Operational Analytics in a Recessionary Environment

Lately I have faced an interesting issue with large companies seeking business analytics assistance: how to provide operational predictive analytics to companies that need results in no more than 5-30 days when their budget is extremely limited and access to their data is narrow. The pressures that companies are facing in these recessionary times are:

1. Time-to-Market – companies must turn around in a quarter to show improvement to their investors;
2. Cost-Containment – predictive analytics solutions must be inexpensive to implement; and
3. Data Limitations – companies can only spend a minimum amount of time in assisting vendors with data issues.

I have found that these issues represent an opportunity for predictive analytics vendors (services, hardware, and software) that have a flexible business model. The answer to this issue involves two old sayings: No man is an island, and You eat an elephant one bite at a time. Alliances with established companies, as well as new vendors, becomes essential, since collaboration is a tenant of surviving in difficult times. The ability to bring together different skills and experiences, as well as to have a flexible position to solve problems, is a keystone in measuring success in these times. Another keystone is to divide predictive analytics issues into small and measurable parts. Vendors that have the ability to prioritize client’s issues have an opportunity to be successful. Prioritization includes the possibility that initial revenues for an analytical project may be limited, but the payoff is an immediate lift to the client.
The time where companies could afford even a free six-month proof of concept in analytics is becoming a thing of the past. Companies do not have the time nor the inclination to hear, “It cannot be done.” Companies literally want and need predictive analytics today so they can face the challenges of tomorrow.
Have you found similar issues? If so, how did you deal with them?

Is BI Technology Too Sexy?

Interesting premise from Peter O'Donnel that BI software vendors are incorrectly selling "the cult of the new".

Happy Independence Day India!

The road to independence was hard but worthy
The road of a free nation is hard but worthy
The people of India known the meaning of hardship and worthiness
I am privileged to have them as my friends
Happy Independence Day India!

Workforce Turnover Efficiency Ratio

This is an article about a management ratio that I created to measure how efficently a company is using its workforce.

Business Competency Model

This is the reprint of an article that I wrote about corporate strategy consulting some years ago.

Data Mining for Fraud Detection

This is a link to the methodology that I have been sucessfully using for the last 8 years in healthcare. I have found that this outlier analysis works for all industries as well.

Corporate Analytics During a Recession

This is a link to an article about how to optimize profits during a recession using enterprise analytics.

Social Media Business Analytics Opportunities: Comments, Quizzes, Surveys, and Games:

If you are in Facebook, Twitter, Linkedin, or any social media you have seen a large number of friends post comments, or take quizzes, surveys, and play games. What the implications of the use of these gadgets in the area of business analytics? The implication is a new wave of opportunities by leveraging the information gathered from these gadgets to increase revenues and profits, and reduce costs. These gadgets may be silly but they contain a troll of information.
1. Advertisers can use segmentation to create robust profiles of customers. Also, they can use cross-products predictive modeling, and spatial (geographical) benchmarking.
2. Marketers can use CTR (Click-thru-Rate) outlier analysis, GRP (Gross Ratings Points) time-series, and TRP (Targeted Rating Points) predictive modeling.
3. Social Media companies can use log-on outlier (to make capacity planning decisions). Also, they can do contribution (users that contribute) segmentation, and use time-series to determine time spend online.
4. Gaming companies can predict revenues by using predictive modeling, and outlier analysis to detect fraud and abuse. Also, they can control inflation using virtual economy tools.
5. Online customer service can do TPC (Time-per-Call) outlier analysis, and segmentation to create profiles based on average cost. Also, they can use predictive modeling to predict and control costs.
6. Blogging sites can use statistical control process to determine best practices in customer reach, as well as predictive modeling. Also, they can create strong branding variables.
The above-mentioned analytics combined with a powerful 3D visualization allows new media companies to increase their ROI. My advice to new media companies is if you want to increase profitability you must use advanced analytics techniques to analyze information on quizzes, surveys, and games.
Contact author at: alberto.roldan@cognizant.com

Researchers mine millions of metaphors through computer-based techniques

This technique developed at Stanford University is going to revolutionized text searching. The 'proximity-searching machine-learning" technique have potential applications in CRM databases for financial services, retail, and CPG. Even more interesting is to use this technique for clinical research using patient records in order to link symptoms, diagnoses, and treatment. Also, it has uses for government intelligence gathering.

Detecting the Madoff Effect: Methodology for Fraud in Hedge Funds

As a result of the recent Bernard Madoff fraud scheme, pension funds and corporate finance managers have been put on the defensive, wondering how to detect this type of “under the radar” deceptive scam. The depth of the fraud in the case of Bernard Madoff and his ability to engage in a $50 billion undetected scheme employing “serial correlation” demonstrated the vulnerability of financial institutions to trusted individuals operating inside the security model. Bernard L. Madoff Investment Securities LLC (“Madoff”) engaged in a ponzi or pyramid fraudulent scheme in which investors were paid interests not from actual investments but from the funds deposited by other investors. But being on the defensive is not the ideal solution, as it forces financial institutions into a reactive mode, always trying to catch-up with the perpetrators, who somehow remain one step ahead. We would like to suggest a more proactive approach and corresponding methodology for detecting fraud in hedge funds.

Madoff was able to hide his scheme using a “serial correlation” reporting scheme. A serial correlation is a term used by MIT professor and hedge fund theorist Andrew Lo to describe the degree to which each month's returns in a fund mirror the results of the month before. Dr. Lo’s theory is that is a hedge fund has a nice smooth line in its rate of return every month. Upon close examination, any variation to the “smoothness” of the line constitutes a red flag, which should be look upon more carefully.

In the last year corporate finance departments, financial institutions, as well as public and private pension fund portfolios have already lost about 33% of their values due to overleveraged investment banks, the housing and credit crises. An effective, (no more than 1 to 3 weeks) and cost efficient proactive methodology to detect the Madoff effect in the hedge funds would be to apply the following methodology in the specified order to the relevant data available to you:

• Link Analysis – Use link analysis to determine in the network Madoff is categorized by, and create a subset of that network of hedge funds.
• Predictive Modeling – Use predictive modeling to create a score of all the hedge funds in your subset. Use Madoff’s variables as your training data.
• Clustering Analysis – Perform a cluster analysis which includes among other variables the predictive score. Since the predictive score is a multidimensional variable when used with one-dimensional or “flat” variables you will obtain a binocular vision (or binocular summation) of your analysis and increase by 1.4 times the ability to detect the serial correlation. See, Improving Search Engine Optimization by Incorporating Predictive Analytics at http://atomai.blogspot.com/2008/12/improving-search-engine-optimization-by.html
  
  For verification of the analysis you could use the following factors:
• The reputation of the independent auditors of the hedge fund identified through this methodology;
• Control Chart using standard deviation of the yearly returns over a 3-5 year period (exclude the current year);
• The ratio of total number of employees to the total amount of investments.
  
  Contact Alberto Roldan at atomanalytics@gmail.com or Sean Suskind at seansuskind@gmail.com
  

Improving Search Engine Optimization by Incorporating Predictive Analytics

As more companies increase the size of their databases search engine optimization (SEO) techniques can be adapted to data mining of commercial databases. In SEO link analysis is a measure of the quality and relevance of the set of links pointing to a given site. This is measured is achieved through an algorithm that maps the hyperlinks in a series of networks. The measurement creates a ranking of the strength of the inbound links to a particular network. The objective of link analysis is to detect patterns or trends that would make the search engine to bring to the top the most relevant web sites in any search.

Link analysis contains multiple variables that are analyzed. Google’s claims over 200 variables are analyzed in its link analysis for its ranking algorithm. Although I do not know which variables it uses, I surmise that they are the keystone of Google’s success. The core of any analysis is its variables. Let me suggest the utilization of predictive modeling as an additional variable that will improve SEO.

Using predictive modeling as another variable in link analysis could potentially increase SEO by 1.4 times by giving depth perception to the link analysis. In ophthalmology medicine it an established fact that binocular vision gives depth perception, and that depth perception (or binocular vision) increases the range of view 1.4 times greater than monocular vision. In other words, you can see better with two eyes than with one eye. The equivalent of depth perception in analytics is the addition of a predictive modeling (or scoring) variable to any pattern detection analysis.

A predictive modeling variable will improve the SEO because:

1. It gives an independent variable that acts as a spare variable in case that another variable is not working. In other words, you can use a predictive modeling variable in a correlation analysis as your independent variable against the other numerical variables in your link analysis.
2. A predictive modeling variable will widen the field of view of your networks from 160 degrees to 200 degrees.
3. Binocular summation (seeing with two eyes) will enhance faint but important networks and links within your data.

Among SEO scientists, statisticians, and business analysts it would increase stereopsis, or the keen sense that they have depth perception. In other words, it would give them another tool to do their work more efficiently.


Most of the variables used in link analysis are flat, or with one-dimension. A predictive modeling variable is multidimensional and hence a “depth variable”. The addition of a “depth variable” to any analysis statistically can be expressed as detecting the networks using two sensors instead of one. If each flat variable alone had a 0.6 probability of detecting a network, that probability has been calculated to be:

Pb = Pr + Pl - (Pr x Pl) = 0.6 + 0.6 - (0.6 x 0.6 ) = 0.84 (1)

The improvement from 0.6 to 0.84 represents a 1.4 fold improvement. This improvement can be achieved in any analytics technique by adding a multidimensional variable to a one- dimensional variable during analysis.

Contact: Alberto Roldan, CEO of R&R Analytics at atomanalytics@gmail.com

Depression Economics: America’s Economic Crisis

I have written many times in the last 12 months about this issue in the Business Analytics website at http://atomai.blogspot.com/. This interview from Paul Krugman in Newsweek gives us an insight into how deep is the ocean in the financial and credit crisis. If you look in page 2 of the interview Krugman talks about a $10 trillion (yes trillion with a “t”) shadow banking system that just went up on smoke. Hence, he argues that a $700 billion capital injection by the Federal Reserve and Treasury Department will be insufficient to make up for those $10 trillion. I agree with Krugman, but we need to consider that if we take into consideration the capital injection that have an effect in America’s economic crisis by other countries in Western Europe, Russia, China, and Japan. We are talking more than $3 trillion injected into the world economy when we take those capital injections into consideration. This still leaves us with an $7 trillion issue. The next issue is the valuation of those depleted assets, because it is the difference between the $7 trillion and the value of the depleted assets that are going to determine when we are going to touch bottom. I estimate that the minimum value of those assets will be about $3.6 trillion, and that is going to leave us with about $3.4 trillion that the international capital markets are going to deal with a combination of global stock market devaluations and injection of additional capital by central banks. The jobs program and other programs by president-elect Obama will have an effect on the economic crisis but it is too early to determine what that effect will be. If the administration of President-elect Obama injects into the economy $1 trillion through different programs, including a jobs program, we will still have $2.4 trillion to deal with. The bottom line is that companies need to prepare to cut 12% to 24% in additional expenses above what they have already trimmed. The role of cost-efficient business analytics decision-support systems at the operational level is going to become a cornerstone in the transformation of companies in how to increase the margin of profit with less resources.

The Krugman interview is at http://www.newsweek.com/id/171871/page/1

Successful Business Intelligence Projects: The Role of Managers and Leaders

Most BI projects fail because the leadership for those projects is wrong. An article on how Some Brains Wire for Change[i] helps explain the physiological reasons how some individuals can adapt easier to change than others. This article makes clear that people’s brains are different and that different does not mean “bad”. In today’s recession it is important for organizations to understand the role of a manager vis-à-vis the need for a leader in the area of analytics.

Every organization needs both managers and leaders in analytics. Managers are those individuals who supervise individuals who conduct analytics within an organization. Leaders are those individuals who guide or have commanding authority in the area of analytics within an organization.

I have found that organizations tend to have good managers in the area of analytics but lack leaders. Managers are efficient at maintaining the status quo and are adverse to risks. Leaders are risk takers and innovators, but not necessarily proficient at managing or maintenance of a department.

An organization that is satisfied with how its analytical capabilities are producing a lift in their revenues and profits, should be looking to improve how to efficiently manage those capabilities. On the other hand, an organization that is looking to improve revenues, costs, or profitability by using its analytical capabilities needs leadership in the area of analytics. A good manager realizes when he needs a leader, and a good leader acknowledges the need for a manager.

Managers and leaders of analytics have different roles, and although they are not mutually exclusive it is the role of executive management to define the priorities in the area of analytics at any given time. Sometimes organizations make the mistake of trying to make managers leaders or vice-versa. The results are that the analytics capabilities within an organization never bloom to its full potential in contributing to increased profits. In my experience managers contribute about 80% to 90% of the success of a business intelligence project, and leaders contribute 10% to 20% of the success of the project. Therefore, a successful BI project needs both managers and leaders.

Contact Alberto Roldan at R&R Analytics at atomanalytics@gmail.com
[i] http://www.livescience.com/health/081201-brain-personality.html

The Commoditization of Analytics

This is an article that argues that since analytics are based in long-standing math concepts they should be treated as a commodity. Hence, the difference in analytics is not the software vendor but:

1. The domain knowledge of the company doing the implementation of any decision-support system.
2. The total cost of the implementation should consider Software-as-a-Service as an alternative to reduce costs.
3. Visualization of analytics is an important component when dealing with large datasets.

This particular point of view is targeting the CFO and CIO from companies that are under pressure to deliver cost-effective solutions that have a direct impact on profitability during recessionary times.

Healthcare Payer Analytics

The link is to an article of the healthcare payer/insurance analytics available in the market. There are three categories of analytic services:

1. Outlier Detection - This area includes claims auditing, fraud and abuse, and Medicare Part D programs.
2. Predictive Modeling - This area includes HCC reconciliation and Outcomes Research.
3. Data Mining - This area includes claims processing, Medicare Advantage, and Care Management.

The architecture of the analytics engine allows for individual portals for payer staff, providers, members, and employers. There are over 300 reports. This architecture is compatible with Oracle (OBIEE), Microsoft (SQL 2005 & 2008), SAS Enterprise Data Miner, Business Objects, Hyperion, Cognos, and SPSS Clementine.

See: http://docs.google.com/Doc?id=dd87qswp_12xvh3mgc6&hl=en

If you have any questions contact Alberto at atomanalytics@gmail.com

Analytics In A Global Recession: Fixed Price Operational Dashboard

As the world economy moves towards a potential global recession companies are asking about how could they could optimized their investments in business intelligence, BPM, data mining, and analytics. The answer is simple: go to a fixed price operational dashboard model that requires vendors and service providers to:

1. Deliver within 90 days a proof of concept (POC) operational decision support system;

2. Operational means that information workers within the company can use the decision support system to meet a company's financial goals;

3. The POC should be measured in one of three simple key performance indicators: increase revenues, decrease costs, or increase the percentage of profits; and

4. Total price for POC should not be more than $25,000

In a recession companies must manage with a laser focus their revenues, expenditures, and investments. Strategic dashboards are nice, but operational dashboards reach down into the income producing worker. The goal of achieving a cost efficient and revenue enhancing operational dashboard is within all companies regardless of the technology that they use (i.e., Oracle/Hyperion, IBM/Cognos, Microsoft SQL 2005 and 2008, SAS, or Business Objects/SAP). Even small companies can use Software-as-a-Service analytics like Zementis to build operational dashboards that are cost efficient.

Let an analytics operational dashboard be the compass that guides your company in this stormy economic times. Contact me at atomanalytics@gmail.com If you would like to work with a technology provider who you can trust to work together in providing a fix cost operational dashboard in the marketing, retail, banking, insurance, manufacturing, CRM, or healthcare industries.

Alberto Roldan

See, Business Analytics at: http://www.linkedin.com/redirect?url=http%3A%2F%2Fatomai%2Eblogspot%2Ecom%2F&urlhash=AwT7&_t=disc_detail_link

IT and Enterprise Architecture Metrics: Managing in Tough Economic Times

How can IT demonstrate its value to the CFO in this times of economic downturn? How can IT make the case for funding for new projects? Lately I have seen this type of questions asked more often. The answer is that IT must have metrics that are directly connected to corporate revenues, expenditures, and profitability. In order to develop this metrics IT must partner with the business units. For example, if a business department wants a more efficeint application so that workers spend less time manually completing reports IT must ask to that department: how is the improvement in the application going to affect revenues, expenditures, or profitability. IT and business departments should be sitting down and making project-specific economic forecasts that are measurable in terms of the economic health of a company. If you need assistance in making these forecasts and its accompanying metrics let me know and I will assist you in this area.

The Democratization of Analytics - Microsoft Project Gemini

Last week I attended the Microsoft BI Conference. I learned about project Gemini. This project will allow analytics power users in companies to use Excel to do powerful analytics, while simultaneously allowing collaboration among all stakeholders using PerformancePoint. It allows Excel to load over 100 million rows (and about 6 columns) in just a few seconds and then create interactive pivot tables. They are still working on calculations but the demonstration was powerful. If you see Ted Kummert, Bruno Aziza, Kamal Hathi, Donald Farmer, or Amir Netz in a meeting make sure that you let them know that they are doing a great job.

Microsoft is doing a fantastic job at integrating Analysis Services, Integration Services, Reporting Services (the visualizations are robust in SQL 2008 RS), Excel Services, and PerformancePoint. This family of products are very different than SQL Server 2000. If you have followed this products for over 5 years you realize that the 2005 and 2008 products are totally different than the 2000.

A brief note about PerformancePoint and Reporting Services. For the first time I saw powerful visualizations that allow reports to be actionable. This is impressive and tells a lot about the folks working to develop these products.

Sometime ago I wrote: "If anticipation is part of journey, I can hardly wait for the sounds and tastes that will come in the next 12 months when business, technology, and science merge to produce masterpieces to help solve complex business problems." See, http://atomai.blogspot.com/2008/06/genetic-algorithm-grid-computing-and.html. Microsoft is providing the right tools for those who do analytics, to do their job in the most efficient manner.

In the Disney-PIXAR movie Ratatouille a Chef says that "anyone can cook". Microsoft is saying anyone can do analytics, and I agree. See, http://atomai.blogspot.com/2008/05/business-analytics-unleashing-power.html

Financial Markets & Credit Crisis: A Potential Solution

The failing of financial institutions are having a global impact on the financial and credit markets. The issues with Fannie Mae, Frddie Mac, Merryl Lynch, Lehman, AIG, and Countrywide were fairly obvious. Since March I have been predicting these type of failures by using the ratio of mortage based securities in a companies portfolio to total capital under management (mortage based securties/total capital under management). Also, I suggested that you use the Carlysle Fund ratio as your upper benchmark (or alarm). See, http://atomai.blogspot.com/2008/03/business-analytics-and-financial.html. If investors are being caught in these financial institutions failures and downgrades is because they are failling to do basic due diligence.

Also, I have suggested potential solutions that may have sounded fairly radical 6 months ago, but they are sounding more reasonable in today's financial markets. Let's allow more direct foreign investment in our capital markets. Second, target housing relief, for homeowners and financial institutions, to primary homes with a market value of up to $250,000. Lastly, let the market forces dictate what happens to properties valued at over $250,000. See, http://atomai.blogspot.com/2008/03/financial-markets-crisis-potential.html.

I have also suggested new metrics, using advanced analytics, as well as financial models that can be used to gauge risk and create a robust decision support system. See, http://atomai.blogspot.com/2008/05/bernanke-banks-must-get-better-at.html

These problems are not easy, but that is part of life...what we need is the will to implement these changes.

Microsoft BI Conference

Business Analytics Group Members,
I will be present at the Microsoft BI Conference in Oct 6-8 in Seattle, Washington, representing HP at our booth answering questions about BI design using SSAS in the the financial, marketing, internet, healthcare, CPG, life sciences, and retail industries. Also, I will be covering how to use advanced analytics for: improving view of the data by 40%; fraud detection; and improving server performance in large databases. If you are attending the conference let me know so we can meet.

I have met some of you, but most of the members of the group I have never meet in person. This is a great opportunity to meet, exchange opinions, and probably get some souvenir from HP. In the best case scenario you can claim that you saved thousand of dollars by consulting with me for free. In the worst case scenario you can claim that you "stumped the chump" (i.e., me) with your questions and knowledge.
Thanks,
Alberto
alberto.roldan@hp.com