Completing the Picture - Enriching Your Data

Data augmentation can range from something as simple as adding a missing postcode, to augmenting records with demographic or geographic data based on a name or address match. It's a simple process, with the value gained far outweighing the effort required.

According to Leigh Bates, Data Quality Product Manager, SAS UK, "After profiling, cleaning and consolidation, you'll have achieved the best representation of your data. But this alone may not deliver maximum competitive advantage. For example, you probably won't be able to build a complete customer profile, to address all requirements in your CRM strategy. Augmentation provides the additional value needed."

"For example, organisations wanting to target different campaigns to prospects in various locations will benefit by adding geographic data to their customer profiles, or credit information from a third party provider," he adds.

CRM is just one example. The same approaches can be applied with equal success to any other data or business activity.

Enhance and Enrich
Technologies in SAS enable you to enrich data with the following types of information:

• Geographic: such as postcode, county name, longitude and latitude, and political district
• Behavioural: including purchases, credit risk and preferred communication channels
• Demographic: such as income, marital status, education, age and number of children
• Psychographic: ranging from hobbies and interests to political affiliation
• Census: household and community data

So how does augmentation work in practice?

While most data quality tools focus on name and address data, SAS supports any number of non-name and non-address data types, including dates, telephone numbers, account numbers and e-mail information, to name just a few. SAS also enables full customisation of the data quality rules engine, so you can modify existing rules, tweak data types and create your own data types. For example, If you want to parse, standardise and match product names, you simply create a "product name" data type that is specific to the type of data you have.

Moreover, integration with ETL Studio supports the easy deployment of quality rules into an automated batch environment with no need for extensive programming. You can achieve highly accurate matching from a single interface: whether you want to identify duplicate records, link people into households, uncover fraud, or link information across multiple data sources.

Metadata is the key. When integrating rules into the batch ETL process, metadata is captured and registered at every stage. This enables you to clarify how data was derived, its source, how it was transformed and by whom, the target destination (where it was sent), and how data has been analysed for reports. Such detail enables instant reconciliation against any source.

This is why we talk about ETLQ - a robust ETL process with integrated data quality.

Summary
Now that we've covered all four stages, where does this leave the people responsible for data quality? The SAS^® Data Quality Solution and methodology speed up the development and deployment of data quality rules, which can mean a much faster and higher ROI. Integrating data quality within the ETL process provides, in a measurable way, real value to IT professionals, the business analysts they support and the organisation in general.

The solution is also easy to use. With its intuitive interface SAS enables you to delete erroneous data, merge data from disparate databases across platforms and match common data for improved accuracy. It's all at your fingertips, with activities like cleansing, consolidation and enhancement made far easier and less resource-intensive - with the net result of ensuring true quality in your data to add real value.

For more details on the SAS Data Quality Solution and methodology, please visit: http://www.sas.com/technologies/dw/etl/index.html