Data Enrichment And How It Helps In Improving Data To Add Value
Traditionally, data integration process is about extracting, transforming and loading. Pulling a data out of a source system is “extracting”, to “transform” means approving the source of the data and renew it for the expected standard whereas “loading” is storing the data at a place.
Data enrichment has recently come into picture offering allowing important development in business value of integrated data. For its effective implementation requires sound and knowledgeable data management practices. The integrators work to retrieve the data from its source to the destination unharmed. It’s as if extracting, transforming and loading developers are movers who are responsible for keeping your furniture at a new place unbroken. Now a day’s businesses want these developers to improve and repair data before transferring it.
One of the relevant instances of data enrichment is address connection. When you add your address on some e-commerce website, it auto corrects itself by entering street name, city and also the four digit zip code. ETL sellers can boost various possibilities beyond only address correction. These are added information to demographics database from which the sellers provide behavioral, demographic, psychographic, geographic, and census data.
Enrichment is just not restricted to demographics. The definition of code of conduct integrated into ETL field for any data source is allowed to data quality tools:
Cross checking the incoming records with existing data like finding which insured member the claim applies to.
Rectifying invalid data which are based on other data as per the record like re-checking beyond reach and manually entered calculations on the basis of independent automated data feed.
Insertions of missing values on the basis of other data available. So while loading a claim for travel ticket, system may fill a missing value for gender.
Changing the source of the data runs in opposition with the developer’s instincts and it’s risky. Workings which automatically match, insert and auto correct data, operates with such level that sometimes they gets wrong. A customer service enterprise whose processed routine are around 10 million records 95% accuracy which means that there’s room for data enrichment for those hundreds of thousands matches not made or got incorrect. It is not mandatory that a respective application is involved.
Considering these risks, following the undermined steps, the organizations can augment enrichment to their data integration field:
- Integrators who are knowledgeable enough to understand that the key drivers are the incoming data and its intended use of what data is enriched, how it is done and test its results.
- Identifiable and auditable enriched data in the destination database. It must feature a complete lineage metadata involving the data element source, its loading time and the current status. For data added in insertion form, adding, checking and rectifying the source data will prove to be truer. Analysts should know the direct source of data generated and the accuracy level of the source.
- Enrichment processes should store modified and augmented data in a way that the analysts have the access or permission to the real or raw data source. They must give their suggestions for improvements after testing the enrichment processes if by any reason it doesn’t meet the specifics regarding the analysis needed. Then they must go back to the authentic data source.
By sticking on to these principles, which guides and ensure that your organizations deploy enrichment processes, enhances the value of the business and of the integrated data while simultaneously reducing risks and increasing the flexibility as the requirement comes up.
Originally published at surevin.com on July 6, 2016.