Microsoft Office Tutorials and References
In Depth Information
Chances are you’ve seen the recommendations pages on sites such as Netflix, Wal-Mart, and Amazon. On Netflix, for
example, you can choose your favorite genre, and then select movies to order or watch online. Next time you log in,
you’ll see a “Movies you’ll love” section with several suggestions based on your previous choices. Clearly, there’s some
kind of intelligence-based system running behind these recommendations.
Now, don’t worry about what technologies the Netflix web application is built on. Let’s just try to analyze
what’s going on behind the scenes. First, because there are recommendations, there must be some kind of tracking
mechanism for your likes and dislikes based on your choices or the ratings you provide. Second, recommendations
might be based on other users’ average ratings minus yours for a given genre. Each user provides enough information
to let Netflix drill down, aggregate, and otherwise analyze different scenarios. This analysis can be simple or complex,
depending on many other factors, including total number of users, movies watched, genre, ratings, and so on—with
Now consider a related but different example—your own online banking information. The account information
in your profile is presented in various charts on various timelines, and so forth, and you can use tools to add or alter
information to see how your portfolio might look in the future.
So think along the same lines, but this time about a big organization with millions of records that can be
explored to give CIOs or CFOs a picture of their company’s assets, revenues, sales, and so forth. It doesn’t matter if the
organization is financial, medical, technical, or whatever, or what the details of the information are. There’s no limit to
how data can be drilled down into and understood. In the end, it boils down to one thing—using business intelligence
to enable effective decision making.
Let’s get started on our explorations of the basics and building blocks of business intelligence.
Just about any kind of business will benefit from having appropriate, accurate, and up-to-date information to make
key decisions. The question is, how do you get this information when the data is tightly coupled with business—and is
continually in use? In general, you need to think about questions such as the following:
How can you drill down into tons of information, aggregate that information, and perform
mathematical calculations to analyze it?
How can you use such information to understand what’s happened in the past as well as
what’s happening now, and thereby build better solutions for the future?
Here are some typical and more specific business-related questions you might have to answer:
What are the newly created accounts this month?
Which new users joined this quarter?
Which accounts have been removed this year?
How many vehicles have we sold this year, and what’s the new inventory?
How many issues have been addressed this week in the command center?
What is the failure rate of products in this unit?
What are the all-time top 10 stocks?
Can you rank these employees in terms of monthly sales?
Is it possible to run statistical analysis on existing data?
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