Essie Reynolds, MBA, is a Data Analyst and Business Intelligence Consultant at Sojourn Solutions.
Modern marketing is fueled by quality data. Understanding and coordinating all of the parts of your data ecosystem is therefore critical to marketing success. Below is a ten-step process you can follow to drive data quality within your organization. As mentioned above, these steps represent an update to a post we did way back in 2017 – a lot has changed since then, especially around data privacy and an increased emphasis on quality data to drive marketing outcomes, and this updated post certainly reflects those changes.
The 10 steps below are divided into three distinct categories: (1) surveying the data and ecosystem you already have; (2) the tactics of ensuring data quality; and (3) ongoing maintenance of your data and data ecosystem. Let’s begin.
1. Know your “why” for data
When you collect data just for the sake of collecting data, it can add lots of “noise,” increase the cost of storing your data, and lead to a poor user experience. In order to be more strategic about how you’re collecting and using data, you need to know what your organizational goals are for data, what questions you want answers to, and what you want to accomplish with data.
2. Be accountable and aligned on data
Each functional area within your organization should have a point person who's accountable for maintaining the data in their realm of responsibility. Anyone touching that data should be trained so they have some accountability to data quality as well.
There are always going to be differences in what different functions or departments might need regarding data, so the appropriate identity management needs to be defined and set up. For example, if the marketing team is buying a new martech tool, they need to be strategically aligned with their organization on who owns the tool, where the data will be stored, and who gets access to the data.
3. Complete an audit
Take a comprehensive look at all the data your organization is currently collecting. You’ll need to know where data is coming from, the tech tools that are involved, who's using what data, and why they’re using it. How is data being stored and analyzed? Knowing all this gives you a clear understanding of your entire data ecosystem.
An audit helps prevent data redundancies and highlights gaps that need closing. Part of the audit would be creating a visualization of data flows. You might have data going from Salesforce into Tableau and from Eloqua into Tableau. It helps to visualize these connections.
4. Outline rigorous data processes
You need defined data processes to ensure that your data is consistent and standardized. Your values should also be standardized with tools such as pick lists or country names (US v. USA v. U.S.A.). Standard processes make your data cleaner and make your reporting more accurate. Make sure everyone involved with your data understands how these processes help drive data quality. Have standardized routines for how data is added and deleted, how lists are uploaded, and how data is managed across the organization.
5. Automate with a contact washing machine (aka automated data cleansing)
Using a contact washing machine can make a huge difference in your data quality and save you time and manual effort in cleaning your data. It'll automate and streamline your data standardization. With a contact washing machine, you set up rules that are automatically applied when data flows in. Any data that comes in and deviates from your defined rules gets standardized by the contact washing machine.
6. Take advantage of third party applications
You can use third party applications to normalize, validate, and add missing contact data. So an app can validate email addresses or tell you if a phone number is still valid or help you fill in missing pieces of mailing addresses. You don’t want to send email or direct mail to addresses that are wrong or invalid – that wastes your resources and hurts your numbers/KPIs.
7. Create and use templates
Templates are key to the standardization of data: they save you time and improve your data quality while helping your user experience. For example, if you're using templates on your websites and landing pages, and you use pick lists, they’ll standardize your data because users can't free-form type anything incorrectly. Your marketing team can leverage templates and won’t have to keep reinventing the wheel.
8. Stay updated and compliant with privacy regulations
The number of data privacy laws are not just limited to Europe or Canada or California. You have to consider all the different data privacy laws in all the different places you're doing business in. You must comply with those data privacy laws so you can continue to do business in those places. In addition, customers want to see that you’re taking data privacy seriously. You should have a point person to ensure that you’re following relevant data privacy requirements.
9. Ongoing maintenance planning
Have a plan for regular maintenance of your data and data ecosystem. Routinely check on mapping fields between tools to ensure that the data flows are still accurate. In marketing automation, for example, you’d want to make sure that the fields you're mapping from Salesforce into your MAP are the relevant ones for what you need. And when you make changes in Salesforce, you must also consider how that affects your MAP.
10. Monitor the age of your database
If you have contacts that have been dormant for a year, you’d want to treat them differently than contacts who are brand new or very engaged. Those differences would determine how you're doing your nurturing and how you're tracking engagement. If you're not getting any new contacts or new leads, that would be a problem you’d want to analyze.
If there's no activity from a contact for a year, you're wasting a lot of resources trying to engage with someone who won’t engage. You might be able to save money if you say, “well, a percentage of this database hasn't responded to anything in a long time. Let’s make them inactive and target our efforts on contacts who engage.”
Need more help with your data and/or data management processes? Reach out to us here.
Editor's note: This post was originally published in March 2017 and has been updated for comprehensiveness.
Related Data Management posts:
Driving ABM success: How to leverage quality data to define and engage your target accounts
How to create a culture of data to drive your B2B marketing: 4 steps to success
How Marketing Operations enables data quality and the infrastructure needed to drive ROI
Originally published March 5, 2017, updated May 13, 2022