In this Xforce webinar, Andre van Kampe (Salesforce Consultant, Sales Engineers Amsterdam) explains the importance of data quality and shares a simple, five-step framework that will help you administer a data quality framework.
Any sales or marketing professional who needs to understand the crucial role data quality plays in successful sales and marketing will benefit from this video. Discover how Salesforce can become a “single source of the truth” by following the step-by-step, iterative framework. Plus, discover tips and tricks for tackling your data management roles.
[00:00:00] Welcome to another X-Force data summit presentation. Today I've got Andrea van Kampen, who works, for sales engineers in Amsterdam. He's a consultant there. And he's going to talk about a framework for data quality. And I think we all want to hear how to make our data quality better and our Salesforce words.
[00:00:32] So here's Andre. Thank you for letting me speak at this summit. That's, it's quite exciting. My very first virtual summit. I'll skip most of this because it's the introduction. I have a background in IT, and for the last seven years I've been involved in Salesforce as a consultant, and I currently work in Amsterdam for a consulting partner.
[00:01:06] You might recognize the logo. YeurDreamin’. That's what I do in my free time. I'm a user group leader and also organize community conferences, a bit like this, but in person.
[00:01:30] So for today there are four chunks. First of all I have some background about a data framework and quality. We introduce a data steward then why data quality matters. So this will help you explain why we’re actually doing this within your company or team.
[00:02:05] Then the actual five-step framework. And then also some more practical tips and tricks of what you can do after this presentation.
[00:02:26] For the first part, these might be some questions you get from your users, your managers, or even your customers. For instance, do you actually need to back up the data in Salesforce. That's, not even a question, but since Salesforce is cloud software, that's actually often kind of forgotten.
[00:03:00] Other questions are, do we adhere to the privacy and data retention rules? If not, how do we fix it? One that you can get from, for instance, your managers or users; why do all my reports show garbage, like, you know, what's the deal there? And they'll come to you as an administrator, architect, consultant, or other data steward.
[00:03:29] This is our data steward, Alex. The role is responsible for the enterprise data now in our organization and helps you with these four main areas. Identify the focus areas for your data management program. Evaluate the data needs that your business has.
Assess KPIs and then plan and execute accordingly. And of course, to then monitor the impact of all the processes on your data.
[00:04:17] Of course, we're talking about data quality management. but this is to show where it actually fits in with all the other major building blocks of information strategy. There’s enough content available online per building block to fill several days of webinars and training sessions.
[00:04:43] Today we zoom in on the data quality management and a bit on master data management. But I do encourage you to actually dive into each of these separately and don't just focus on one of the blocks.
[00:05:00] Then the second part, why the data quality matters. So what you see here on the left hand side, that's often how companies start. You have your operations departments, who have their own data and tools. Sales has to run their own tools and also manufacturing and so on.
[00:05:28] So it's really siloed per department. As your company grows, you go more towards an enterprise view of your data. With large enterprises, they're better served by a master data management. One is a platform instead of separate tools.
[00:05:59] This enterprise tool will be sort of the gold standard for each department who is pretty much like a subscriber to the whole MDM, master data management, platform. The MDM provides a consistent context for the consolidation of your data.
[00:06:26] Now, one of the things that could also impact your data quality is legacy systems. Every company has them. Even if you're five weeks old, there's already some legacy stuff happening, usually in the form of Excel sheets. You could easily have like seven, 10, 30 different data sources, which then of course, causes the different business units . . .
[00:06:52] Sales, they're not talking to marketing; operations is not able to anticipate demand because they aren’t getting up-to-date information from sales teams. And the most important thing is that has an impact on your customers. You should ask yourself, how can you provide your customers with a seamless and personalized experience?
[00:07:28]How can you do that if your backend does not have the right access to the information. So this is not just an internal exercise.
[00:07:41] There are a few different processes that can impact your data quality. I won't go through all of them. but for all this, together you can choose, Your class, everything, how complete is it? How accurate is it? Is this all the data we have?How relevant is it?
[00:08:04] You can score that. If you do that just across the board here with your entire data set, you get a score. And of course, firstly it's Harvard and benchmark and you'll see the score will all change constantly. Because internal processes are changing, tooling is changing because these days, which tool lasts longer than five years in your organization?
[00:08:41] These processes will help dictate the quality of your data over time. Sometimes it could also be the lack of appropriate skills within teams or your company. So I'm back to the data quality score. Once you have that, you measure it, you can improve it and you can work on that. Which you'll do. It's an iterative process. So you kind of just say, okay, today we have bad quality. After next week, we will have good quality data. it's something that you evaluate over time.
[00:09:37] Of course it's good to map out which processes impact your data the most at the moment.
[00:09:48] And then the actual framework. This will help you with the iterative increases to your data quality. As you can see, this might remind some people of the PDCA cycle - plan, do, check, and act. There's five steps here. Data profiling is at the top to start with. So you profile and analyze your data so you know where you're starting.
[00:10:22] Then you start cleansing it. So you identified the different anomalies within the profiled and analyzed data. Then you standardize and normalize your data. I'll dive into all these separate topics in the next few slides. Once we've done that, of course, you can start matching and merging.
[00:10:53] It doesn't stop there. I just keep going, monitor it. Keep your score in mind or keep an eye on it. And then repeat.
[00:11:10] So just start with the data profiling. Don't try to boil the ocean. That's the main take away from this slide. Perfection is really, really difficult to achieve. You just want to improve over time. First you start grouping a sample of your records and then you find what was good and what's bad.
[00:11:43] Then you start grouping them. Once you've done that, focus on prevention tactics for the point of entry of your data. What are the different channels and tools when you're done with that comes in; keep that in mind.
[00:12:17] Once your good data and your bad data, then focus on formatting issues. For instance, you have your dates or postal codes, legal values. Start with those to do the first cleansing. They're usually the easiest.
[00:12:50] This is the part where your data story, and Alex plays a quite a big role, involved in the business in actually identifying all these rules. What do we actually class as bad data and good data? Make those rules and stick to them. Of course, in the next cycle you can improve those.
[00:13:22] Then standardizing. The legal name of your customers and business partners. Often a company has a name. Internally it’s known as something else. Maybe if you look at Chamber of Commerce details, there’s a different name. Make sure you standardize the easy thing;, the website, the name, or the country.
[00:13:57] Doesn't matter if the data quality is good or bad. Standardize this and then split it again to good and bad.
[00:14:13] Then matching and merging is the fourth step. This is when we decide who should survive the battle of the duplicates. It’s a question that will constantly pop up and you have to duplicate records. Which one wins? It's a few steps. Identify them, the duplicates, determine what should be consolidated.
[00:14:47] So you have an account, business account, all these fields. One, which parents are actually going to be involved in the consolidation process and then build survivorship rules. That’s the decision of which one survives. Make sure you can automate these rules as well.
[00:15:11]Because if you have really super complex rules and then only one person understands it’s never gonna fly. If you can explain it to someone who can automate it, for instance, in Salesforce, that’ll really help. Don't have to explain it, just let the system do the work for you. And one thing that's often overlooked, dependent data.
[00:15:8] So you have a company or a contact person, the customer, and there's email communication, there's opportunities, cases, everything. What's going to happen with that? Make sure they actually get reparented. if you merge two companies, in all the new cases, opportunities, emails should go on the new winner. There’s two types of matching, deterministic and probabilistic. Deterministic is ideal. it's a hundred percent exact match with everything. That's the goal. But yeah, sometimes you just have to go, search for the duplicates that are not a hundred percent duplicate.
[00:16:36] So keep that in mind. In my experience it’s less than a hundred percent probability that something actually is a duplicate.
[00:16:54] Then the monitoring part. You create a profile of the data at the beginning, just to understand your current state of the quality. Then you took actions to normalize, standardize, match, and improve it. Make sure you keep monitoring the quality.
[00:17:27] I won't go through all of these details. One thing I do want to highlight is to make sure your data steward is not alone in this. While Alex looks like a powerful person, but managing your entire enterprise and all the different related companies and stakeholders is just not realistic.
[00:17:56] I started doing something that's called tribal stewardship. Have people from all different departments and areas in your business. The key business users involved in this process. And if they're not involved in the first iteration, involve them in the next one or the next ones after that.
[00:18:27] Now these are the five steps, but there's this additional step, backing up. But you mentioned Salesforce doesn't actually back up your data for you in a way that you can easily contact them and say, “Hey, I deleted this case. Please send me the backup.” You're responsible for that yourself also. For large organizations or old ones, make sure you have some time for our archiving strategy as part of your information strategy. Your business rules change; your quality rules change over time.
[00:19:16] A chunk of data that's maybe 10, 20 years old, or sometimes even three years, that's already old, might not be relevant anymore. You don't need it, but don't want to delete it. Archive it somewhere. Then the last part, after you've heard all this, what are you actually going to do?
[00:19:40] The first step could be building a dashboard. There's actually out of the box dashboards available. Or some tools that can help you with this. For instance, what this dashboard shows is her objects, for instance, accounts or opportunities. It shows all these fields that are mentioned here, like your name or your address details.
[00:20:08]How many times is it actually filled out? Of course it will be perfect if everything's always filled out, a hundred percent, but we all know that's utopia. Then once you've done that, there's a lot of different tools out there on the AppExchange that can help you with your data management. There are a few links in here to help you with that.
[00:20:48] Then some additional resources. For instance, on Trailhead, it's like an online learning platform, you have a few modules specifically about this topic. There’s a success community. You can get in touch with Salesforce experts and other customers and partners. And of course there's also the face-to-face or in person events, like user groups in my community conferences.
[00:21:37] Then the last slide, because you’ve subscribed to this presentation, I'm allowed to give away a free workshop that's dedicated to our virtual workshop where we will help you create a solution for your most challenging Salesforce problem. And that could be in any area.
[00:21:48] For instance, setting up your master data management strategy, building a business case or roadmap for Salesforce implementation improvements, help with adoption or performing health check. All those kinds of things. The link will be sent to you after the session. They will also have my contact details. I'll be happy to go more in depth on some items for areas to help you with. It starts with the information strategy or the lack of quality management. And with that, I'd like to say thank you very much.
[00:22:37] Moderator: Thank you. Andre. I have a couple of questions here. You answered most of them with one of your slides there. You had a really good slide on the different tools you would use to clean up the data. I know I'm familiar with demand tools as an example. You started out talking about the problem of having silos.
[00:22:55] Q: Do you find that a lot of your customers are ending up using Salesforce as a single source of truth? And therefore concentrating their MDM efforts in there. Or is Salesforce still on the sidelines, just the sales system?
[00:23:17] van Kampen: What I see is that in a lot of companies, “OK, we now have Salesforce, let's use it across all the business units.”
Often as the first tool that does that. And often they'll then say, if it's not a Salesforce, it didn't happen or doesn't exist. We do see that quite a bit. Yes.
[00:23:48] Q: The data quality dashboards, I know that there's the built in Salesforce ones, but some of those tools in there have other scoring metrics that are a little more sophisticated than just Is the data filled out. They can do things like look for, for instance, are the abbreviations in addresses standardized and so forth. I would imagine that'd be something that you would want your MDM team to do too. Right. Run these tools that can get a dashboard based on how much cleanup is needed in your org in addition to just missing data.
[00:24:34] van Kampen: Yeah, indeed. The dashboard I showed is really just a starting point. Like if you have absolutely nothing, it's a good start. Just to get a feel of one of the easy things to fix.
[00:24:54] There are tools that often have integrations with, I think it's called webservices.com, or like a chamber of commerce integration where you can indeed do that. If, say you started entering company data or contact data in Salesforce, once you click save, it will then contact the database and, do a check for you and maybe even make suggestions. Like if you have a chamber of commerce reference number, “Is this the company?” And then you have a whole list of more data. There's tools like that out there.