on the same page here. The goal of this application is to make it easy to deploy, integrate, and execute predictive models.
It is the first step towards a more general adoption of predictive analytics. Now we can work towards the same simplicity in business applications that sit on top of the ADAPA deployment platform and draw their decisions from the predictive power of advanced scientific algorithms."

Predictive Analytics Engine available as an iGoogle gadget

This was a posting in a group that I have at Facebook (Analytics, Data Mining, Predictive Modeling, Artificial Intelligence). As predicted on May 12, 2008, the next battleground between Google and Microsoft is going to be in the realm of business analytics. The science in this Google product is good, as is the science behind Microsoft Analysis Services too.

In my opinion Google is correct in assuming that the future business analytics is in Software-as-a-Service (SaaS). I specifically addressed this topic on blog postings of March 10 and March 26

"If you are interested in executing data mining models such as neural networks, SVMs, logistic and linear regression, ... from your desktop, check out the new ADAPA iGoogle gadget. It allows for the uploading of data mining models expressed in PMML (Predictive Modeling Markup Language) for scoring. Model execution is done through Amazon EC2 and is free. A paid version which will allow for real-time scoring via web-services (as a SaaS - Software as a Service) is going to be launched in May. You can install the engine as an iGoogle gadget by following the link below:http://www.google.com/ig/adde?hl=en&moduleurl=hosting.gmodules.com/ig/gadgets/file/115640297026242314759/adapawidget.xmlAlso available is a converter gadget to convert old PMML to version 3.2:http://www.google.com/ig/adde?hl=en&moduleurl=hosting.gmodules.com/ig/gadgets/file/115640297026242314759/converterwidget.xmlDecision trees is next in the range of mining models that ADAPA supports. Have fun!Alex"

Business Analytics: Unleashing the Power from Within

Every person has the inherent ability to interpret the world around them in a physical and in an intuitive manner. This ability of the human mind to deduce facts and patterns based on both external and internal conditions is one of the main characteristics that separate us from other species. Business analytics have evolved to give every member of a company the ability to interpret known metrics like revenue and income, as well as internal evidence-based multidimensional analysis (like the predict probability for an event to occur) which clarifies the drivers in the data.

In my experience, executives understand the concept that people are the most important asset within an organization. Also, they understand the importance of business analytics to maintain a competitive advantage. Business analytics is an enterprise continuous improvement system that allows every person within an organization to detect patterns in the data that allow for incremental improvements on a regular basis. Companies are like large ships, they are difficult to turn around. The advantage of business analytics is that it allows for regular and incremental changes in a business that allows for change to take place.

Allowing every employee the ability to suggest incremental changes, based on business analytics, which will improve their own performance, is kaizen analytics. Kaizen analytics assumes that employees are the most important asset in an organization. Therefore, analytics and improvement should permeate through out the entire company.

In the last few years some of the best minds in software companies have developed the technology and science to put business analytics at the fingertip of every employee: from the entry-level to the executive suite. Companies have the capacity to unleash the power from within each employee by deploying business analytics. The implementation of business analytics allows companies to separate the cluster of data and clarify the driving factors.

Some companies already have the internal capacity to deploy business analytics, but lack the skills to implement a business analytics system. For example, companies that already have Microsoft SQL 2005 Server and Excel 2007 are in a position to quickly turn around a business analytics system. If you want to unleash the power from within your company contact me at Hewlett-Packard: alberto.roldan@hp.com

BI Enterprise Conceptual Architecture Solutions in the Financial Services Industry and Healthcare Industry: Microsoft SQL 2005 Server and Excel 2007

If you are a Microsoft shop and want to implement a business intelligent (BI) solution using SQL 2005 Server and Excel 2007, these are the high-level conceptual architectures for the financial services and healthcare industries. If you need any assistance in implementing these models please contact me at Hewlett-Packard: alberto.roldan@hp.com

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

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.

Explaining the Value of Business Analytics: Clarifying driving factors in a decision support system

There are many ways to use data mining and predictive modeling to find patterns in large volume of data and transform them into actionable information. I have found that a combination of data mining and predictive modeling techniques are necessary to separate the clusters of data and clarify the driving factors. For the purposes of this posting, a vector is a multidimensional variable that uses mathematics to predict the probability of an event. When a vector (or predictive modeling) is used in conjunction with a clustering technique (separate data into groups of similar characteristics) the result is a decision support system that separate the clusters in the data and clarifies the driving factor.

The best visualization to explain this concept is in the area of graphics. The graphic to the left illustrates how a vector can clarify an image using vector and cluster analysis. This methodology can be used in any industry. The predictive modeling science and computer technology allow companies to build this capability efficeintly throughout an enterprise using products like Microsoft SQL 2005 Server Analysis Services, or SAS Enterprise Miner.

Intelligent CRM Requires Business Intelligence

"...enterprises use BI applications that can highlight information that will lead to meaningful change in company performance. Rather than just running reports with reams of information, Herschel recommended using reports with "traffic lights" or other signals that alert the reader to the handful or so of the most meaningful data points." This is suggesting to use a methodology that separates the data (cluster analysis) and clarify the driving factors within those clusters (predictive modeling or scoring the predict probability for an even to occur).

Microsoft Introduces Tool for Avoiding Traffic Jams

Microsoft strategy of integrating data mining, predictive modeling, and artificial intelligence in all its product is what separates them from their competition. The application of these technologies to their product offering would change the landscape of how we conduct business for the next 50 years. Imagine integrating the Clearflow AI with the data mining algorithms of SQL 2005 Server Analysis Services, and adding a powerful visualization tool to a product like Excel...wow!

Information on Some Trends in the Disease Management Industry

A very good summary of the Blues survey pertaining predictive modeling in the Blues Plans.

MARKET SEGMENTATION: A Neural Network Application

This paper is about the utilization of business analytics in the tourism industry. The methodology could be applicable to places like Dubai, or for the cruise industry.

On optimizing the selection of business transformation projects

This is an IBM paper that presents a good methodology for business transformation using predictive modeling and business analytics. This approach could be used in supply-chain cases. From a practical approach, if you have a Microsoft shop, you can use Analysis Services (2005 or 2008) Logistic Regression or Linear Regression and use the predict probability feature to get the multidimensional vector. At the front end, the cluster analysis function of Excel 2007 will help separate the cluster. By adding the vector to the cluster variables this algorithm will give you an interactive or dynamic visualization that will separate the clusters in the data, and clarify the driving factors.

How to compete on analytics: apply it

This SAS.com blog is excellent and it reflects the changes that are happening in the market place: companies are incorporating business analytics as part of their strategic objectives. The SAS Institute is far ahead in their analytics products than their competitors. They have business modules for almost any industry and great dynamic visualization tool. They are the pioneers and leaders in this area.

Business Competency Model: Turning Around in a Quarter

Companies need an organization that allows faster responses to market needs, and differentiated products or services to survive in changing economic times. In the area of business analytics the issue is to leverage the knowledge within an organization that allows faster responses to market needs, and differentiated products of services. Business analytics allows everyone within an organization to visualized potential trends internally and externally. The premise is that the greater number of people within an organization that have access to the entire data the faster changes can take place. Since the amount of data is so large there must be a visualization tool that has the capacity to show the entire dataset, while simultaneously separating the clusters of data (i.e., what is in there?) and clarifying the driving factors (i.e., why is in there?).

Companies that outperform the market in tough times have shown to change their organizational model to reflect the times. In 2001 I wrote a paper which states that the changes in an organization start at the executive management committee level and it filters down to the entire organization. Therefore, companies which want to use business analytics to achieve their strategic objectives must have a business analytics committee within the executive management structure. I like to think about this area in terms of parents setting the examples within their own families: an example of how you do something has a greater impact than speaking about it.

Business Analytics – Getting the Point

This article explains the evolution from business intelligence to business analytics: from getting the data to using the data.

Default Risk, Asset Pricing and Debt Control

In today's world markets this is an important paper. The premise is that you need to analyze a company's credit risk when you evaluate a company's assests. This is relevant because insurance companies, savings and loans, banks, mutual funds, pension funds and other companies have invested in what is considered high-risk investments like mortagage-backed securities. We might find that this credit-risk issue may touch other organizations like insurance companies and pension funds.

DEVELOPING RICH INSIGHTS ON PUBLIC INTERNET FIRM ENTRY AND EXIT BASED ON SURVIVAL ANALYSIS AND DATA VISUALIZATION

The methodology of this paper can be used to determine the "health" of public companies in the area of credit risk management. These visualizations can be done in Excel. I would add a multidimensional vector (or variable) in order to clarify the driving factors.

Seeing is believing: Designing visualizations for managing risk and compliance

This is relevant paper by the IBM Labs in visualization analytics. Although the paper orientation is toward risk management compliance in the Sarbarnes-Oxley area, its general principles have applicability over all analytics. For example:
1. A visualization should be thought of as a user interface to a control task, not as a report or a report component.
2. The patterns that are important for users to manage are at the level of the
controls
3. Visualizations must be able to evolve as the process for an individual or a group evolves or as the overall compliance process evolves

Computer-aided Detection of Lung Cancer from Computed Tomography Images

Good technique to separate the clusters and clarify the driving factors.

Sr .Net Architect

I usually do not do this, but there is a friend that is looking for a Sr .Net Architect in the Nashville, TN area. It can pay up to $120/hr.

Conference or Seminar on Data Mining and Visualization Tools

A member of the Business Analytics Group wants to know if anyone knows a conference or seminar that will cover the issue of data mining and visualization tools. If anyone has any information please let me know.
Alberto

More data usually beats better algorithms

This article pinpoint something that has been true for a long time: more data usually beats better algorithms. Therefore, assuming that the data mining algorithmns are not the issue (assuming good science behind them, which I have found in all the major software vendors), the issue then becomes the quality of the interactive visualization tool that allows end-users to make better decisions. Fed Chairman Bernanke, when at Princeton, published a paper that is complimentary to this issue.

Business Analytics is Evolving Says SAS

Good article explaining the definition and value of business analytics.

Schlegel on Search, Analytics and Visualization

Gartner's Schlegel on next BI industry consolidation trend: predictive analytics and visualization tools.

People would rather live with a problem they cannot solve than accept a solution they cannot understand

This quote by Robert Woolsey is the main business analytics issue. The science and the technology nows allows predictive analytics and data mining to be available to every business regardless of size and complexity of the problem. The issue is how to explain the solution to organizations. I believe that an interactive visualization tool, that mirrors the goals and strategy of an organization, is the key to make businesses understand and embrace the solution. A picture speaks a thousand words!

Predictive Analysis with SQL Server 2008

This is a good article. In the interest of full-disclosure, I am familiar with Donald Farmer, Jamie MacLennan, Kamal Hathi and some of the individuals of the SQL data mining team within Microsoft. Also, I believe in the underlaying strategic principle that making predictive analytics available to all users within an enterprise is the keystone of turning data into actionable information for better decision-making. I know that the underlying predictive algorithms are based in good science and technology (as well as the products of SAS, SPSS, Business Objects, and many other vendors).

Two issues that I would like to explore with this product:
1. The interactive visualization of Analysis Services with Excel 2007. I believe that this is the key for data mining for the masses; and
2. The creation of industry-specific modules that allows for an easier designing of a multidimensional vector that is industry specific.

Family Study Associates Pesticide Use With Parkinson's Risk

This article shows the type of pattern that made me go into data mining. My father was a pure scientist (chemistry and mathematics) that dealt with pesticides in his research. Th entire laboratory staff (scientists and lab workers) all died of Parkinson's disease. The only thing that these people had in common was working with pesticides. These were very careful people that took extraordinary precautions in dealings with pesticides because they were aware of the harmful effects (even at the cellular level over a long period of time) of pesticides. My goal is to create a data mining visualization and predictive tool that can be used for as many people as possible to detect this type of patterns. Hopefully, when you have many people (experts as well as common people) looking at data patterns we are able to solve some of the serious problems that we are faced as a society.

TowerGroup: Losses Resulting from Mortgage Fraud in U.S. Will Reach $2.5 Billion in 2008

For U.S. companies the mortgage fraud software can be provided as a software as a service with analysts offshore. This will bring a cost-efficient and robust business analytics solution to this problem for financial institutions. As the Federal Reserve Board discuss the new oversight requirements for investment banks that are borrowing money from the Fed the requirement of a robust fraud prevention program could become a requirement.

Data Mining for Disease Management:Adding Value to Patient Records

This is a good article. I still question the underlying premise within the medical community that expects "perfect data" to equal perfect results. Data mining is about finding patterns (known and unknown) in the data that would allow imperfect people to make better decisions.

Software as a Service overview

This is a good overview video of software as a service (SaaS) if you need to explain the concept to others. In the case of SaaS analytics the issue is to create modules by industry. For example, I have worked on modules for payers, providers, pharmaceutical and DME companies. I am currently working on modules for the financial industry: banks, investment banks, brokerage companies, and insurance companies. Also, I am working modules for supply chain and biotechnology. Is anyone else working on SaaS modules by industry?

The Financial Market Crisis and Risks for Latin America

The purpose of this article is to give an example of the variables used in international credit risk exposure due to the changing world financial markets. We know that financial institutions in the U.S. and Europe have been negatively impacted, but banks in China and Latin America have a lower exposure because their main growth in organic or in their own domestic markets.

Therapeutic Cloning Works in Mice With Parkinson's

This is the type of discovery that a data mining system can have a direct impact.

Why good companies go bad

This is a classic article that I thought in this changing economic times could be a good reminder to companies that change could mean doing something different instead of just doing more or faster the same things that a company has done in the past. An enterprise analytics solution could help executives determine whether doing more of the same is helping or getting their companies in a bigger rut.

Enterprise Analytics: A Business Decision

The last three years have seen advances in efficient data mining algorithms, and computing advances that allow software companies to provide powerful analytical tools that were not available to the business community some years ago. Whether we refer to this type of software as data mining, predictive analytics, business intelligence, or analytics (web or business) their purpose is to efficiently detect patterns in large datasets that can lead to increase revenues or lower costs. The purpose of this article is to give a general framework to businesses regarding how to measure the different analytics solutions available in the marketplace.

First, let us make the difference between an analytical tool and an analytical solution. Most companies need an analytical solution instead of an analytical tool. Software vendors are in the business of selling analytical tools (SAS, Microsoft, Oracle, Business Objects, and SPSS). All these companies are using state-of-the-art science and technology to create powerful analytical tools. A hammer, a saw, and nails in the hands of a master carpenter can create a beautiful house. Those same tools in somebody else hands are something that you put in the garage. Therefore, the question is do you need an analytic tool or do you need an analytic solution. If your IT staff has not built an advanced analytical decision support system in the past what you need is a solution. If they have this experience then what you might need is a new tool. One way to tell the difference is whether the sum, or average (or mean) are the most common measurements used in your organization. If this is the case, you need an analytics solution and not a new tool.

Most carpenters will tell you that some people have all the tools needed to build a house in their garage. Therefore, before going out buying new tools you need to know: 1. what do you want to build? 2. What skills do you have in designing and building an analytical system? What tools you already have in your IT department?

Alignment with specific business objectives
What do you want to build? The answer to this question is that you want an analytical solution which is aligned with your organization strategic and operational objectives. This is one of the keystones of an analytical solution: it must match strategic and operational objectives. An enterprise strategic objective will represent the schematics or floor plan of your analytical solution. The enterprise operational objectives will represent the plumbing and the electrical systems of your analytical solution.

An analytical solution must ultimately represent the vision of executive management, while simultaneously be efficient to operate for those responsible for day-to-day operations. We know that a CEO can write his strategic vision even in a napkin and the role of his team should be to translate this vision into a series of strategic and operational goals. Hence, the importance that the overall strategic design of an analytics solution starts at the CEO and executive management team level. The decision to build an enterprise analytics system is an executive decision. A CEO thinks in terms of analytic solutions to business problems, and the IT department tendency is to think in terms of tools.

An experience analytics architect should be able to turn the executives input into an analytics solution prototype with some additional research and input from operations. This prototype should be detailed and must be approved by executive management before proceeding with specific business requirements and technical design. See,http://www.youtube.com/watch?v=bVmLfCajDjI.

Encompass all relevant data
Your data is the equivalent of your raw materials (wood, stone, bricks, pipes, flooring, carpets, and drywall). Since not all your materials are the analytics solutions architect and the lead software developers need to have the proper experience with large, diverse and complex datasets. Not all the houses are the same but the materials used are mostly the same. The same logic applies to the designing and building of an analytical solution. It is important not to confuse the quality of a tool to the quality of the materials. The tools can make a job easier for the builder, but is the quality of the materials in the hands of an expert builder that is going to make an average house an outstanding home.

The skill level of technical personnel varies and is as diverse as the number of home builders in the country. Some IT departments are made of technical staff that can give maintenance to current operations. Other IT departments have a specialized area for development, and others have fully functional project management offices (PMO). Very seldom do IT departments have an integrated analytics expertise (statistician and actuaries) and specialized software analytics developers (data cleansing, OLAP, and interactive visualization) within their departments that would allow the creation of advanced analytics solutions.

Flexibility of use for different business users
A robust analytical solution needs to be flexible for the different business users within an organization, and be adaptable to future analytical needs. Some users are interested in strategic objectives and others in operational objectives. An enterprise analytics solution should meet both strategic and operational demands. A keystone of an analytical solution is that it should allow different business users to have access to it. A business problem may have different potential solutions and analytical solution should take advantage of different perspectives from different users (human interaction) to identify potential solutions.

Also, it should have the capacity to accommodate future growth and changing business needs. The current business analytics requirements of an enterprise may be different from the analytical needs of the future. An analytics solution needs to be design with the capacity to growth, and the flexibility to accommodate unforeseen business issues.

In conclusion, a business analytics solution should cover the fundamentals:
1. Emphasize the alignment of the strategic and operational objectives to the analytic solution instead of the analytical tool;
2. Make sure that you have the correct materials (staff and data) to build your solution;and
3. Design a flexible analytical solution that could be used by strategic and operational users.

Human Computer Interaction

This is the site for the Human Computer Interaction at the University of Konstanz in Germany. If you want to know about best practices in interactions and visualization techniques, see http://hci.uni-konstanz.de/index.php?a=teaching&b=corner&c=15851838&lang=en

Interaction Techniques for High Resolution Displays

A video of interactive visualization. Imagine analyzing data using this type of techniques and that is available to everybody in an organization.

Real Time Scalable Visual Analysis on Mobile Devices

This is the next logical step: integrating analytics visualization with mobile devices.

Gartner: Emerging Technologies Will Help Drive Mainstream BI Adoption

Gartner's analysis is 100% on target. Business intelligence is more than creating an enterprise data warehouse, it is about transforming data into actionable information by allowing all individuals within an organization to make decisions based on business analytics.

Mid-Market Insurance Provider Selects Blink Logic for SaaS BI Solution

Two main reasons that I am posting this article:
1. It illustrates that analytics can be provided using a software as a service (SaaS) business model; and
2. It brings home the point that business analytics is not only for highly-skilled analysts, but available to the decision-makers as well.
This is the future of analytics!

Human Factors In Visualization Research

It is the interaction of the computing capabilities and the human mind capabilities that allows for evidence-based decision making in situations involving large sets of data. Therefore, in order to make evidence-based decisions data must be summarized in such a way that takes advantage of the increddible capabilities of the human mind. A good data mining visualization tool should be able to separate the clusters of data and clarify the driving factors by clearly and cognitively recognizing the patterns or trends in the data.

This is a good article that emphasizes the cooperation between the end-users and the development of visualization tools for data mining and business analytics. The bottom line: make it simple and intuitive!

Visualization Projects for Data Mining

These are the current projects for the visualization group at the Lawrence Berkeley National Laboratory. One of the members of the Business Analytics Group expressed interest in the latest developments in network traffic analysis and cybersecurity (see, http://vis.lbl.gov/Vignettes/QDV-NetworkTraffic/qdv-vignette.html).
I would suggest to spend 1-2 minutes for each project and just look at the visualizations and determine if this is something that can be apply to an organization as part of your business analytics and predictive modeling. The visualization technology is available right now. The question is whether the business is in a position to accept these concepts as a way to look at large datasets. We know that there is not even a probability that a spreadsheet can capture the complexity of large datasets. You may want to start with proposing a simpler visualization like SAS, SPSS and Cognos, Business Objects, or Microsofot Analysis Services and Excel 2007. The key for these visualization tools is to train the business users in using these tools. My approach is to teach first executive management and find what tools they find more useful. The purpose of these visualization tools is to let the human brain look at the totality of the data and start discovering new trends and patterns.

SAS Advances Enterprise Intelligence

Without a visualization example this are just words.

Risk Management Confidence: Higher-ups More Conservative

My interpretation of this article: executives that use predictive modeling understand the complexity of the risks involved and threfore are less prone to take non-fact based risks.

MADE IN IBM LABS: IBM Software Finds Hidden Product and Service Insight in Customer Interactions

I wish that when this type of news would come out a small visualization example of the output will be publish, since the visualization is what is going to tell us whether a business analytics tool is efficient in assisting an end user in looking at the totality of the data by separating the clusters in the data and clarifying the driving factors.

Financial Markets Crisis: A Potential Solution

In the late 1980's I had the opportunity to participate in the discussions of what to do with billions of dollars in commercial bank debt for countries in Latin America. In a nutshell, commercial banks had acquired a very large exposure to debt from Latin American countries, and in reality most of that debt was worthless although the interest rates were very high. The issue was similar than today's mortage backed subprime securities: the lending institution failed to follow fundamentally sound practices in their portfolios and their exposure came to the point of affecting the world finance markets. I remember the 2nd International Conference on Debt and Trade where the policy makers, financial instituions, and debt-ridden countries met. One of the solutions was to create markets for this exposure and hence the birth of the Bradley bonds. At the time, I was a proponent of a more drastic measure, to let the market forces decide the outcome of each financial instituion according to their exposure. Over the years I have come to the conclusion that the Bradley bond secondary market has substantially assisted in bringing order to the financial markets in a way that has not negatively affected Latin America countries.

My proposed solution to the current financial markets crisis is a three tier solution:
1. Allow a market for mortage backed securities in which central banks around the world could buy some of the exposures of large financial institutions. I would suggest up to 3% of the market capitalization of each large financial institution. This solution will allow the internalization of the financial markets by allowing central banks to have a direct ownership in private financial institutions. I know that this is a radical solution, but the liquidity problems in the financial markets are of such magnitutde that it requires the intervention of central banks around the world in a different way and at a different magnitude that past remedies. Special class of stocks can be created that will allow foreign central banks to participate that would restric voting rights, while simultaneously allows their input into investment practices.
2. Create a tax incentive for financial institutions in which they could lower by a total up to 3% the interest rate and/or the principal of the mortgage loan, for properties with the value of a mortage of up to $250,000. The total amount of the tax incentive could be cap at $2,500 per qualifying loan.
3. For properties with a mortgage value greater than $250,000 I would suggest to let the market forces dictate what will happen to these properties and the financial institutions that made those investments.

DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKERS

This article covers the fundamentals of data mining in financial markets and banking. The section on risk management (financial market risk and credit risk) is something that is worth taking a look at the fundamentals in today's changing financial markets. It is amazing how many times we need to go back to the fundamentals in the banking and finance industries to make sense of the corrections in the markets.

Business Analytics and Financial Markets Liquidity Issues

The news that Bear Sterns needs liquidity assistance from JP Morgan Chase and the Federal Reserve was predictable using off-the-shelve predictive analytics software. For those who are engaged in financial markets analytics let me suggest that instead of using Bear Sterns as your sampling data (before applying in to the entire dataset), use instead the Carlyle Fund data as your market for predictions. This data is less noisy and would help you determine the ratio of mortage based secutirites: total capital under management to determine risk in liquidity.

Also, please look at the other variables (i.e., geography, diversification, currency hedges, etc.) to determine what constitutes a robust liquidity scenario. In other words, invert the scenario and analyze the driving factors. Let me suggest the creation of multidimensional vectors and combine them with cluster analysis so you can separate the clusters of data and clarify the driving factors. This is a great opportunity to bring potential solutions to the executives.

A technical framework for sense-and-respond business management

This paper from the IBM Labs presents a good technical framework about what processes could be needed to achieve change managemetn when introducing predictive analytics (phrase taken from James Taylor) in a rapid changing enviroment. I am not suggesting that this process is exclusive or innovative, but I am suggesting that a process is indeed needed for change management to take place (if you fail to plan, you plan to fail). What other processes, or components of a process, are needed to introduce predictive analytics into a rapid changing environment? If you take into account the changing nature of the world economy and how is affecting organizations, what processes need to be in place to introduce predictive analytics into an organization? We know that is not a matter of the science and technology since those are already in place.

Emerging Trends in Business Analytics

I specifically like the emphasis and analysis of business users as it concerns business analytics in this article. The emphasis on solving business problems (not technical problems) and the ability to measure results are great points too. This brings me to the topic of change management and metrics. A collegue of mine, Mitch Weisberg, is the expert in this area: how to have the right metrics and the right processes that will incorporate advanced business analytics into the fabric of an organization.

Top 10 Unsolved Information Visualization Problems

A well thought out paper about the issues with visualization of information. I believe that the issue has been defined by a collegue, Edith Ohri, when she told me that that we (those with the expertise) have the responsibility to explain the issues to the executives in such a way that they can understand. Her definition of multidimensional vector analysis is beautiful: separating the clusters of data while clarifying the driving factors.

Cognos and SPSS Forge Partnership to Deliver Predictive Analytics Integration

This is big news. This moves Cognos from a reporting tool to a predictive analytics visualization tool. This bring IBM and SPSS in direct competition with Microsft, SAP, and SAS.

Information Visualization and Visual Data Mining

A little thick but extremely accurate. As companies prepare to provide analytics in software as a service (SaaS) they must be aware of the nteraction between data mining visualization and the human component. Data mining visualization is the next evolutionary step in utilizing the full capabilities of the human mind.

Business intelligence on demand

The next generation of predictive analytics is here!
Congratultions for a job well done!
Software as a Service (SaaS) in predictive analytics!

Why Don't American CIOs Want to Lead In Emerging Technologies?

Read this article. My answers to the question: timidity, lack of understanding of what it means to be an executive, some people are followers and some are leaders, and it is the safe route.

Why Business Analytics as a Service Won’t Spook IT

This is a very good and insightful article. Business Analytics as a Service (SaaS) is where the market is moving to. the main reason is that CIOs and business users do not want the details of advanced analytics, they just want a tool that works. Last week I was talking with a couple of IT executives from different large companies and they are looking for something "actionable". SaaS meets this requirement.

My only difference with the authors is that when people talk about "Excel hell" they do not know the capabilities that SQL 2005 Server Analysis Services and Excel 2007 can bring to an organization in terms of advanced analytics. I can see the combination of these two products becoming the first sucessful SaaS product for large enterprises as well as small businesses.

Visualization of Data Mining

I have found that using a clustering analysis visualization including a multidimensional vector is an efficient way to cut the cost of predictive modeling while simultaneously making data mining available to everyone within an organization. The advantage of this process is that it allows everyone to see large data sets in such a way that they can draw their own conclusions even without subject matter expertise.

Microsoft and Yahoo

I personally think that Microsoft proposed adquisition of Yahoo is a good idea. Specifically, from my little part of the universe (data mining, predictive modeling, and analytics) Microsoft has powerful tools in SQL 2005 Server Analysis Services and Excel 2007 when used together. Microsoft and Yahoo have mature and first-of-class research laboratories. I believe that this type of adquisition will benefit the consumer of healthcare and other industries.

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.