of the overall architecture of the enterprise.■ 7.4 VISUAL SUMMARY
Synopsis - In this chapter, I will give a high-level overview of the distinction between five different types of data: transaction data, master data, business intelligence data, reference data, and metadata. For each, I will also provide links to other chapters.
■ 8.1 CLASSIFYING DATA
Most organizations have large amounts of data. This is a well-known fact and one of the reasons why DM is such an important topic. What’s more, they typically also have many different types of data. Classifying data can be useful for different purposes. For example, it may help to decide on the approach to DM, or to decide what type of media it should be stored on. Many different classification schemes have been proposed. This is illustrated in example 16.
Example 16. Data classification
Data can be classified to indicate the type of use: descriptive data (describe a state of affairs in the real-world), diagnostic data (show how well something – e.g. a process – is functioning), predictive data (make predictions about a future state of affairs), or prescriptive data (define parameters to ensure that a certain process or system performs as desired).
Another way to classify data is to consider what it describes: i.e. geographic data (what a specific area looks like), weather data (past/ present/ future weather for a specific area), and people data (such as names, addresses, and relationships to other people).
While useful, these types of classifications are not the main topic of this chapter. Instead, I will look a level deeper and consider five related types of data. I already hinted at these in table 7.1 where I gave an overview of the DM topics that I will discuss in this book.
■ 8.2 FIVE FUNDAMENTALLY DIFFERENT TYPES OF DATA
In this section, I will give a high-level overview of five fundamentally different data types and indicate in which chapter I will discuss these further. The point is not so much to give an extensive discussion here but to make the reader aware that there are different types of data before launching into detailed discussions about governance, architecture, etc. in future chapters. Figure 8.1 outlines the five types of data.
Figure 8.1 Five types of data
■ 8.3 TRANSACTION DATA
The first type of data is transaction data. This type of data usually provides a description of some event that took place in the real-world, such as a purchase, a payment of an invoice. Assuming business goes well, you will typically have many records of this type that are created every day: every time someone makes a purchase or payment, for example. Also note that these records tend to be highly structured and you want to keep track of all of them so that you can later analyze how business is really going.
■ 8.4 MASTER DATA
The second type of data is master data. To understand what this is about, consider a situation where you have half a dozen systems where you store data about your customers. One of your customers calls with a complaint. In which system are you going to look to find out what is going on? Even more, how are you going to deal with the situation where systems are in disagreement (one system says this customer has his office in Amsterdam, whereas the other claims it is in Rotterdam)?
To tackle challenges of this type, organizations typically want to organize a “golden record” or “single version of the truth” which must show what the organization believes to be true. There are many ways to implement this as we will see in chapter 15. This is both complex and costly and organizations typically only do this for their most important business concepts, such as Party / Customer, and Product. Typically, this type of data does not change all that often (ask yourself this: how often do people move or change their name? How often do you introduce new/ retire old products?). Example 17 shows that transaction data may also contain (references to) master data objects.
Example 17. Master data & transaction data
Suppose that you have just sold a product called Cool8 to a customer whose name is John Doe. The record of this transaction will show such things as a time stamp, the actual store where the purchase was made, which employee was involved and so on.
From a master data perspective, two business concepts are of interest: the customer and the product. This customer may have made previous purchases at this store, or perhaps at other stores. If this customer purchases a lot of our Cool8 product then this may be useful to know. If this customer used to purchase Cool7 and has now switched to Cool8 then it may also be useful to find out why and what that implies for future sales.
Now, suppose that John Doe did, in fact, make purchases at various stores but
