Not All Databases Are Created Equal
When shopping for external data, consider the following when it comes to pricing options:
- Number of variables to be acquired: Don't just go for the full option. Pick the ones that you need (involve analysts), unless you get a fantastic deal for an all-inclusive option. Generally, most vendors provide multiple-packaging options.
- Number of records: Processed vs. Matched. Some vendors charge based on "processed" records, not just matched records. Depending on the match rate, it can make a big difference in total cost.
- Installment/update frequency: Real-time, weekly, monthly, quarterly, etc. Think carefully about how often you would need to refresh "demographic" data, which doesn't change as rapidly as transaction data, and how big the incremental universe would be for each update. Obviously, a real-time API feed can be costly.
- Delivery method: API vs. Batch Delivery, for example. Price, as well as the data menu, change quite a bit based on the delivery options.
- Availability of a full-licensing option: When the internal database becomes really big, full installment becomes a good option. But you would need internal capability for a match and append process that involves "soft-match," using similar names and addresses (imagine good-old name and address merge routines). It becomes a bit of commitment as the match and append becomes a part of the internal database update process.
Evaluating a database is a project in itself, and these nine evaluation criteria will be a good guideline. Depending on the businesses, of course, more conditions could be added to the list. And that is the final point that I did not even include in the list: That the database (or all data, for that matter) should be useful to meet the business goals.
I have been saying that "Big Data Must Get Smaller," and this whole Big Data movement should be about (1) Cutting down on the noise, and (2) Providing answers to decision-makers. If the data sources in question do not serve the business goals, cut them out of the plan, or cut loose the vendor if they are from external sources. It would be an easy decision if you "know" that the database in question is filled with dirty, sporadic and outdated data that cost lots of money to maintain.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at email@example.com.