So, what is householding and how would you define it? Simply put, householding consists of grouping like data from numerous sources. This can be identifying a set of data records from one source system, such as order entry, and how they are related to another set of data records.
The concept of householding your data is very straightforward. Imagine a specific item of data needs to be in a specified household for that type of data. Just like you have a home address, so should the data item. But, just like you, you have a home address, work address and addresses of relatives you spend time with. All of these addresses are your households. The data item should be considered in the same way and it can reside in other households also. This logic gives your data flexibility and true meaning. Let’s start with discussing some tips on “householding” your data:
- Identify and eliminate duplicate records. This step is crucial to your success.
- Determine the level of accuracy of your records. This can be increased, thus directly related to your data quality and the sophistication of your matching rules.
- Determine what makes up a group or household. The quality will vary depending on the grouping rules and quality of the data.
- Determine grouping rules. The level of accuracy achievable when grouping common records will also vary based on the sophistication of the grouping rules and the quality of your data.
- Consider data confidence factors. The assumption is that every item of data used in the householding process may be invalid.
As mentioned, business rules must be set in order to guide the householding routines. In this example below of householding with a 100% business match or no match, the following two records would not be considered a match:
Bob Smith 555 Main St. Anytown, CA 12345-1234
Bobby Smith 555 Main St. Anytown, CA 12345-1234
However, a rule of last name and address must match or no match would indicate the above records are the same.
Having these rules defined and the ability in place to verify, validate and use the grouped information is the key to successful household logic. Companies are spending millions on data management to ensure their ROI is strong and their fiscal performance is positive. As consumers continue to depend on mobile platforms and the internet for their spending decisions, business must ensure that data is strategically handled.