CDI - Build or Buy?
Organizations with a central HQ and regional offices, must have some form of CDI in order to accurately understand their customer base. Otherwise you are left with disparate sets of disrelated data, unlinked and unconsolidated, making analysis and effective marketing nearly impossible. So once CDI as a topic is understood, the next question becomes - Build or Buy? After travelling down the "build" path for 2 years I wish I had researched and implemented a commercial product just to avoid the headaches alone...
However, with all that water under the bridge, it would be nice to be able to simply "plug-in" the missing parts to make my CDI solution complete.
I recommend buying a solution outright, even though the cost may be overwhelming at first, it will pay off if your organization is doing any volume of mailing - and will probably pay itself back in postage saved alone.
Here are the key questions that should be asked of a CDI vendor if you are looking to buy:
* Since systems rarely share identifiers, does the solution score and match data taking advantage of all attributes that aid in the matching process using likelihood statistical theory for the highest levels of accuracy? State-of-the-art systems use "probabilistic algorithms" to compare attributes one by one and produce a weighted score for each pair of records. The higher the score, the more likely the records represent the same person. This method improves matching accuracy by using weights that are specific to an organization's data.
* Does the vendor provide analysis based on your own real-world data? Premier providers conduct frequency-based file analysis to provide weighting and thresholds specific to a customer's data. By using your own data, a better match is possible than by using generic criteria and arbitrary weights for each attribute. The data can be studied and tuned to recognize that a match on John Smith in the Bronx, NY, contains a more trusted matching value than a match on John Smith in Minnesota.
* Does the provider offer a way to capture and maintain a complete history of changes to attribute values? Such a method of "complete versioning" improves accuracy and has the ability to make the correct decisions on linking data even as it constantly changes due to marriage, divorce, new address, new phone number, etc.
* Does the solution enable you to establish the right accuracy and identification levels for your application and budget? Managing data quality is a business decision, so consider whether you need automated identification of results to ensure faster customer service, for example, or a solution that allows manual review of data to capture fraudulent customer activity, such as duplicate insurance claims.
* Can the solution overcome multiple identifications and data discrepancies across sources? Common obstacles to customer identity include transpositions, misspellings, nicknames, aliases, address inconsistencies and identity misrepresentations, all of which can occur when customer data flows into the company through multiple touchpoints. A strong CDI product should be able to keep data up to date and synchronized across the enterprise.
* Can you use the same matching technology in order to maintain all your relationships: for example, individuals, households and companies?
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