Today on the Demand Gen Pod, Episode 5, Ryan discusses the importance of data governance in ensuring data quality and
compliance. He emphasizes that effective data governance supports decision
making and enables businesses to trust their segmentation and targeting
efforts. Ryan explains that data governance involves establishing policies,
procedures, and guidelines for handling, storing, and using data. He
highlights the significance of regularly assessing and improving data quality,
as well as assigning roles and responsibilities within a team for managing
data. Ryan also suggests implementing tools and technologies that support
metadata management and data quality management. He concludes by emphasizing
the need to foster a culture of data-driven decision making within
organizations through training programs, encouraging exploration of data,
incentivizing behavior aligned with being driven by insights from collected
information, creating decision rules based on those insights, and monitoring
metrics related to accuracy, completeness, timeliness tracking indicators like
breach incidents or regulatory compliance breaches along with stakeholder
satisfaction levels.
Summary notes from Episode 5:
- Data governance is crucial for effective decision making and ensuring data quality and compliance with regulations.
- It involves managing email and contact tables in CRM systems, including engaging unresponsive contacts and removing duplicates.
- Data governance improves operational efficiency and reduces redundancies.
- Access control is important to prevent unauthorized access to data based on different countries’ data privacy rules.
- Best practices include defining clear data governance policies, regularly checking and updating data, and implementing data stewardship responsibility and training programs.
- Data governance tools and technologies can support metadata management and data integration.
- Creating a data-driven culture involves using data to back up communications and making data-driven decisions.
- Strategies include data literacy training, encouraging data exploration, and incentivizing data-driven behavior.
- Metrics like data accuracy, completeness, and timeliness can be used to measure data governance success.
- Regular monitoring and reporting help identify areas for improvement and ensure continuous data governance success.
Full Transcript:
00:01
Welcome to demand, Genpod. My name is Ryan. Today we are talking about data governance. And I know this probably sounds like it’s going to be a little bit boring, but trust me, it really matters because all the cool things that we start talking about segmentation, personalization, dynamic, content, predictive, sending, all of these topics rely on our data. So we have to have a good process in place to ensure that our data is doing exactly what we need it to do and is telling us all the right things. So because of that, we’re going to get going and let’s make it happen. Before I forget, don’t forget to subscribe if you are watching us on YouTube or whatever your preferred podcast listening method is. But today, again, talking about data governance. So data governance quite simply is the process of managing and protecting an organization’s data assets.
00:52
And while that sounds really broad, I’m going to focus specifically on email and contact tables, CRM, that’s kind of what we’re going to focus on today. But listen, it’s really important because if we can ensure our data’s quality and its compliance with regulations, it can enable really effective decision making. And we can say, listen, we trust that if we build a segment and we have X number of rules inside our segmentation, that is who we’re going to get. And if you can’t trust your segmentation, then how can you trust that what you’re sending and who you’re sending it to are the right things? I mean, you can’t. So data governance overall is that management and control of data assets within an organization, but it entails establishing things like policies and procedures and guidelines for data handling, storage and usage. And there are some rules within things like CCPA and GDPR where for example, with GDPR you can’t have somebody’s email and contact information and a CRM record for three years, or you can only have it for three years after they stop responding.
01:56
So from the last time that they engaged or responded to something, and their last update, basically after three years, you need to throw them out. So that’s kind of a CCPA, I think I started with GDPR, but obviously related to CCPA as well. And a good policy anyway, because if you have somebody inside of your contact table who is not engaging, it’s your job, one, to try to engage those folks. But if you cannot engage them, just get rid of them. I mean, what does it matter why I have these dead emails where nobody’s responding from them? And that’s just one part of data governance. I mean, I think that leveraging data is really important. And so if you consistently send emails to your contacts, you can work on getting that response from them and kind of keep them in your system and also make sure that all of their data is up to date.
02:44
So sometimes that even comes down to confirming preferences. So every few years, you might decide that you want to confirm preferences and say, we just want to make sure that you still want to be opted in. Because the big win there is especially if you’re in a larger tool where you might be paying per contact, right? So smaller tools, they might have up to, you have a total allowance of say, 10,000 contacts, right? And then you go to another tier. In larger tools like Eliqua, you might be paying per contact. So you really don’t want to be having people inside of your platform that aren’t actually doing anything for you. So if they’re not getting anything in return from them and you can’t pull that out of them, then it’s worth getting rid of them and keeping that contact table clean and full of people who are engaging, or who you are actively trying to engage.
03:33
It should really be one of those two. Successful data governance really brings in this point of data quality and accuracy and consistency. But it also, like I said, enhances that decision making process, providing reliable and trustworthy data, and that really matters when we’re making those segmentation choices. Operational efficiency also gets improved through that streamlined data processing, and it also can reduce redundancies. So if you have duplicate records, that’s all kind of part of data governance, and making sure that if you have duplicate records, that you merge those together, or if you have two records of the same person with two email addresses, that you recognize that you may only want one of those. Maybe one is bad because they left a role and they started a new role, and so you have a new record with that new email. Now, there are tools, certainly, that can help you to work through this, and there are automation processes that you could build to manage a lot of it.
04:28
But sometimes it just comes down to taking a look at all of those, say, unengaged contacts, people who aren’t responding, who haven’t opened an email in a long time, looking at them and then making some hard decisions about what you want to do. This also comes down, for example, to unsubscribes as well. I don’t know of a single platform that charges you for unsubscribes, so it’s not necessarily a bad thing to keep people who have unsubscribed in your system, because then, you know, you can’t accidentally email them if you upload them again in another list. But that’s also another part of data governance, just to make sure that unsubscribed list is doing exactly what it’s supposed to be doing and that people can get on there really easily. While you’re doing this, things like roles and responsibilities should be assigned to your team, including things like data stewards, owners, executive sponsors for different parts of the data, and processes for data classification and access controls, as well as data lifecycle management and data quality management should also be established.
05:29
So let’s break these down. Something like access control super crucial, depending again, depending on the size of your business, but with larger businesses you might have a single instance with multiple countries in it. So for one of our clients, for example, in a tool like Eliqua, they have I think about ten countries in a single Eliqua instance and each of those countries has their own data privacy rules and regulations. And certainly one thing that you don’t want to allow to have happen is somebody working in the US for sending US emails, to be able to send somebody emails, say in Japan or in EMEA, whatever the particular country is, right? So if you have somebody who is set up to be working for the US team and they’re only sending emails for the US groups, then they shouldn’t have access to Spain’s data. You shouldn’t be able to see any of those contacts, you shouldn’t be certainly not able to edit them, and you certainly should not be able to send anything to them.
06:28
And that’s not really a slight on whoever’s doing the email work. It’s really for everybody’s benefit because at the end of the day you don’t accidentally send an email, say in English to somebody who never actually subscribed to emails in English and that could open the company up to liability. A couple of best practices define clear data governance policies and communication strategies. So what defines your policy? When are you making sure and checking the data to confirm that it is all either up to date or current? And how can you take that approach? What is it going to be? How often are you going to do it? And then communication strategies can work alongside to not only internal communication, but also external communication to your contacts. Like I mentioned, maybe every six months provide an opportunity for people to choose new groups to opt into.
07:19
Maybe they came in on the newsletter and they signed up for a newsletter, but you want to be able to opt them into marketing opportunities or sales outreach or whatever the case may be, right events, things like that. So every six months or so you could email everybody and make sure that they have an opportunity to kind of re up that subscription and make sure that they are aware. And the thing is that even if somebody doesn’t respond to other emails, but they do respond to that and they might choose something else, that’s an action, I mean, that is now allowing you to email them, say for another three years if you’re using that kind of data compliance profile. Another best practice I think is establishing that data stewardship responsibility and then training programs for it as well. So anybody who comes on to Onboard should be aware of these policies that you have in place and why they’re there.
08:11
I think that context is really important and then regularly assess and improve your data quality. For example, we see really frequently that marketing inside of the marketing CRM, it doesn’t really matter. Marketing cloud or Eliqua or pardot or whatever the case may be. Marketing will go and create fields and they’ll create them for a specific upload or a specific send. But those fields may not actually be getting used very often and very quickly. I’ve seen upwards of 500 600 fields in an instance and that is a direct result of poor data management, right? So part of that data governance is the management of that data itself and the database, and doing kind of a sweep to say what are these fields getting used for? Are they currently used in any campaigns, what campaigns are they being used in? And how can we improve the overall health of our system is really important.
09:09
Another good example of where data governance and data management can be really important is if you have fields that could mean more than one thing, or if you have multiple fields that could mean the same thing. For example, we had a client where a custom role field was created where it was job role, but the default field in the contact table was title. So now you have a title field and a job role field. And I just came across this the other day as I was building out a campaign and I needed to get a job title. And I was like, wait, is it job role or is it title? That led me to have to go and look up the forms that the data is coming in on to understand how that data was mapping in order for me to make an educated decision on what to pick.
09:54
That’s frustrating. That is a real easy, also direct comparison to saying good data governance can save time in the future because that took time to go and identify where that data lived and where it was coming in from. Another good, best practice is to implement data governance tools and technologies that support that. Metadata management and data quality management can be established through all of that metadata. And there are tools like Dun Bradstreet, there are lots of other ones that kind of help to provide metadata toward various fields and various data that you have, where you bring data in, you send it out, and then you get more data back. There are lots of APIs that will kind of allow you to do that. Again, I think that’s probably for bigger companies, but it does also help you to identify if somebody’s left a company and they’ve gone somewhere else where perhaps they might have gone.
10:44
So that’s really good. But there has to be a drive for this, right? You have to want to do this. So another big important part of it is to create this data driven culture and fostering that. Data driven culture involves promoting the use of data in decision making processes. And what I mean by that is what are opportunities for you to send emails or to send communications? Why are we sending them and can we back them up with data that we have captured in our systems? Or can we back it up with data that are on contact tables in our systems? For example, maybe you don’t even realize it, but over time, what’s happening is you are getting lots and lots of submissions or form submissions or whatever of people interested in a particular product or you’re seeing a particular angle that we could leverage.
11:34
And you may not figure that out unless you’re looking at sort of like a more macro approach to your data. And data governance can come in real handy there. Some strategies include providing data literacy training, encouraging data exploration, and also incentivizing that data driven behavior. You want to build those data driven programs and also be building decision rules based on those data driven rules. And then finally, some metrics and measurement inside of data governance. You can effectively measure all of these things through metrics like data accuracy, completeness and timeliness tracking indicators like data breach incidents. Certainly this is for larger companies, but like data breach incidents and regulatory compliance and stakeholder satisfaction, all of these things can assess the level of data governance. And those things are crucial. Finally, regularly monitoring and reporting help identify areas for improvement and can ensure this continuous data governance success.
12:36
So overall, coming on 15 minutes here, but overall, mastering data governance, it’s so crucial for today’s businesses, especially when we’re so focused on data driven decision making. But overall, it ensures that data quality compliance and it supports effective decision making, which I think is really important. Businesses should be prioritizing and investing in effective data governance practices to drive success and mitigate risk. And what business doesn’t want to mitigate risk? Thanks for listening. Today on the demand gen pod. My name is Ryan. Really appreciate it. We will see you next time.