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Data Quality Management for SMEs - Part Five

By Kiran Gill

December 1, 2011 | 3 min read

Here are seven simple steps that will help in the extension of data life and will also assist in helping data retain its worth:

1. Data Sources: All data sources must follow stringent protocols of data capture, processing and transfer. The organisation needs to ensure that all data arrives in a fit state (following above capture rules) for storage in a single database.

2. Regular De-duplication: Processes need to be established and automated (to reduce human error) to identify duplicates and merge records. This can potentially be conducted as part of the database in-house. If this option is not available, software can be purchased to perform de-duplication tasks.

3. Batch Cleansing: Data should be cleansed in its entirety on a less frequent basis. Dependent on activity, six months is sufficient for cleansing all addresses and standardising names, addresses and postal codes. This service is available as in house software or as an outsourced Bureau service. There are also online cleansing services e.g. Experian Intact which allow easy cleansing from your desk.

4. Suppressions: Prior to any mailing going out to a contact, the business must run their mailing file suppression screening. Suppression screening is available as an online, in house or outsourced service and the screening returns flags which must be fed back into the database to keep your records up to date and accurate.

5. Archiving: How old is old? Every business needs to have business rules in place to identify old data. Don’t be afraid of archiving. These customers need not be part of the main database, which means they can be managed as a separate subset to be targeted with alternative communications.

6. Customer Preference and adhoc updates: Define and implement rules around the capture and recoding of customer preferences and management. Appropriate systems must be in place to record vital information which may be the make or break in a sale. Address updates are equally as important and a defined set of processes will help achieve good levels of accuracy.

7. Audits: Whether or not things seem to be going well, reporting remains an integral part of early identification of problems with data. Weekly reports and where possible, daily monitoring of data coming into the organisation will help to maintain the data quality across the board. Monitoring is the key to Maintaining.

Data Quality Management need not be seen as a mammoth all singing and all dancing project. For smaller (and larger enterprises), data quality can be achieved in small controlled steps. As long as the desire is there, to move to a more organised, lean and effective way of working, the business can succeed in its quest without spending large amounts of money.


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