The most common recurring recommendation I make to customers is to purge data from their databases. As the years go by, it’s easy for the data to pile up. Just like we hold on to those jeans in our closet in the hope of losing ten pounds, we often hold on to data sets for “just in case” scenarios that don’t materialize. Although letting go can be tough, there are four key reasons that can make the purge process worth it:
Excessive amounts of data slow down regular update processes, the building of data aggregations, analytics, ad-hoc queries, reporting and dashboard performance, as well as campaign performance. I’ve seen many business intelligence and campaign platforms reach their max performance limits, frustrating technology teams and end users.
Although archive storage for snapshots or old files is generally inexpensive, active block storage hardware can be pretty pricy. In addition to storage costs, many customer data platforms, email platforms, and campaign segmentation tools charge on a per-profile or per-email basis. This means that you are likely being charged for individuals that are unmarketable or have incomplete records. You may also find yourself in need of a new database server to handle the higher data volumes, along with an increase in database software license costs.
What is that table for again? Poor documentation and lack of cleanup processes can result in copious mystery tables. Single or temporary use tables can add up quickly. Forgetting to delete tables on a regular basis can result in analysis paralysis, where teams are afraid to purge tables ‘just in case’ some person or process might be using the data. Similar tables or views are often created for different audiences.
We find that the oldest data typically carries the highest amount of quality deficits. Legacy systems typically have fewer checkpoints and best practices built in. This means many of these records aren’t marketable or useful in any way. We also find issues where legacy address data has not recently undergone NCOA (National Change of Address) processing, and addresses are so out of date, an updated address cannot be found. Additionally, bad data often creates bad data merge and duplicate data scenarios when it comes to customer records.
Cleaning out old or legacy data can be a complex and time-consuming process, but it is important to do so regularly to ensure that your data is accurate, complete, and secure. By following the tips above, you can make the cleaning process more efficient and effective.
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Christy brings over 15 years of experience in data analysis, direct marketing, strategy, requirements management, systems implementation, and project management. She specializes in customer and prospect marketing database, campaign, reporting, and email solution implementations.
Christy has a proven track record of helping clients implement complex marketing programs, including service reminders, welcomes, retention, upsell, and winback campaigns. She also provides solution architecture support and best practice recommendations to her clients, and leads a team of Solution Managers, Business Systems Analysts, Email Technology Leads, and Email Deliverability Analysts. Her experience spans the automotive, retail, telecommunications, non-profit, healthcare, insurance, and defense industries. She holds a Master of Business Administration and a Master of Finance degree from Boston College.