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