In order to fully understand some of the later discussions in this book, it is important to have a basic understanding of the problem that DecisionPoint was trying to solve. DecisionPoint represented a series of software and consulting processes to automate the analysis of financial information for large corporations. Overly simplified, DecisionPoint can be compared to the reporting capabilities of Quicken. In Quicken, you manage your checkbook and bank accounts to pay your bills. There is also a reporting capability that allows you to see where you’re spending your money, what categories, etc. DecisionPoint was designed to provide very similar capabilities for large corporations, but on a much bigger scale. For example, DecisionPoint could take financial information from multiple systems running in multiple countries and consolidate that information for the customer so that employees of the customer could perform complex analysis on that information in one place without having to go back to the source systems. This would, hopefully, lead to better decisions about what direction the business should go.
DecisionPoint was in what is known as the data warehousing market within the computer technology market. When you simplify the meaning, data warehousing is the ability to take very large quantities of data from many different sources, and assemble it in a central location that is fast and easy for non-technical people to access and make decisions. From a personal perspective, picture it like doing your monthly finances. You have your pay check along with your typical monthly expenditures. You use this information to decide what bills to pay, how much money is left over, and what you want to do with the left over money. Now picture that same situation for millions of people that have to make the same decisions. There are millions of people accessing millions of pieces of information with minimal knowledge of how to use a computer other than a home PC. That’s pretty much what a data warehouse is like. It involves a lot of people looking through a lot of data to try and figure out what to do next.
As an example, a data warehouse in a grocery store is something that most everyone can relate to. You go into a grocery store to buy a lot of different products including food, beverages, bathroom supplies, and many other items that you use in your home. Each time you go to the grocery store, they scan the products that you intend to purchase. When you initially look at the scanning process, you fundamentally believe that the primary purpose of scanning is to help determine how much a product costs, and therefore, how much you pay for it. While this is the fundamental purpose, there are other things that go on behind the scenes that you never see. In addition to scanning products for price, the grocery store also tries to track the frequency at which products are purchased, and what products are typically purchased together. For example, in the summer, you may find that not only do people purchase a lot of steaks for grilling, but when they do so, they also buy other supplies such as steak sauce, meat tenderizer, even things like charcoal for the grill. The goal of knowing this information is two fold. First, the grocery store wants to track what items are purchased frequently at what time of year. This helps them to determine what products and quantities of those products they need to have in the store to make sure they have enough supply for everyone that comes to buy those products. Second, the grocery store also knows what products are typically purchased at the same time. This may lead them to place the items closer together in the store to make it easier for shoppers to find. In our steak example, a store might choose to put the steak sauce, and even possibly the charcoal near the butcher section making it easier for shoppers to find the products most closely related to steak and how people choose to cook it.
Now you may ask what this has to do with data warehousing. As you might notice when you go to the grocery store at a busy time, there may be hundreds of shoppers in the store. Additionally, there are literally thousands of products that those shoppers can buy. In any given day, a grocery store may scan literally millions of product transactions. Imagine having to sort through the scanned data by hand trying to determine the most frequently purchased products along with trying to correlate what products are commonly purchased at the same time. This is an exercise that could take weeks to do by hand for just one day’s volume of transactions. The role of the data warehouse is to use a computer to take all of those scanned transactions, and organize them in a way that the manager of the grocery store can quickly determine, with the press of a button and in a matter of seconds, the most frequently purchased products for a day, week, month, season, or year. So, literally in seconds, the grocery store manager knows what products to order for what time of year. Additionally, with the press of a button, the store manager can correlate what products are frequently sold together, which may help them to determine where to place products on the shelves to make it easier for shoppers to find them.
Fundamentally, the goal for the data warehouse for a grocery store is to sort through what has been bought, and allow the manager to ask both basic and complex questions to help them more effectively manage the store with the goal of selling more products to more people. A data warehouse can apply to more than just a grocery store.
Some other examples of the usage of data warehousing to enhance business opportunities include:
• Cell phone companies analyzing call and call volume data to determine new
and different programs they can offer to their customers
• Manufacturing companies analyzing what they buy and who they buy it from to
determine if there are any products or vendors they should negotiate to
obtain discounts on future purchases.
• Retailers that analyze product purchases to be able to advertise and send
coupons to offer discounts to customers on frequently purchased items.
• Companies that collect and analyze their travel expenses to determine where
employees are traveling and what it is costing the company. This can help
to determine most frequently used airlines, hotels, car rental companies,
etc. so that the company can negotiate volume discount with those vendors.
There are many other situations where a data warehouse can be used, and these are just some cursory examples to help you see the possibilities when using this type of technology.
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