The 3 Steps Needed for Squeaky Clean Data

Data cleansing is a pain point for many organizations. While it might seem like a daunting initiative to take on, standardizing your data can be crucial for your company.  Having clean data will optimize processes like segmentation, lead scoring and reporting – ensuring that your marketing efforts are working like a well-oiled machine.  How do you get squeaky clean data?  Keep reading to find out!

Steps to Data Standardization

Step 1 – Identify the fields that you want to standardize

Data fields that are often used for segmentation (e.g. state, country, job title) are good candidates for cleansing.  Think about which data elements your team uses for targeting, lead scoring and reporting.  Create a list of the identified fields.  Think about what the standard values should be for each field.

Step 2 – Analyze your existing data and align “dirty” data to a standard value

Look at the existing data in your database for the fields that you want to standardize.  All of the crazy misspellings, weird values or blank data needs to be translated or mapped to a standard value.  Depending on how many fields you want to normalize and how much data you have in your database, this step can be time consuming.  But it is well worth this time investment in the end!  Having standard values will allow you to accurately identify records of interest in your data.

Step 3 – Build a Contact Washing Machine program to cleanse your data

Ensure your marketing data is always clean by building a Contact Washing Machine program in your marketing automation platform.  This program will standardize fields on records that enter your database using the analysis you performed in step 2.

Want to know more about how to build a Contact Washing Machine in Eloqua? Download our guide here.

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