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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Monthly Archives: December 2009

12/14
2009
12/11
2009

Using Analytics and Visualization to Predict Natural Gas Forecasts

j04432093 150x150 Using Analytics and Visualization to Predict Natural Gas ForecastsThe ability to use business intelligence analytic and visualization tools to forecast short-term energy demands and gas price futures is a given for energy traders today.  The opportunity is to lengthen the forecast period and abilities to look at longer range patterns and predict, based on the weather, a profitable trading or hedging strategy for gas futures.  This is a very interesting, cutting edge and exciting use of business intelligence tools.

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12/10
2009

Faster, Smarter Utilities Need Faster, Smarter Analytics

j0442296 150x150 Faster, Smarter Utilities Need Faster, Smarter AnalyticsIf you lost electric power while you were asleep, how would you know?

Probably not until you wake up and see the flashing LED clocks on your bedside table, coffee maker or cable TV box.  The same lack of ‘business intelligence’ has been the norm for electric utilities, which may not know about system outages until CUSTOMERS called to complain they were without power.

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12/09
2009

Business Intelligence Answers the Gift-Buying Challenge

j0440999 150x150 Business Intelligence Answers the Gift Buying ChallengeDecember means gift-buying to many people, and thanks to Spotfire, we have a business intelligence solution to guide us in our gift-buying.  The Spotfire Holiday Gift Finder lets you browse over 10,000 hand-made gifts you won’t find on big retail sites.  And, using business intelligence, the Gift Finder helps you select the right gift in just seconds.

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12/08
2009

Prescribing BI and Analytics For The U.S. Health Care Debate

j0409702 150x150 Prescribing BI and Analytics For The U.S. Health Care DebateOne sure thing in the cloud of uncertainty regarding changes to the U.S. health care system: analytics and data verification will be critical to reviewing thousands of pages of documents and the charts, graphs and statistics used by various industries in the debate.  When Congress convenes to examine H.R. 3962 there will be a lot to analyze.  Entire global industries that have grown by sharing broad, unknowable risks– insurance and especially health insurance – now face the 21st century realty of predictable outcomes and specific forecasts.  Tom Davenport, co-author of Competing on Analytics: The New Science of Winning, writes about the future prospects of change with clear-eyed realism.

When a company or organization seeks health insurance for a large group of employees, the particular attributes of those employees aren’t usually assessed in detail.  “Increasingly, however, life and property & casualty insurers have attempted to increase their profits by predicting just how much risk a particular customer represents, and pricing the risk accordingly,” he writes at the Harvard Business School Press website. “Pooling the risk, it seems, is no longer an attractive proposition for life and property insurers.

Yes, some elements of your personal medical history are protected INSIDE of doctor offices and hospitals by HIPAA and other laws, there are plenty of other unsecured and correlated data outside those offices that can link to greater risk of early death, behavioral, financial or other risks.

“All you need to know is how much someone weighs, what kind of food he or she eats, how much exercise they get, and so forth.  Much of that information is publicly available, can be bought, or can be legally requested in insurance applications.  One health insurance actuary told me that such “lifestyle” data is a much better predictor than age of who is going to contract, say, diabetes.  Among 45-year olds, for example, there is an eightfold difference in annual medical spending between the highest-risk lifestyle group and the lowest,” Davenport writes.

Lifestyle indicators are already gaining popularity in health insurance firms, who enroll certain customers in “disease management” programs. Some can reduce risks of certain conditions.  But Davenport notes that data can also be used to refuse coverage, or to price coverage at a much higher level.  Sometimes the details unearthed by advanced analytics can have unexpected or negative results.

The prospect of universal coverage in the United States could move more people to shop for insurance as individuals (and trackable) instead of as employees who might be harder to classify as group.  It will be possible for insurance firms to identify which customers will be profitable, and which will cost too much money to insure.  And some insurers will be better at predicting risk than others.  “This will lead to dramatic differences in performance between the more and less analytical health insurers.

Some will go out of business, creating disruption for the entire industry and its consumers,” Davenport predicts.  “If there is a “public option” that takes consumers no one else wants, it will undoubtedly get the citizens who are most likely to acquire expensive diseases.  Taxpayers will foot the bill, while the private health insurers who are good at prediction will become much richer.”

Auto insurers, for instance, have used “risk adjustment” in many states, sharing losses when they arise and preventing companies from skimming only the most desirable clients.  Another view comes from the public radio program “Marketplace” which found healthcare outcomes showed worse care or repeat hospitalization for minority patients compared with native English speakers.  A Nov. 23 report explored ways hospitals and insurers are looking to better data for improving predictive, responsive information.  For example, cultural details identifying someone more specifically as Cambodian, Indian or Japanese instead of more generically as “Asian” can make a difference when communicating care or prescription drug use.

David Wallace
Spotfire Blogging Team

Image Credit: Microsoft Office Clip Art

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12/07
2009

Business Intelligence: Its Progression into Financial Services Analytics

j0442414 150x150 Business Intelligence: Its Progression into Financial Services AnalyticsJay Morreale, first vice president of enterprise credit and market risk technology for Bank of America has a wonderful account of the genesis of business intelligence and its progression into true analytics for the financial services markets in an article entitled From BI to Analytics: An Evolution In How Business Gets Done.  Jay’s contention is that vendors are using Business Intelligence and Analytics interchangeably and that “It’s clear there is a widening gulf between what traditional BI tools deliver and the emerging market for true analysis capabilities.”

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12/04
2009

Business Intelligence Can Stop The Spread of “Spread-Mart”

j03879531 150x150 Business Intelligence Can Stop The Spread of “Spread Mart”It’s a corporate equivalent of “when all you have is a hammer, everything looks like a nail.”  If a spreadsheet is the only tool you have for analytics, chances are good you won’t produce any business intelligence.  E-mailed spreadsheets multiply like, well, Excel formulas as the documents and attachments move throughout your network.  They can turn any organization into a SpreadMart with lots of data but no knowledge or insights that let you plan for the future.

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12/03
2009

Forget Massive Business Intelligence Projects

j0439587 150x150 Forget Massive Business Intelligence ProjectsWe recently interviewed BPM Partners’ John Colbert, who talked about five common attributes of failed business intelligence projects.  One of these attributes is having a very large scope for the business intelligence project.

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12/02
2009

Christmas Shopping, Business Intelligence and Your Privacy

j0442188 150x150 Christmas Shopping, Business Intelligence and Your PrivacyAh Christmas.  A time for reflection, thanks, family, and shopping.  As we get ready to watch the smoke billow off of our credit cards, how many of us really consider our privacy as part of the transaction?  And by privacy here we mean from the notion of how much a vendor knows about us, our buying patterns, buying preferences, selection criteria, and price sensitivity. Generally speaking, these are not the top of mind concepts that the average consumer considers when thinking of purchasing security.

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12/01
2009

Is Your Company Smart Enough? Business Intelligence From Customers Who Don’t Buy Anything

j0402015 150x150 Is Your Company Smart Enough? Business Intelligence From Customers Who Don’t Buy Anything Business Intelligence in the 21st century enterprise has as much to do with what your company “knows” as what it makes, sells or buys.   Companies like Priceline, Amazon and SmartBargains are just a few giants that have compiled huge databases of consumer preferences, pricing, packages of multiple purchases.  That data has tremendous value – even if they never sold another item.  And thanks to analytics from the streams of customer Web traffic, they can learn as much from tire-kickers who never purchase as from their most frequent buyers.

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