Extracting Data to Staging Area

Data is first extracted from the source system and placed in a staging area. This staging area is typically formatted like the source system. Keeping data in the same format as the source makes the first extract simple and avoids bogging the source system down.

You most likely will want to process only changed data, to avoid the overhead of reprocessing the entire set of data. This could be done by extracting data based on date/time information on the source system, mining change logs or by examining the data to determine what changed.

• Tip 1: Make sure the source system date/time information is consistently available. Use data profiling to validate.
• Tip 2: Store a copy of the prior version of data in the staging area so that it can be compared to the current version to determine what changed.
• Tip 3: Calculate check sums for both current and prior versions, then compare check sums rather than multiple columns. This speeds up processing.
• Tip 4: Add a source system prefix to table names in the staging area. This helps to keep data logically segregated.

Applying Data Transformations

Data is now ready for transformation which includes cleansing, rationalization and enrichment.  The cleansing process, sometimes called "scrubbing" removes errors while rationalization removes duplicates and standardizes data.  The enrichment process adds data.

Before starting data transformation efforts it is important to diagnose and understand problems.  See the Data Profiling topic in the article Data Sources for Data Warehousing and Business Intelligence for guidance.  This article assumes that data errors that could be cleaned and / or prevented at the source have already been cleaned or corrected.

These processes may take place in tables dedicated to transformation or may take place "on the fly" as data is moved from staging to data warehouse or from data warehouse to data mart.

Tools have been developed to scrub and standardize party information like SSN, names, addresses, telephone numbers and email addresses.  This software can also remove or merge duplicate information ("de-duping").

Techniques available include:

• Audit
• Correct At Source
• Specialized Software (Address Correction Software)
• Substituting Codes and Values

Missing, Incomplete and Wrongly Formatted Data

Common problems that may require correction are missing data, incomplete data and wrongly formatted data.  In the case of missing data, a complete column such as zip code or first name is empty.  A tool could correct the zip code based on look up of address lines, city and state.  Incomplete data is partially missing such as the case where an address constains the name of a street without the building number.  Tools are available that can correct some of these problems.  Finally, data may be in the wrong format.  We may want telephone numbers to contain hyphens.  A tool could consistently format telephone numbers.

Applying Data Consistency Transformations

Consistent data is important for "apples to apples" comparisons.  For example, all weight measures could be converted to grams or all currency values to dollars. Transformation could be used to make code values consistent such as:

• Gender ("M", "F") or ("y", "n")
• Boolean ("Y", "N") or (1, 0)

More Data Cleansing Issues

Loading the Data Warehouse

The data warehouse is a mix of atomic and dimensional data.  The atomic portion is stored in a normalized, relational format.  Data stored in this format can be repackaged in a number of ways for ease of access when moved to the data mart.

Positioned for Direct Load to Data Warehouse by Utility

• Benefits:
• Very Flexible
• Reduces Contention and Load Time for Data Warehouse

Loading the Data Mart

Loading the data mart through efficient and effective methods is the subject of this article.  When loading the data mart, dimensions are loaded first and facts are loaded second.  Dimensions are loaded first so that the primary keys of the dimensions are known and can be added to the facts.

Make sure that the following prerequisites are in place:

• Data is stored in the data warehouse and ready to load in the data mart
• Data maps have been created for movement from data warehouse to data mart
• Grain is determined for each dimension and fact

Loading Data Mart Dimensions

There are specific prerequisites that must be in place for dimensions:

• Dimensions have surrogate primary keys
• Dimensions have natural keys
• Dimensions have needed descriptive, non-key attributes
• Maintenance strategy is determined for each dimension:
• • Slowly Changing Dimension (SCD) Type 1: Overwrite
  • SCD Type 2: Insert new row - partitions history
  • SCD Type 3: Columns in changed dimension contain prior data

Some dimensions are loaded one time at the beginning of the data mart project such as:

• Calendar Date
• Calendar Month
• US State
• US Zip Code

 Loading Data Mart Facts

 Data mart facts consist of 3 types of columns:

• Primary key
• Dimensional keys
• Measurements

In the data warehouse, there will be natural keys that can be joined with dimensions to obtain dimensional keys.  For example: