Data standardization is a key part of ensuring data quality. Lacking standardization results in bad data, which has numerous negative effects, from sending bad emails, to mailing to bad addresses, to losing customers altogether.
Unfortunately, data standardization is often left out of discussions when planning the input and organization of your company data, especially when you’re implementing a CRM system. This is a crying shame.
Step 1: Understanding and Cleaning Your Data
There are a number of different ways to approach standardization, but it comes down to being proactive toward the data that’s going into your CRM system. You want to make sure that the data is correct, clean, complete, formatted and verified before it gets committed into your CRM system, and before you take action on that data. Doing so ensures the accuracy and integrity of the information; and it’s going to prevent that dirty data from entering your database. It helps ensure that your operational efficiency is at its highest level by cleaning that data either prior to migrations and campaigns or, at that initial point of entry within your CRM system.
Step 2: Knowing the Data Entry Points
If you’re capturing data from a web form, for example, you want to know what data you’re collecting and how you’re collecting it. A form can have open text fields, multiple choice options, and pull responses into a spreadsheet or CRM system. Understanding where and how this data is collected helps determine whether normalization is needed.
Step 3: Choosing Data Standards
What type of data should be normalized? Data categories that describe the buyer persona or impact business processes are the best candidates for normalization. Translating crazy data into a standardized list gives you the ability to take actions that otherwise would be difficult or impossible to do properly. For example, data such as job title, industry, state, country, or platforms/technologies impact lead scoring and nurture messaging, so accuracy and consistency are vital. Some common choices for normalization are job titles, locations, and bogus data, such as “Mickey Mouse”.
Step 4: Defining the Normalization Matrix
A normalization matrix maps dirty data to your new standard data values. Start with a value that’s important to your organization, such as job title. Identify job levels for the different job title values, and then refine the title-to-level interpretations.
Once your normalization matrix is created, run it against your data. Once you’ve got the matrix, you need a data normalization program in the marketing automation system. Essentially, this is the brain that compares the entry data to the final result.
Don’t expect perfection: data normalization is an ongoing process for improving data hygiene over time. Individual “hiccups” can be separated out for manual follow-up, but the normalization process can significantly reduce the manual effort needed.
Learn more about data standardization with our free ebook below.