Data normalization is a process in which data attributes within a data model are organized to increase the
cohesion of entity types. In other words, the goal of data normalization is to reduce and even eliminate data
redundancy, an important consideration for application developers because it is incredibly difficult to stores
objects in a relational database that maintains the same information in several places.
This article is organized into the following topics:
- Why Data Normalization?
- The Steps of Data Normalization
1. Why Data Normalization?
There are two primary advantages of having a highly normalized data schema:
- Increased consistency. Information is stored in one place and one place only, reducing the
possibility of inconsistent data.
- Easier object-to-data mapping. Highly-normalized data schemas in general are closer conceptually
to object-oriented schemas because the object-oriented goals of promoting high cohesion and loose
coupling between classes results in similar solutions (at least from a data point of view).
You typically want to have highly normalized operational data stores (ODSs) and data warehouses (DWs).
The primary disadvantage of normalization is slower reporting performance. You will want to have a denormalized schema to support reporting, particularly in data marts.
2. The Steps of Data Normalization
Table 1 summarizes the three most common forms of normalization (
First normal form (1NF),
Second normal form (2NF), and Third normal form (3NF)) describing how to
put entity types into a series of increasing levels of normalization. Higher levels of data normalization are
beyond the scope of this article. With respect to terminology, a data schema is considered to be at the level of
normalization of its least normalized entity type. For example, if all of your entity types are at second normal
form (2NF) or higher then we say that your data schema is at 2NF.
Table 1. Data Normalization Rules.
2.1. First Normal Form (1NF)
Let's consider an example. An entity type is in first normal form (1NF) when it contains no repeating groups of
data. For example, in
Figure 1 you see that there are several repeating attributes in the data
table - the ordered item information repeats nine times and the contact information is repeated twice, once for
shipping information and once for billing information. Although this initial version of orders could work, what
happens when an order has more than nine order items? Do you create additional order records for them? What
about the vast majority of orders that only have one or two items? Do we really want to waste all that storage
space in the database for the empty fields? Likely not. Furthermore, do you want to write the code required to
process the nine copies of item information, even if it is only to marshal it back and forth between the
appropriate number of objects. Once again, likely not.
Figure 1. An Initial Data Schema for Order (UML Notation).
Figure 2 presents a reworked data schema where the order schema is put in
first normal form. The introduction of the OrderItem1NF
table enables us to have as many, or as few, order items associated with an order, increasing the flexibility of
our schema while reducing storage requirements for small orders (the majority of our business). The
ContactInformation1NF table offers a similar benefit, when an order is shipped and billed to the same person
(once again the majority of cases) we could use the same contact information record in the database to reduce
OrderPayment1NF was introduced to enable customers to make several payments against an order -
could accept up to two payments, the type being something like "MC" and the description "MasterCard Payment",
although with the new approach far more than two payments could be supported, potentially one per payment type.
Multiple payments are accepted only when the total of an order is large enough that a customer must pay via more
than one approach, perhaps paying some by check and some by credit card.
Figure 2. An Order Data Schema in 1NF (UML Notation).
An important thing to notice is the application of primary and foreign keys in the new solution. Order1NF
has kept OrderID, the original key of Order0NF, as its primary key. To maintain the relationship back to
Order1NF, the OrderItem1NF table includes the OrderID
column within its schema, which is why it has the
stereotype of FK. When a new table is introduced into a schema, in this case OrderItem1NF, as the
result of first normalization efforts it is common to use the primary key of the original table (Order0NF
) as part of the primary key of the new table. Because OrderID
is not unique for order items, you can have several order items on an order, the column ItemNumber (which
is unique to a type of item) was used to form a composite primary key for the OrderItem1NF
table. A different approach to keys was taken with the ContactInformation1NF
table. The column ContactID, a surrogate key that has no business meaning, was made the primary key.
2.2. Second Normal Form (2NF)
Although the solution presented in
Figure 2 is improved over that of Figure 1, it can
be normalized further.
Figure 3 presents the data schema of Figure 2 in
second normal form (2NF). an entity type is in second normal form (2NF) when it is in 1NF and when every non-key
attribute, any attribute that is not part of the primary key, is fully dependent on the primary key. This was
definitely not the case with the OrderItem1NF table, therefore we need to introduce the new table
Item2NF. The problem with OrderItem1NF
is that item information, such as the name and price of an item, do not depend upon an order for that item. For
example, if Hal Jordan orders three widgets and Oliver Queen orders five widgets, the facts that the item is
called a "widget" and that the unit price is $19.95 is constant. This information depends on the concept of an
item, not the concept of an order for an item, and therefore should not be stored in the order items table -
therefore the Item2NF table was introduced. OrderItem2NF
retained the TotalPriceExtended
column, a calculated value that is the number of items ordered multiplied by the price of the item. The value of
SubtotalBeforeTax column within the Order2NF
table is the total of the values of the total price extended for each of its order items.
Figure 3. An Order in 2NF (UML Notation).
2.3. Third Normal Form (3NF)
An entity type is in third normal form (3NF) when it is in 2NF and when all of its attributes are
directly dependent on the primary key. A better way to word this rule might be that the attributes of an entity
type must depend on all portions of the primary key. In this case there is a problem with the OrderPayment2NF
table, the payment type description (such as "Mastercard" or "Check") depends only on the payment type, not on
the combination of the order id and the payment type. To resolve this problem the PaymentType3NF table
was introduced in Figure 4, containing a description of the payment type as well
as a unique identifier for each payment type.
Figure 4. An Order in 3NF(UML Notation).
The data schema of Figure 4
can still be improved upon, at least from the point of view of data redundancy, by removing attributes that can
be calculated/derived from other ones. In this case we could remove the SubtotalBeforeTax
column within the Order3NF table and the TotalPriceExtended column of OrderItem3NF, as you
see in Figure 5.
Figure 5. An OrderWithout Calculated Values (UML Notation).
From a purist point of view you want to normalize your data structures as much as possible, but from a practical
point of view you will find that you need to 'back out" of some of your normalizations for performance reasons.
This is called "denormalization". For example, with the data schema of Figure 1
all the data for a single order is stored in one row (assuming orders of up to nine order items), making it very
easy to access. With the data schema of Figure 1 you could quickly determine the
total amount of an order by reading the single row from the Order0NF table. To do so with the data schema
of Figure 5 you would need to read data from a row in the Order
table, data from all the rows from the OrderItem
table for that order and data from the corresponding rows in the Item
table for each order item. For this query, the data schema of Figure 1 very
likely provides better performance.
I'd like to thank Jon Heggland and Nebojsa Trninic for their thoughtful review and feedback. They found
several bugs which had gotten by both myself and my tech reviewers.