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I recently came across this great post by Jonathan Block on the Sirius Decisions blog. He hits the nail on the head with his comments on maintaining the quality of marketing databases. Here’s what he says:

• Dirty databases abound despite significant efforts by demand creation executives.
• Most companies focus on trying to clean the entire database at once rather than fix the intelligence that matches the right contact with the right offering
• Database quality is a point in time and all data cleansing projects have an end date.
• The data is cleanest right before the next round of contacts are uploaded into the database.
• Distributed data systems are difficult to maintain and synchronize.

Very simply it’s about the process not the state of the system. No matter how many times you clean your database and bring it to a clean state, unless you create a clean process you will always find yourselves in the quagmire of unclean data. Here are some of his recommendations:

• Create a unified customer database. This helps to avoid integration and synchronization issues, lost responses, incorrectly routed leads, marketing redundancy amongst other issues and drives up the ROI for this activity.
• Couple that with a robust data quality strategy that facilitates segmenting and targeting. Implement best practices around what data points are acceptable (or enough) for your sales and marketing teams to do their jobs.
• In addition to data quality marketing operations personnel should co-own data maintenance along with field marketing and sales. A sales partner is necessary to ensure compliance from the sales side in both the marketing automation and CRM systems.
• Target “ideal” prospects based on macro criteria such as industry or sub-industry growth and maturity, behavior patterns, demographics and regulations. (This could be part of your buyer personas, more on that in a future post.) Putting this into practice helps prioritize contact discovery, account intelligence and maintenance efforts.

So if you implement good practices into your system processes you can avoid a majority of the data issues most organizations face. I will try to touch on some of these best practices such as minimizing collection points, moving records with incomplete data to a discard or holding pile, etc. in a future post.