Lizz Hellinga talking about clean data

Why Clean Data Is Non-Negotiable in the AI Era with Lizz Hellinga

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Today on the Salesforce Admins Podcast, we talk to Lizz Hellinga, Consultant and Salesforce MVP. Join us as we chat about why clean data is more important than ever if you want to leverage the potential of generative AI.

You should subscribe for the full episode, but here are a few takeaways from our conversation with Lizz Hellinga.

Generative AI needs clean data

I brought Lizz on the pod to bring you an important message: clean data is no longer optional. “If your data isn’t ready for generative AI, your business isn’t ready,” she says. This was the theme of her presentation at Florida Dreamin’, and I thought it was something every admin needed to hear.

Everyone is excited about the new generative AI tools coming to Salesforce and it’s potential to revolutionize how we use data. Something that Lizz feels gets lost in the conversation, however, is that these insights will only be as good as the data you use to generate them. That’s why clean data is more important than ever before.

What is bad data?

This naturally begs the question, what makes for bad data? Some common examples Lizz shares include:

  • Duplicate data
  • Inaccurate data
  • Incomplete data
  • Stale data
  • Hoarded data

Hoarded data sticks out to me as something that isn’t discussed as much. As Lizz explains, not too long ago we went through a phase of “more data = good.” This has led many organizations to blindly hold onto data they’ll never use but are too afraid to throw away.

For Lizz, the key here is to work with your stakeholders to create a data governance policy. That way you’re clear on what data quality means for your organization and what data you don’t need to retain. As Lizz points out, reporting is a good opportunity to highlight why this is important to everyone involved.

How to get started cleaning your data for generative AI

When you’re looking at what data is most important to clean up for generative AI, Lizz recommends that you start by documenting the process you’re going to be working with. What are the essential data points in that journey? How does the data come in, and how can you make that easier?

It’s hard to get people motivated to clean up their data. Whether it’s the end of the quarter or the beginning of the new one, it’s never the right time. For Lizz, you need to talk about the why. You need to sell your stakeholders on what generative AI can do to make their lives easier, and why you need high-quality data to do that.

We get into a lot of specifics with Lizz on the podcast, so be sure to take a listen to learn more. And remember, now’s the time to clean up your data!

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Full show transcript

Mike:
This week on the Salesforce Admins Podcast, we are talking with Lizz Hellinga about why clean data is so important for AI. Lizz is a consultant and Salesforce MVP, and before you think, “I can skip this,” it’s probably going to be boring. Let me tell you, this is a fun conversation because you’re going to learn about different types of bad data. One of them is super fun to understand. Also, Lizz shares with us a lot of tips and tools for cleaning up your bad data, and also making things a little bit more easy for your users, so I really enjoy this conversation.

But before we get in the episode, can you just be sure that you’re following the Salesforce Admins Podcast on iTunes or just wherever you get your podcast. That way, you get a new episode every Thursday right on your phone. So with that, let’s get to the conversation with Lizz. So Lizz, welcome back to the podcast.

Lizz Hellinga:
Thank you, Mike, for inviting me. I am thrilled to be here.

Mike:
It’s been a while since you’ve been on and a part of the team, but we had Jennifer Lee down at Florida Dreamin’, and she Slacked me, “Oh my God, Mike, you have to talk to Lizz about her Florida Dreamin’ session and Clean Data, and get her on the podcast.” That’s literally word for word what she said. So I listen to my team members when they have good suggestions and wanted to get back on the podcast and talk about clean data and why it’s important. But before we do that, what have you been up to?

Lizz Hellinga:
Yeah, it’s been a busy time. I’ve been consulting for a couple of years now, and just building that and enjoying working with customers on their Salesforce challenges. Then, I did move to Michigan, so I am enjoying the Michigander lifestyle, which includes winter, which is coming.

Mike:
Yeah, you’re north of the cold air line.

Lizz Hellinga:
Exactly, but it’s beautiful. So we’re in Holland.

Mike:
Yeah, and you get all the seasons.

Lizz Hellinga:
We do.

Mike:
I like the seasons.

Lizz Hellinga:
Yup. Yes.

Mike:
Yep. I hinted you had a great presentation at Florida Dreamin’ about Clean Data, and especially now with AI, I guess we need clean data. I don’t know, Lizz. Do we need clean data? I mean, we’ve always needed clean data, right? Like I’m supposed to clean my room.

Lizz Hellinga:
Yes. I mean, we always needed clean data. We always need clean data, but it is now not optional, right? You need to have clean data because with generative AI, it’s table stakes. So if your data’s not ready for generative AI, your business isn’t ready, and I’ve read that amazing quote from a McKinsey report that came out in September about data and being ready for AI.

But I will say, before we get too far into this, I was inspired to discuss data because of a podcast that you all shared, not once, but twice, with Sarah Flamion on generative AI. So I think it aired again in September … Maybe it was originally in August.

Mike:
Yeah.

Lizz Hellinga:
And listening to that, and I started to think about, “Well, what can I do to help the orgs that I’m involved in be ready for AI?” And doing my research, I came across this McKinsey article that talks about if your data isn’t ready for generative AI, your business isn’t ready, and I thought, “Well, gosh, not all of this is new, right?” Having your data and your clean data approach isn’t something that’s new to Salesforce Admins. It just is becoming more important now, because of the way that data is consumed to create generative AI outputs.

Mike:
Yeah. I think it was always, “Okay. Well, this report’s wrong, so I need to fix it,” or kind of as issues play whack-a-mole with data, “If that comes up, then we’ll deal with it,” or, “If there’s imports that are going sour, then we’ll kind of deal with it,” but now that we’re on the precipice of having these discussions, I feel like the second discussion besides, “What are we going to use AI for?,” is exactly what you brought up is, “Is it ready? Have you ran a report? Can we even have AI tell us if we’ve got a good enough phone number list?”

Lizz Hellinga:
Exactly, and so that leads to my first question in the presentation that I shared with the audience was, “What really is bad data? What is poor quality data?” Some of the common ones, duplicate data, inaccurate data that we see, incomplete data, that’s a huge challenge usually, stale data, and my new favorite, hoarded data. So almost having too much data, not going through, and maybe cleaning up, archiving, removing data from your system.

Mike:
Oh, I feel like that could be the next show on TLC Data Owners.

Lizz Hellinga:
I have seen where I’ve been in orgs where people will have 600,000 contacts in there and maybe only use about 30,000 at a given time, so …

Mike:
So is that what you mean by hoarded data?

Lizz Hellinga:
Yes.

Mike:
Okay.

Lizz Hellinga:
Just maintaining or keeping data in there that may not be relevant.

Mike:
Just afraid to throw it away.

Lizz Hellinga:
Yeah, just to … Yeah.

Mike:
I mean, we got it, right? At some point, you paid for a list, or did a trade show, had a fishbowl out there.

Lizz Hellinga:
Exactly, and then they just never go through to clean it and remove it, and sometimes there’s resistance to doing that. It just depends on the org and the leadership, and that changes at times.

Mike:
Yeah, or I could see this, I bet there’s no policy for hoarded data …

Lizz Hellinga:
True.

Mike:
… to really sit down and like what … Lizz, you do a lot of consulting. You sit down. Well, what is the last time you’ve contacted them, and then setting that as a timeline, and okay, maybe contacts older than that. Now, this is our policy, much in the same way of data retention, you know?

Lizz Hellinga:
Exactly. And it’s the same type of conversation, and it’s important that as an admin, that you and your stakeholders agree on how you define that data. “How do you retain it? What do you define as poor quality or bad data?,” and you have to work with your stakeholders to do that, but there’s ways that you can start the conversation, because it can feel daunting, and reporting is the best place to start.

Mike:
Now, as we did, or as you mentioned, the types of bad data with hoarded data, as you would go through and create different applications, would you include maybe some of those policies or procedures in that documentation? Right? I’m thinking if we’re creating maybe one-off delivery records as they would apply to an opportunity, for example, we probably don’t need to keep that for very long, maybe a year or two as part of a data retention policy, but would you include kind of what the definition of hoarded data in your process documentation?

Lizz Hellinga:
I would. I would, and you have to have a data governance policy, I mean, at least an outline, and you have to start somewhere with it, or else it could get unruly and out of control. I mean, many orgs do have that, where they’re just not as diligent about maintaining it because they’re only working on the records that they’re focused on at that time.

Mike:
Right.

Lizz Hellinga:
And it can become pretty easy, and so that’s why as an admin, it is your role to think through these things and build the reports and have the discussions around what this means, and sort of push that rock forward within your company.

Mike:
Yeah, and I think as we think through the use of AI and cleaning data, anytime you do data cleanup as a topic, whether it’s a blog post or a podcast, I’m sure, or eight or so minutes in, I’d be surprised half the people are listening because the other half are like, “I don’t want to listen to that. I know I got to do.” It’s like almost weight loss. Yes, you need to do it for all of your data, but I would think with AI, you would really want to prioritize the data that you’re going to expose as part of your use with AI, right?

Lizz Hellinga:
Yes. And I would start with your key processes that you want to use AI to support. So if you’re thinking about your sales process, that’s a big topic that comes up quite a bit. How can sales reps use AI for contacting clients or knowing when to contact clients, and what to contact them about? I would start with by documenting that process and really highlighting, “What are the essential data points within that journey?,” so whether it’s, “What contact information is essential?,” “What account information?,” “What opportunity data will help drive those generative insights?”

You can start there and start even just simply building some reports that examines that information. “Do you have the email addresses? Do you have correct roles, and titles, contact? Are you using last activity date?,” things like that, to help you start to see how healthy that data is. So it doesn’t have to be overwhelming. You could start with just a few basic objects to get that going.

Mike:
Yeah. Then, once you kind of have those reports and you figure out the level of uncleanliness that you have, what’s out there for admins to kind of work on and pick away and get that data cleaned up?

Lizz Hellinga:
There’s a couple of things that you can do starting today. So reports and dashboards are the springboard for it, but you can have fun with it, kind of gamify it, and use … One Salesforce Labs application that I like is called Clean Your Room! Dashboard. That just is a springboard dashboard with components that help you examine the fields that you find important for AI or for your processes. You can use those to start a competition of like, “Who’s going to have the cleanest data?,” and fun things like that.

Obviously, you have to work with your stakeholders, but having people focus on updating some of the key fields makes it a lot easier than having them say like, “Oh my gosh, you got to clean up all 1,000 accounts that are in your territory.”

Mike:
Right.

Lizz Hellinga:
Nobody wants to do that. So just highlight the things that are most important, and it could be, even if you’re looking at deals this quarter or deals for the six months, opportunities, they’re set to close in the next six months, getting them to at least update things that are in flight will be hugely beneficial. And then, it’s about helping them build those habits. They’ll start to update other things when they have to update those few.

Mike:
What do you … Because I want to pause on that for a beat here. Anytime I’ve asked salespeople to update stuff it’s, “Well, it’s this quarter, or it’s almost the end of the quarter, or it’s this,” or I mean, a million excuses, right? And I’m sure you’ve ran into it too. I love the idea of gamifying it, and I think that’s something that an admin could really get an executive to help be behind, and they can push out. What is your advice for kind of rolling that out that also helps with scale when you gamify data cleaning in terms of timing that admins could suggest?

Lizz Hellinga:
There’s a couple of things that you could do. Obviously, the executive support is huge, really talking about the why. This is a journey, and it is going to become a competitive imperative. They have no choice to really … But if they want to take advantage of AI, they have to have updated data, and it will eventually make their roles easier so that they could potentially target more customers more quickly, right?

It’s hard to sell that long game sometimes, but gamifying a little bit, getting a Starbucks gift card or some kind of treat even goes a long way. People like to be rewarded even if it’s not something expensive. People like that recognition.

Mike:
Right. I think we always talk about cleaning data in the sense of, “Well, there’s bad data in there,” but it’s also a little bit like, “How do we prevent shoveling in a snowstorm?,” to use a Midwest term, because we’re Midwesterners here.

Lizz Hellinga:
Right.

Mike:
But once you do have everything shoveled, now, how do you prevent more bad data from accumulating? So there’s the proactive or reactive of running reports and fixing stuff, but what do you see as being on the proactive side so that we can start making sure that it’s cleaner data coming in?

Lizz Hellinga:
Yes, there’s so many tools that admins can use for this, and one of my favorites is Path, so Sales Path of putting those key fields that you deem necessary at front and center as they’re managing their opportunity lifecycle. The highlights panel is another area that you can use on a page layout to highlight key fields, again, that are essential, or I’ve even done this, where I’ve done like a quality of data rating in the highlights panel using flows and formula fields. Dynamic forms can help support the data entry process, as well as screen flows. Then, another thing that is great too is just the email integration. Either using Einstein Activity Capture, or the Outlook, or Google plugin with Salesforce is huge.

It makes it a lot easier for people to get the data in and accurate. So thinking more, I guess, putting on your user experience hat when you’re thinking about the requirements for your teams will help you, to your point, that you always say, Salesforce administration by walking around, so sitting with them, thinking about what their user experience is, and figuring out ways within the tool that you can make it easier for them to enter the data. Sometimes it’s just as simple as moving a field up the screen page layout, so above the fold. It could be that simple. Other times, it could be thinking about dynamic forms and something a little bit more sophisticated.

Mike:
Yeah. So it’s actually what you don’t know is next week on the podcast, I have David Giller on to talk about project management for admins, so a bit of a teaser here. And what you’ll quickly learn, I realized that I love the idea of SABWA, I just didn’t contextualize it right, because SABWA is really about personalization as opposed to customization, and the difference being scale.

Lizz Hellinga:
Right.

Mike:
And what you’re talking about there is also thinking through when you’re working with users to get better implementation, and maybe it is moving a field. Like I recall for a call center, they 100% wanted all of the fields lined up on the left side of the page, which wasn’t hard, right?

Lizz Hellinga:
No.

Mike:
In the edit field, you could edit the page layout, because it just didn’t make sense to them to go left, right, left, right, left, right, right? They just bang, bang, bang, bang, bang, bang. What was interesting is then you also have to think through, “Were other teams using that?,” and the sales team, it did not work for the sales team. They were used to seeing left, right, left, right, left, right, and so one of the things that we talk about, which is why I’m hung on this, is you get these ideas for personalization, but you can’t confuse them with customization, because there is ways to make it easier for that one particular user that doesn’t affect the productivity of all of your users.

Lizz Hellinga:
Right. And that’s where page layouts, dynamic forms, all those things can help with that process, so that in using the tools in your admin toolkit, that can help you think about it from almost like a persona-driven approach.

Mike:
Yeah. I think this is the interesting conversation that most people don’t have as the follow-up to, “Oh, there’s a whole bunch of AI stuff, and how are we using it?” And the second part is, “But are we ready to use it?” It’s almost like the, “Oh, everybody’s having a house party. Let’s have a house party,” and nobody looks around to be like, “Oh, we better clean the house first.”

Lizz Hellinga:
Right. Make sure everything’s in its right spot. Make sure we have plenty of napkins and cups.

Mike:
Yeah. Yeah, and that-

Lizz Hellinga:
It’s true.

Mike:
Yeah, because I mean, we’re so interested in just like, “Let’s use AI,” and then it’ll fix everything.

Lizz Hellinga:
Right. It’s all good until something happens that isn’t good, and then they’re like, “Oh, why is this not working the way we intend?” It’s like, “Well, we’ve got to go back and we’ve got to clean these things up.” It’s interesting because it’s a very hot topic, and it makes people nervous too, a little bit. I mean, there’s some hesitancy and there’s some like …

I still don’t fully understand generative AI. I mean, I’ve been-

Mike:
No. I think like three people do in the world. Yeah.

Lizz Hellinga:
Right, and so it’s, “What do I know?” is most important to help us be ready for that, and to me, it’s thinking about the quality of the data in the system so that when we are confident to make those decisions and investments and using generative AI in Salesforce, we’re ready, because it’s going to take a little bit to get people all on the same page and ready for it, but you don’t want it to fail because you haven’t spent the time thinking about the backbone to me, which is the data quality.

Mike:
Right. No, it’s comparable. This has an ending. I was reading somewhere that Glengarry Glen Ross, the movie with Alec Baldwin and Jack Lemmon, was actually … It was ranked up there as one of the … Something.

I forget what it was at the top of the list for. I bring it up because it is in, and they point this out in the article, very specific time in sales and technology in the United States, where salespeople had individual Rolodexes on their desk, and that was the value that that person brought to the organization. This almost feels like the same precipice, because can you imagine, at some point, every organization went from salespeople with Rolodexes to know, “We’re all going to take this data and share it, and you’re all going to have access to it, but I promise you, every single person’s Rolodex, those cards look different and had different notes, and now, we have to clean this up so that we can all be unified to be in one CRM.” Now, fast-forward how many years later, and now that we’re in that CRM, this is almost like, “How do we get that clean data to the next level so that AI can jump in?,” because I’m sure at that time, most people looked around and said, “I don’t know how A CRM works.” Much like you said, “I don’t know how an AI works.”

Lizz Hellinga:
Right. I mean, I had a really amazing manager say to me, probably in 2017, maybe 2016 … No, it was probably 2015.

Mike:
Wow. Wow.

Lizz Hellinga:
She said, “Our data in our CRM, our goal for it is to be an asset.” I feel like she was years ahead saying that to me, because I keep reflecting on that phrase within the last, I’d say just six months realistically, as we start to think about how we can use AI with Salesforce, with our CRM data. How is it an asset, that it will help us reach our customers, reach potential customers in a targeted, genuine approach?

Mike:
Yeah. Data is an asset. She’s light-years ahead.

Lizz Hellinga:
Yeah, she was.

Mike:
There might not even be companies that know that now.

Lizz Hellinga:
Right. I remember her saying that, and as I worked on this presentation for Florida Dreamin’, and thinking about like, “How do we turn it into an …” It needs to be an asset before we can even dip our toes in the world of generative AI.

Mike:
You would argue, and companies forget this too, but it is the one thing that is always sellable when companies close down or they get acquired. You could say, “Well, it’s the manufacturing plant.” No. I mean, you live in Michigan. Just go across the state to Detroit and look at how many empty manufacturing plants there are.

They didn’t buy the company because of the manufacturing plant. They didn’t buy the company because of the raw steel that they had in inventory. They bought it arguably for the brand, but also the data.

Lizz Hellinga:
Right.

Mike:
That’s something every company has, a list of people they sold to and what they bought.

Lizz Hellinga:
And their longevity with them.

Mike:
Right. Yeah, or lack thereof, longevity. There’s a lot to tackle. The one thing that I always love to ask people, because I feel like it could be walking into a hoarder’s house and you don’t know where to start, journey of a thousand miles starts with one footstep, for admins that listen to this and think, “Okay, maybe we have or have not started the conversation about we’re going to use AI,” needless to say, I should start the conversation about cleaning up data, where would you start? What would be the next thing that Lizz would do after listening to this podcast?

Lizz Hellinga:
We’ve said it a couple of times, I think begin with reports and dashboards. Start to examine the data. Maybe build some baseline reports, because your next move will need to be to have some conversations with stakeholders, so speak with leadership, your main stakeholders … I’m trying to think of the right word, but the main people that you work with that use the system. Start to define, “What is your bad data?,” because it is not so much like I may come in and say, “Oh, you haven’t talked to them in two years,” but your deal cycle may be three years, or there may be some different things that go on within your organization that are unique and different.

So I may think something stale because we haven’t talked to them in three months, but to you, your org, it could be a year. So determining what really is poor quality, low quality, bad data in your system, so whether it’s … Obviously duplicate’s usually inaccurate, incomplete, stale, hoarded, and you can kind of flip that and say, “What is the most important data about our customers that’s in Salesforce, and how do we make sure that people are able to update it accurately on a regular basis?” Those are a couple things to think about. So gather your stakeholders.

Reports usually will help you do that because it kind of shines a light on the problem areas. The next thing, thinking about your process. What data points are key throughout maybe your opportunity life cycle, your case life cycle? Thinking about those things will help you identify the essential data points in that journey, and that’s where you want to begin, because it can be overwhelming. You may have 150 fields on your account page.

I guarantee that not all of those are essential to the data journey in relation to maybe the opportunity life cycle. So think about those processes, and what are the key points of data? Then, embark on that journey of cleaning the data, but then also put on your user experience hat and think about how your end users are updating, adding, creating data in the system, and see what you can do to help drive towards those key data points so that they’re easily able to update them.

Mike:
I think that’s very actionable stuff. Thank you.

Lizz Hellinga:
Thank you. I’m excited to keep talking about this.

Mike:
I can tell.

Lizz Hellinga:
There’s so many things that you can do with promoting quality data within your system, but these are tactical things, but there’s also long-term, like with data governance, that you can start to be a leader. It’s a way to elevate your role, yourself within your organization because you’re seeing this. Clean data is the imperative now.

Mike:
Yeah. Absolutely. Thanks so much for coming on the podcast and hopefully inspiring us to clean up our data and not be data hoarders.

Lizz Hellinga:
Yes, please. We don’t need a TV show on that.

Mike:
I mean, we kind of do. I just like to see how they do it. I think that would be fun.

Lizz Hellinga:
It would be interesting to see all that.

Mike:
Because you kind of need that IT guy coming over and saying, “Move. Oh my God, you have 60,000 contacts in your Outlook. What are you doing?”

Lizz Hellinga:
Right?

Mike:
Just something funny like that.

Lizz Hellinga:
Clean those up.

Mike:
Right.

Lizz Hellinga:
I know.

Mike:
Exactly. All right, thanks, Lizz.

Lizz Hellinga:
Thanks so much.

Mike:
So I love that discussion with Lizz. I think her line, “The tactics aren’t anything new, it’s just more urgent now,” is completely true with the precipice of a new, really transformational technology coming our way on top of just having clean data being important, really making sure that our organizations and ourselves can be successful with AI as this comes about is super important. Now, if you enjoyed this episode, I need you to do one thing, and that is share it with somebody you think would appreciate listening to it. If you’re listening on iTunes, you can just tap the dots and choose Share Episode, then you can post it to social or text it to that friend. If you’re looking for more great resources, your one-stop for everything admin is admin.salesforce.com, including a transcript of the show, and be sure to join our conversation in the Admin Trailblazer group that’s over on the Trailblazer community, and of course, the links are in the show notes. So until next week, we’ll see you in the cloud.

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