6 recommendations for Marketing Operations to improve data maturity (post 3 of 5)

Data is the fuel that powers modern marketing, enabling better understanding of customer needs that leads to better engagement, better content, and better marketing tools/approaches. Data by itself is useless, however, without a mature data management infrastructure of people, processes, and technology to turn raw data into actionable insight that drives engagement and ROI. Our “Data and Insight Series” focuses on building that mature data infrastructure.

The previous posts in this “data and insights” series described the importance of data in driving marketing ROI and detailed how your organization can develop a mature data infrastructure to support quality data and insights. In this post, 6 recommendations are offered that will have an immediate impact on your data quality and data maturity. 

1. Improve the value of data with a data governance model. You can achieve data governance in phases, so that it doesn't need to be super complex right from the start. A lot of organizations get stalled, and initiative dwindles, because they try to overcomplicate data governance. Three important things to focus on initially are:

(1) Build a cross-functional team made up of people who have a stake in quality data.

(2) Enable and empower the data team with decision-making powers, so they can identify issues and make needed changes to ensure data quality and effective data governance.

(3) Map the data landscape you have, which requires having working sessions where the cross-functional team sits down together and everyone pulls up their systems and brings up the data points that are in play for them. Each team would also bring up its use cases for data and you can start discussing sources of data and their quality. 

You’ll need to break data soilo’s wherever they are. So, for example, maybe marketing has their campaign data and customer segments built on certain data fields, say “country.” What happens when your ERP system changes from a three digit country code to a two digit country code, or vice versa? Your ERP is likely integrated with your CRM, so the new country code comes into your CRM. Your CRM is also integrated with your MAP. The country code change can completely blow up every single logic and country code that marketing uses in its MAP. That's a more common and chaotic scenario than you might think. 

That’s why having cross-functional data governance protects the integrity and quality of your data and the systems it flows through. You need to map out your data systems, get people talking across functions and systems about any pending changes and how they might impact everyone’s use cases and data flows. 

2. Perform an annual (or periodic) health check on your data and data quality, supported by a robust data health dashboard for ongoing monitoring and support. Ideally, if you have a cross-functional data team in place, you're able to do the health check across the entire business and across all your systems. Marketing, for instance, may have data within its lead scoring program that suddenly stops getting captured for lead scoring purposes. Well, you're probably not going to notice that until a health check reveals it – during a health check, you'd actually look at the penetration of values across your key fields and you’d see that something’s gone missing. The health check helps you identify and remedy problems with your data and systems before they significantly impact everything you do.

3. Create a systems integration roadmap, allowing for “accepted levels” of integration within each phase, and communicate progress.  If your organization uses a MAP, CRM, and ERP, and your marketing team wants more customer-level data that lives within your ERP, marketing may find itself manually extracting all that data. These manual processes take so much time and effort, and can potentially diminsh the quality of the data too. So you need to know beforehand whether there's an integration in place (or not). An integration roadmap gives you that information.

It tells you where you are with your core system integrations and what you have planned to expand those integrations. Knowing that can save you on needless investments, where people say, “we're not getting the data we need, so we need to invest in a CDP.” Maybe you don't need a CDP, but actually just need to better integrate what you already have – and let's put a plan in place to do that.

4. Implement controls on all data input and output sources to ensure data compliance, as well as data integrity. When it comes to data input, people often ask, “how do we fix the data that's already in the system?” They don't necessarily think about how to fix data before it gets into the system. So looking at your form captures and your upload templates, etc., can help you ensure that you have quality controls in place before data flows in. 

You could define validation rules on your forms or pull down master templates for your uploads. And it’d be exactly the same for your outputs. Make sure that not just anyone can export your data sets – you need controls in place, permissions within your user controls. That’s more important than ever because of increasing data compliance requirements.

5. Stay current on key business and/or functional team use cases to ensure data and insights are aligned. This recommendation requires talking to people regularly in cross-functional planning meetings in order to understand and align with their use cases. Marketing operations, for instance, needs to remain aligned to the business use cases across their stakeholder teams. If that alignment is lost, then the data and insights you're capturing are going to lose value and confidence levels will erode.  

6. Get a seat at the table re: martech/tech purchase decisions that will generate data within your Data Governance model. New tech can change everything. New data could also overlap where data is already being collected. Data may need to be integrated into a BI tool or a data lake. If data comes in and is integrated with multiple systems, there has to be consideration of the impacts. 

When you're looking at responsibilities around data management insight, you have to communicate with the stakeholders across functions. Planning across functions is far better than people getting surprised by unanticipated impacts that disrupt data quality and operations.

For help in improving your data quality and data management maturity, reach out to us today.