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Oracle 11g Database Management for Business Intelligence

Oracle 11g Tips by Burleson Consulting

Database management for business intelligence systems

The use of consumer data for market analysis has been used since ancient times when the Mesopotamians sold shipments of olive oil and other commodities to the Ancient Grecian empire.  While the foundations of the data storage have changed dramatically from Mesopotamian clay tablets to today's modern database management systems, the goals of business intelligence and data mining remain unchanged.

The basic tenet of business intelligence is that one can predict the future by analyzing the past, and by grouping together related groups of consumers, you can develop highly sophisticated and accurate predictive models that can save billions of dollars a year in advertising expenses.  At the same time, consumers are provided with targeted marketing which is most appropriate to their needs.

Business intelligence is not limited exclusively to the area of marketing and sales. Hospitals group patients together in terms of their age and symptoms (a "cohort"), and analyze treatment regimens in order to determine the best course of treatment for their specific patient populations. 

Even though the use of business intelligence saves lives, BI technology has broader social implications.  First and foremost is the issue of data privacy.  As consumer monitoring becomes more and more ubiquitous (note how your purchasing behavior is controlled at super markets via your buyers club card), we see that many privacy advocates do not want even our most innocuous behaviors recorded. 

Fortunately, most consumers don't care whether you prefer peas to string beans and they allow point of sale systems to readily track purchases.  Via the use of buyers club cards, the BI expert ties individual purchases to background demographic information. When consumers apply for buyers club cards, they provide basic demographic information which is in turn analyzed with publicly available information on major life events and income (such as the purchase of a house, a divorce, the presence or absence of children).  Hence, the database has detailed information not only about what products are being purchased, but the basic demographics of the person who is purchasing the goods or services. 

The issue of data storage has always been important to business intelligence because of the dynamics of changing technology.  Disk prices are falling radically each and every year.  Back in the 1980's, 1.2 gigabytes of disk storage could cost a whopping $200, 000 whereas today you can purchase the same amount of disk for less than $100. 

Given our ability to store large amounts of empirical information cheaply, the goal of the business intelligence manager is to somehow be able to cleanse and manipulate this data in such a way that accurate predictive models can be built.

Let's take a closer look at the evolution of business intelligence from the perspective of the database manager, and explore how the database influences the manipulation of these vast quantities of observable data in the real world.

Data as a predictive tool

As with previously noted, people have been analyzing data for centuries in the attempt to predict consumers' future behavior, as well as the behavior of other important tasks such as medical treatment programs.  The statistical methods for analyzing predictive data have been with us for centuries, and data mining analysis allows us to predict, with relative certainty, the internal mechanisms and behaviors of groups of people in the general public.  For an interesting exploration of this concept, see the book Super Crunchers by professor Ian Ayres of Yale University. 

In his book Super Crunchers, Dr. Ayres shows how data is often replacing human intuition in many areas of business intelligence.  Today, we know the top CIO's and CEO's of large corporations can earn hundreds of millions of dollars a year, largely for their human intuition.  It's been largely recognized that computers can only take care of the well structured part of any decision making task.  We generally find that these types of information systems fall into different categories:

  • Expert systems - Expert systems are systems that quantify the well structured component of a decision task and make recommendations without the input of a human expert.  These systems are typified by MYCIN, a predictive tool that quantifies the questions asked when diagnosing specific blood illnesses.  The same approach can be applied to just about every area of business management, including the database management system itself.  In the early twenty first century, Oracle database administrators can use tools such as Oracle data mining to filter through their database metadata and performance data (using Oracle's automated workload repository), and predict in advance resource consumption trends within the database management system.

  • Decision support systems (DSS) - Decisions support systems are systems where it is recognized that human intuition is an essential component of the decision making process; and DSS technology makes no claims to actually solving the problem.  Rather, a decision support system provides the decision maker with information from their problem domain and leaves the actual decision process to the human expert.  This is an important concept within information systems. 

It is interesting to note that many systems which were first thought to be decision support systems turn out to be expert systems.  In one notable case, a major soup manufacturer was about to loose a long-term employee of forty years, who knew every intricacy of the tricky soup vats within the company. 

Initially setting out to create a DSS, the decision analyst quizzed the employee over a period of months and discovered that what was once thought to be intuition was actually the application of a large set of well structured decision rules.  When this soup vat expert would say something like "I have a feeling that the problem is X", it appeared to be human intuition to those less knowledgeable observers. 

However in reality it was the application of a long forgotten decision rule or an experiential case for which the individual had since lost conscious knowledge.  The application of the decision support system technology eventually led to an expert system.  This allowed the forty year worker to retire comfortably, with the knowledge that all of his years of decision rules had in fact been quantified, helping the soup company carry on without him making even faster and better decisions as a whole.

The application of business intelligence for predictive models

The idea of data mining allows us to do far more than predict the future behavior of a consumer.  Companies such as Amazon pioneered the idea of a "recommendation engine", which analyzed patterns of behavior amongst known consumers, extrapolated them online, and made on-point recommendations for future purchases. This type of technology has also been applied to other web-type interfaces such as NetFlix and TiVo, where consumers are directed to related entertainment that people of similar interests might have in mind. 

Another good example of data mining is the role of a bank loan officer.  Traditionally, bank loan officers all have access to the same set of data, but it is undeniable that some people serve as better loan officers than others.  This could be blamed on human intuition, whereby the loan officer recognizes someone as either having a good or a bad propensity to repay the loan based on non quantifiable characteristics. 

It is largely understood now that the role of an experienced bank loan officer has more to do with the subtle nuances of the applicant; and being able to recognize them.  Hence, today's bank loan officers are largely constrained by following the computer whereby an individual borrowers is compared against a cohort (the term "cohort" is the arbitrary grouping of like minded people).

In some, the rapid falling prices of disk storage technology have now made it feasible for organizations of even a modest budget to store trillions of bytes of real time information about their business processes.  The immediate challenge is how to store, organize, and extrapolate from this information in order to make valuable business decisions. 

Let's take a closer look at the underlying database technology, and explore how today's database management systems help business intelligence experts to organize, collect, and make valid predictive models.

The foundation of database management for business intelligence

The storage of online data began in the 1960s as organizations began to develop the digital means to store information about stock prices, consumer trend behaviors, and so on.  Unfortunately, this information had to be stored on large volumes of magnetic tape, and simple decision support queries for correlations could take days, making it difficult for a manager to follow any 'flow' of a decision process.  It was only as disk storage began to become cheaper that this information was able to be brought online, so that the information could be indexed, pre-computed, and organized in such a fashion that the user of the business intelligence system could quickly get feedback on given questions.  This would stimulate new questions, and provide a platform for making more informed business decisions.

An early leader in the area of decision support systems and expert systems was SAS, the Cary North Carolina based company which has been a capstone of data analysis for more than forty years.  SAS had its own full programming language and rudimentary data storage platform, upon which statistical algorithms could be run to analyze just about any kind of information. But as today's corporations start collecting "raw" data from their observable world, several problems have to be undertaken:

  • Data cleansing - Data is only as good as the input to that system, and common keyboarding errors from individuals can skew the quality of the information. Today we recognize that all data must be cleaned, scrubbed, and standardized in order to get meaningful information from it.

  • Data summarization - In data summarization, we find the problem of pre-computing large scale aggregations from mammoth volumes of data in real time.  A simple question like "how many consumers of widgets are their in New York?", might require millions of data block I/Os, and a significant amount of computing power. Even with today's super fast computer systems and super cheap disk storage, the decision support system or expert system must be able to have this information available at the fingertips of the decision maker, which often requires pre-summarization and pre-aggregation of the salient data factors. Hence, today's database managers devote a significant amount of time to observing the decision patterns of their end user base, using tools such as Oracle materialized views, Oracle's star query joins; allowing the information to be accessible to the end user base in a real time fashion. We also see today's business intelligence applications supporting a drill down mechanism whereby they can take a look at the behavior of a cohort as a whole, then double click through to see the information at successive levels of usage.  Today, we see tools such as the Urchin software (now called Google Analytics) which allow website referrer stats to be organized in such a way that an SEO, or search engine optimization expert, can quickly drill through and see how customers are visiting their individual websites.

We also see a paradigm change on the Internet whereby referrer statistics can now measure not only the number of page viewed for an individual webpage but how long an individual actually spends on that page; a far better indicator of the actual popularity of a web page.  These types of technology are fostering a whole new way that we use information in order to make predictions.

For example, Ayres notes in his book "Super Crunchers" that he helped chose the title "Super Crunchers" by doing an empirical experiment using Google AdWords on the keywords "data mining". 

By presenting his end user community with a choice of either "Super Crunchers" or "The End of Intuition", Ayres was able to determine that Super Crunchers was a far better title using the very type of data mining technology which he espouses within his fantastic book.  But the idea of using database management systems as the foundation for business intelligence also has applications far beyond basic predictive modeling.

 Let's take a look at some of the more sophisticated uses of these trillions of bytes of corporate information, and understand how they can be used for hypothesis testing.

Hypothesis testing in business intelligence

The aircraft industry learned in the 1960s that large-scale computers could be used to simulate the flying of a new aircraft without putting pilot's lives at risk, and we are starting to see the same application of hypothesis testing being used within the business community today. Prior to launching a 100 million dollar ad campaign, the behavior of that can be simulated using sophisticated algorithms and techniques which will model the actual advertising campaign in order for the marketing executive to see what kind of an ROI (return on investment) the marketing campaign might do.

Hypothesis testing is generally a "what if" type of question, whereby the business intelligence expert can isolate individual variables within their database and manipulate them over time based on well defined preconditions. This "ceteris paribus" approach (ceteris paribus literally means "all else being equal"), allows the decision maker to keep everything except their problem domain fixed. By fixing all but a single variable, and testing it against a well known universe, the business intelligence person can develop models which are far more sophisticated than traditional predictive analysis.  For more information on this technique see Dr. Carolyn Hamm's book "Oracle Data Mining". 

The costs of business intelligence

It's often said in the information technology world that you 'can't afford not to have a data mining technique with in your organization'. It's not uncommon to hear stories of payback periods compressed into mere weeks even on data mining projects that cost tens of millions of dollars, because of the high value of the information that comes from these, and the end users savings for consumers.

The best example of this of course is within predicting consumer behavior, where organizations save hundreds of millions of dollars in broadcast advertising, replacing it instead with well-targeted advertising and a high probability of buying a specific product.  The consumers appreciate the targeted marketing, and the reduced costs allow products to be offered more cheaply; benefiting everyone. 

Let's take a close look at the shift of the costs.  Back in the 1970s the major cost of any data warehousing or any data mining operation was the hardware itself which would often comprise more than 80% of the total cost.  In the early twenty-first century we see a complete reversal of this, whereby the disk storage, while significant, are minimized by the amount of work required by both the database administrator and the business intelligence analyst. A highly skilled database administrator must be put in place in order to capture the real time data and organize it in such a fashion (using tools such as Oracle partitioning) so that the information can be more easily accessed by the statistical managers. 

Once the data has been collected, organized, and aggregates are pre-computed and summarized, the largest expense is that of the business analyst themselves.  These people must have very extensive backgrounds in multivariate statistics and understand in detail how all of the algorithms work, so that they can tear through all of this data in order to make statistically meaningful correlations between the data.  In some, the lion's share of today's costs of data business analysis are in the human resources arena, propelling an experienced data mining analyst into the realm of some of the most highly paid people within the information systems industry.

Conclusions about business intelligence in America

The Gartner Group  has predicted a large scale uptake of business intelligence within the IT arena for the years 2007-2015, largely based upon the industry's understanding as a whole that this information is quite valuable and has an extremely short payback period.  While disk costs continued to fall, data storage engines such as SAS, DB2, and Oracle provide the vehicle and platform for sophisticated business analysis. 

We can expect to see a lot more demand for people who are highly skilled in quantitative aspects of data analyses.  These data analysts may not be fluent in Oracle Database administration or the nuances of the internal data storage, but they have the statistical acumen of an actuary, a 'super cruncher' if you will, who is someone who can take terabytes of point of sale information and glean the nuggets of golden information from that.  This is certainly an emerging area of information technology, one that requires many years of studies in statistics and understanding how to present information in a meaningful way to the decision maker.

sf: DBNR



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