1.
Retail Sales
Kimball & Ross, Chapter 2

2.
Overview
 Four-step dimensional design process
 Transaction-level fact tables
 Additive and non-additive facts
 Sample dimension table attributes
 Causal dimensions
 Degenerate dimensions
 Extending an existing dimension model
 Snowflaking dimension attributes
 Avoiding the “too many dimensions” trap
 Surrogate keys

3.
Four-Step Dimensional Design Process
1. Select the business process to model.
 not business department or function
 E.g., purchasing, ordering, shipping, invoicing,
inventorying
2. Declare the grain of the business
process.
 Specifies individual fact table row
 E.g., individual line item on sales ticket, daily
snapshot of the inventory levels for a product

4.
Four-Step Dimensional Design Process
3. Choose the dimensions that apply for each fact
table row.
 Q: How do business people describe the data that
results from the business process?
 E.g., date, product, store, customer, transaction
type
4. Identify the numeric (measured) facts that will
populate each fact table row.
 Q: What are we measuring?
 Typical facts are numeric additive figures
 E.g., quantity ordered, dollar cost amount
 In making decisions regarding the 4 steps,
consider both the user requirements as well as
the realities of the source data

5.
Retail Case Study
 Large grocery chain: 100 grocery stores over 5
regions
 Each store:
 Departments: grocery, frozen foods, dairy, meat,
produce, bakery, floral, health/beauty aids, etc.
 60,000 products (SKUs = stock keeping units) on
shelves
 55,000 SKUs with UPCs
 5,000 SKUs without UPCs but with assigned SKU
numbers
 Data is collected:
 from cash registers into a point-of-sale (POS)
system
 at back door where vendors make deliveries

6.
Retail Case Study – Cont’d
 Management concerns
 Logistics of ordering, stocking, and selling
products
 Maximizing profit
 Product pricing
 Lowering cost of acquisition and overhead
 Use of promotions to increase sales
 temporary price reductions
 newspaper ads
 grocery store displays
 coupons

7.
Step 1. Select the Business Process
 Decide what business process to model, by
combining an understanding of the business
requirements with an understanding of data
realities.
 The first dimensional model built should be the
one
 with the most impact,
 that answers the most pressing business questions,
 is readily accessible for data extraction.
 In retail case study: POS retail sales
 Business Question: What products are selling in
which stores on what days and under what
promotional conditions?

8.
Step 2. Declare the Grain
 What level of data detail should be made
available in the dimensional model?
 Choose the most atomic information
captured by the business process.
 Atomic data
 Most detailed, cannot be subdivided
 Facilitates ad hoc, unexpected usage and
ability to drill down to details
 Case study grain: individual line item on
a POS transaction

9.
Step 3. Choose the Dimensions
 A careful grain statement determines the
primary dimensions.
 It is then usually possible to add
additional dimensions.
 If an additional desired dimension violates
the grain by causing additional fact rows
to be generated, then the grain statement
must be revised to accommodate this
dimension.
 Case study dimensions: date, product,
store, promotion

10.
Preliminary Retail Sales Schema
 POS Sales Transaction Fact
 Date Key (FK)
 Product Key (FK)
 Store Key (FK)
 Promotion Key (FK)
 POS Transaction Number
 Other facts TBD
 Product Dimension
 Product Key (PK)
 Product attributes TBD
 Promotion Dimension
 Promotion Key (PK)
 Promotion attributes TBD
 Date Dimension
 Date Key (PK)
 Date attributes TBD
 Store Dimension
 Store Key (PK)
 Store attributes TBD

11.
Step 4. Identify the Facts
 Picking the business measurements for the fact
table: true to the grain.
 Case study - Facts collected by POS system:
 Sales quantity, sales price/unit, sales $ amount,
standard cost $ amount
 Gross Profit = cost – sales
 Recommendation: Include in fact table even though
it can be calculated. Eliminates the possibility of
user error.
 For non-additive measurements such as
percentages and ratios (e.g., gross margin) store
the numerator (gross profit) and denominator ($
revenue) in the fact table. The ratio can be
calculated in a data access tool for any slice of the
fact table. Caution: Calculate the ratio of the
sums, not the sum of the ratios

12.
Date Dimension
 Ubiquitous in every data mart
 See Figure 2.4, p. 39
 Use verbose, self-explanatory values rather than
coded values. They are used as column headers
in reports. By decoding in the database, we
ensure consistency across different application
environments.
 E.g., Holiday Indicator – use values: Holiday,
Nonholiday; as opposed to Y/N
 Date Key should be an integer rather than a date
data type
 Data warehouses need an explicit date dimension
table to describe fiscal periods, seasons, holidays,
weekends, and other calendar calculations that
are not supported by the SQL date function.
 If transaction time is of interest, we may need a
separate Time Dimension table

13.
Product Dimension
 Describes every SKU in the store
 Fill this dimension with as many descriptive
attributes as possible.
 “Robust dimension attributes deliver robust
analytic slicing and dicing capabilities.”
 Hierarchies = groups of attributes
 Merchandise hierarchy
 SKUs roll up to brands to categories to
departments.
 Each is a many-to-one relationship
 Although there will be redundancy, no need to
normalize. Given the relative size of the
dimension (as compared to the fact table) space
saving is minimal.

14.
Store Dimension
 The store dimension: Store Key
(PK), Store Name, Store Number
(Natural Key), Store Address, …
 Possible to represent multiple
hierarchies in a dimension table
 Store to any geographic attribute (e.g.,
ZIP, county, state)
 Store to store district to region

15.
Promotion Dimension
 Describes the promotion conditions under which a
product is sold
 Called a “causal dimension” – describes factors
thought to cause a change in product sales (price
reductions, ads, displays, coupons)
 Could keep all 4 causal mechanisms in a single
dimension
 They are highly correlated, so not much difference in
space requirements
 More efficient browsing for finding out how various
promotions are used together
 … or split into 4 separate dimensions
 May be more understandable to business
 Administration may be more straightforward
 To avoid null keys in the fact table (violation of
referential integrity), for line items not being
promoted include a row in the promotion dimension
to indicate “No Promotion in Effect”

16.
Factless Fact Table
 Q: Which products were under promotion but did
not sell?
 Cannot answer yet. POS sales fact table has only
products that were sold
 Answer: Create Promotion Coverage Factless Fact
Table
 Factless Fact Table = has no measurement metrics
 Contains date, product, store, and promotion keys
 Two-step process to answer Q:
 Query Promotion Coverage table: products under
promotion on given date
 From POS Sales Fact table: products sold
 Answer is the set difference of above

17.
Degenerate Dimension (DD)
 Dimension keys used in fact table without
corresponding dimension tables
 In case study: POS Transaction #
 Still useful for grouping by transaction
 Common DDs: order numbers, invoice
numbers
 Fact table primary key: Product Key and
POS Transaction Number

18.
Retail Schema Extensibility
 Original schema extends gracefully
because POS transaction data was
modeled at its most granular level.
 Premature aggregation limits ability to
extend if new dimensions do not apply to
higher grain
 Case study new dimensions:
 Frequent Shopper
 Clerk
 Time of Day

19.
Schema Extensibility
 Dimensional models can handle extensions without
invalidating existing applications:
 New dimension attributes – simply add columns
to dimension table. If new attribute is only available
after point in time, populate old dimension records
with something like “Not Available”
 New dimensions – add foreign field keys to fact
table
 New measured facts – add to fact table. If not at
the same grain, then need separate fact table
 Dimension becoming more granular – create
new dimension. May imply more granular fact table,
in which case, may have to rebuild the fact table.
 Addition of a completely new data source
involving existing and new dimensions – usually
needs new fact table

20.
Resisting Dimension Normalization
 Snowflaking = Dimension table normalization
 Redundant attributes are removed from the denormalized
dimension table and are placed in normalized secondary
dimension tables
 Fully snowflaked schema = 3NF ER diagram
 The dimension tables must not be normalized, and should
remain as flat tables.
 Numerous tables and joins usually translate into slower
query performance.
 Efforts to normalize any of the tables in a dimensional
database solely in order to save disk space are a waste of
time. Disk space savings gained by normalizing the
dimension tables are typically less than one percent of
the total disk space needed for the overall schema.
 Normalized dimension tables destroy the ability to browse
within a dimension or across dimensions (e.g., list
package types for each brand in a category). SQL needed
becomes too complex.
 The fact table is naturally normalized.

21.
Too Many Dimensions
 Too many dimensions increase space
requirements for the fact table.
 A very large number of dimensions
typically means that several dimensions
are not completely independent and
should be combined.
 A single hierarchy should not be captured
in separate dimensions.

22.
Surrogate Keys
 Surrogate keys are integers assigned sequentially as
needed to populate a dimension. They serve to join
dimension tables to the fact table.
 Avoid embedding intelligence in the data warehouse
keys.
 Benefits:
 Surrogate keys buffer the DW environment from
operational changes. What happens when operations
decide to recycle account numbers after some period of
inactivity? Fine for operational systems, but problematic
for DW if it is using account numbers as a PK.
 Can more easily integrate data from multiple operational
systems, even if they lack consistent source keys.
 Performance advantages because small size of surrogate
keys leads to smaller fact tables
 Surrogate keys are used to support one of the primary
techniques for handling changes in dimension table
attributes (Chapter 4).

23.
Acknowledgements
• Ralph Kimball & Margy Ross