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.

New Media Quizzes, Surveys, and Games: Business Analytics Opportunities

If you are in Facebook you have seen a large number of friends 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