Four Steps to Efficient Data Processing
More Vang has one of the most skilled data teams in the industry. We handle millions of data records a year, coming from countless systems in a variety of formats and states of readiness. From time to time, we run into common issues that can be easily avoided: things like missing elements in the data, last minute changes to the list, or delayed approvals.
Nobody wants to miss a mail date because there are problems with the data. Luckily, there are a few things you and your data team can do when pulling and preparing your list to keep the process moving.
Keep your database clean and updated.
When it comes to data, the cleaner the better! We’ve written before about address verification and how you can use the information provided in your COA reports to update your database. There are also a number of tools you can use to automate all or parts of the data cleaning process. Maintaining good data hygiene will help you avoid all manner of delays and protect your brand’s reputation, not to mention save you money and resources.
Review your data before sending it over.
You wouldn’t send a proposal without proofreading, so apply that same conscientiousness to your data. Ensure your data is in the correct format (.csv, .txt, .xls, or .xlsx) and includes all of the fields you need for mailing as well as any variable data or content you plan to use. All address information should be in different cells/columns in the same row for each record, and there shouldn’t be any soft returns in the fields. If you want to include any seeds in your list, add them before sending your data. If you’re working with a selection of data based on filters, it’s good practice to spot check a couple of records to confirm the right data pulled into the file.
Quickly approve your data proofs.
With the exception of automated direct mail programs, you’ll receive a set of data proofs for every mailing. We’ll send approximately 20-30 PDF images for your approval. Those images are purposefully selected to show you different iterations of how the data will appear in the final product, including the tallest, widest, first 10, and last 10 records. The quicker you can approve these proofs, the quicker we can get your project in the mail.
Normalize your data.
Normalization is the process of structuring data so it’s consistent across all records and fields. This includes making sure you have the same columns of information in the same order with the same content formatting. While we only formally require normalized data when we are automating direct mail, it does help build efficiencies around data processing.
If your incoming data is consistent (i.e., normalized), we may identify opportunities for automation to speed up the steps involved with handling your data. For example, we can save import settings so the data process runs smoothly. You can structure and format your data however you like as long as it is consistent job-to-job. Your Project Manager can even work with you to create a data template that includes all of the fields you will need for your mailing.
To keep things running as smoothly as possible, we’ve put together a data intake checklist that covers the details we’ll need to handle and process your data efficiently. If you have any questions, reach out to your Project or Account Manager—they’ll be happy to help!