Recently, I wrote about the importance of maintaining a high level of prospect quality within your Marketing Cloud Account Engagement¹ instance. I talked about making sure your data was accurate, complete, and relevant because the benefits of doing so can permeate throughout the business. For the purposes of this article we are going to talk about the accuracy of your data and how you can tackle that beast of an issue if you are finding your data is anywhere from teenage boy room messy to a speck of dust messy. Let’s dig in.

What does it mean to eliminate data quality issues?

As we all know, data can come from anywhere and anything. You could have captured it first-hand at a tradeshow, it could have come from a data enrichment tool you recently purchased, and sometimes you might not even know where the data came from (zoinks Scoob!). Regardless of where your data came from, you need to make sure you and your stakeholders are able to extract value from it, and that means you need to eliminate those pesky quality issues. This brings me to my definition of what it means to eliminate data quality issues: preventing the poor quality data from coming in, and then standardizing and cleaning your existing data.

Why should I care about data quality issues in my Account Engagement instance?

Like I said above, the whole reason we collect data is so we can extract value from it and ensure our stakeholders across the business can do their jobs efficiently and effectively. Here are some reasons that come to mind as to why data quality issues are problematic:

• Inaccurate reporting. It’s usually not just you who is consuming reporting that references data points with poor quality. Directors, VPs, even CMOs all want to understand how the business is performing from a numbers perspective. The bad data can tell a completely different story compared to if the data was correct, which can lead to serious dollars and time being spent in the incorrect spaces.
• Your stakeholders lose trust. If the money and time are invested into an effort that does not yield successful results your stakeholders start to wonder if the data is even reliable. When we are able to ensure high data quality at the core, we can be sure the takeaways from that data are valid and accurate.
• Wasted time. This is one of my pet peeves as a MOPs professional. I want to be changing the world with my MOPs knowledge and be a helping hand to my stakeholders doing cool projects, but I know that if one data field is completely inaccurate I’ll have to spend time correcting the data via Data Loader, or even worse: manually.
• Missed opportunities. It’s not just what you had to do to clean up the messy data, but it’s also what you and your company could not do because of the messy data. We have an opportunity as MOPs professionals to play a significant role in creating a great brand experience for our customers and prospects, which oftentimes involves leveraging the data we collect. Personalization, whether it’s knowing the roles and responsibilities of an individual’s job or which product of ours they are most interested in, can really enhance the buyer journey.

How can I eliminate data quality issues in Account Engagement?

Account Engagement offers a variety of tools to aid you in this process. Here are some of the automations you can leverage in your Account Engagement instance:

• Segmentation Lists. While they may not be the sexiest automation tool in Account Engagement due to their limited capability, they can provide some value (especially if you already leverage tags and lists). This can serve as a solid starting point to understand the volume or your data quality issues, which can then be served up on a list to whomever else needs to be informed of the matter. As a reminder Segmentation Rules are only ran once but you can create others as needed.
• Automation Rules. We are moving up in capability and complexity here. Automation Rules offer more flexibility and capability in comparison to Segmentation Rules, but not quite as much as an Engagement Studio. Once resumed upon creation, Automation Rules will constantly run in the background looking for qualifying records and apply the desired action(s) you configure. In terms of rectifying data quality issues, you can create one to standardize field values and/or clear out field values for your old data and any new data coming in. Keep in mind the Repeating Rules configuration and rule group logic before pushing these bad boys live. This is where the Preview Matches capability can come in handy. The one big kicker with Automation Rules is you only have a limited amount of them available to use in your instance. Be shrewd with how you leverage them.
• Engagement Studio. Typically used for nurture and drip campaigns, Engagement Studios can also serve as a great tool for cleaning up your data on an ongoing basis. While Automation Rules don’t allow for advanced logic within the “Actions” section, Engagement Studios allow for a multitude of actions to be done while using the advanced logic. This can essentially combine multiple Automation Rules into a single Engagement Studio, which is especially helpful when you are close to your Automation Rule limit in the instance. It is important to note that there is also an Engagement Studio limit in your instance, but I’m confident most organizations out there are far from their assigned limit. Similar to above, use the testing functionality before activating these to ensure the correct updates are being made. Nothing like updating dirty data with more dirty data.

When should I prioritize data cleansing against my Revenue Operations roadmap?

The easy answer here is immediately, but that may not always be possible or necessary. I think it’s essential to first understand the impact of your poor data quality, which can be measured by how often the data is leveraged and the number of records holding bad data. If you and your team determine the number of records with bad data is low and the data points are not leveraged in other places across Account Engagement, the effort can most likely be pushed to another quarter as part of a larger instance-wide spring cleaning.

However, if you and your team are confident your instance contains many records with bad data and that data is leveraged for important business processes, start to build out a roadmap of how you want to tackle the issues at hand. I like to triage issues into High, Medium, and Low impact and communicate your plan out to the business. Chances are your stakeholders can provide some quality input on the effort as well.

Who should be responsible for fixing data quality issues in Account Engagement?

Data quality is an organization-wide effort, but you as a MOPs professional can establish the foundation and drive the effort forward. In my previous blog post I talked about how we can prevent bad data from coming in in the first place, which ultimately increases the quality of our prospects in Account Engagement.

Once you prevent the bad data from coming in and educate your stakeholders on the necessary processes to assist in your efforts, you can begin to clean up the messy existing data in the instance leveraging the tools I discussed above. One fair warning before doing this, though: communicate out when you plan to clean the old data, as this might trigger other processes to run that affect your stakeholders. I have learned this the hard way when sales reps begin getting a multitude of false positive alerts and stuff hits the fan. Your Salesforce admin team can help you identify some risks to be mindful of before cleaning up your data. Trust me, it’s worth the extra time spent.

What should I do after fixing data quality problems?

While we unfortunately can’t fix data quality issues with the snap of our fingers, we can leverage the automation tools available to us in Account Engagement to help us. Not only is a clean database pleasing to the eye, but the benefits can extend across the business.

Is your database quality in Account Engagement keeping you up at night? Not sure where to start? Let Etumos provide you with all the Account Engagement consulting services you could ever need.

¹In April 2022, Salesforce renamed Pardot to Marketing Cloud Account Engagement.