Don’t Have Clean Data, Will Need Help

by | Jan 18, 2023 | Blog

If you’re a key stakeholder in a B2B business, you’ve probably heard a lot of talk about the importance of having data analytics if you want to thrive in the complex, Internet-centered business landscape of the 21st century. Good data can give you an in-depth understanding of the key behaviors in your customer base that can help you make better business decisions. It’s become the kind of thing that everyone just takes for granted as best practice now—we talk about it all the time on this blog. 

But how do you actually put data to use? How do you cultivate good, or “clean” data? And if you’re stuck with a bunch of “bad” data, then how do you fix it? Well, let’s consider. 

What Does Bad Data Look Like?

First, it’s easy to assume that more data=better data, and that collecting as much information on something as possible will yield the most opportunities for useful insights. But this isn’t always the case: sometimes you end up with a data set that’s just a messy mass of outdated information.  

  • For example, you might be running a rebate program. Usually, these programs can create a flux of really great data, but in so many different formats: text messages, emails, letters, photocopies, images. All this incoming data can lead your team with only enough time to process it and not without the time to do anything meaningful with it.  
  • Another example is if you use a contact relationship management tool (CRM), you might be holding onto data for every single person who’s ever given some kind of information to you for the past ten years. A lot of that information is clogging up your CRM with information that you probably won’t be able to use.  

Second, data can get really difficult to work with if it’s dispersed across several sources. Let’s say you want information on customer buying behaviors with respect to a specific product line you carry. The data that can help you get those insights might come from a lot of different places: 

  • Your CRM for customer information 
  • Your product management tool for information on orders and inventory 2
  • Your website analytics, if you do eCommerce 

If you have all three of these tools, that’s great! But depending on how you’ve set these tools up, it may be very difficult to match purchases and customers across them. If that’s the case, then you’ve got a problem. 

So, You Don’t Have Clean Data. What Do You Do?

If you just read all of that above and are starting to realize your data isn’t very workable, does that mean you have to throw all of it out and start again? Of course not. But, depending on what insights you’re trying to get, it might be more of a challenge. 

First, evaluate the data you have and how it was collected, so that you can identify its parameters and general shape. Is it a subset of a wider picture? Is it a duplicate of another source? Whatever the case, you’ll need to understand where it fits into your business. If you’re trying to make a major transition in your data management—like, for instance, if you’re trying to get your salespeople to adopt a CRM—you may run into some growing pains in shifting all of that data from a dispersed system to a centralized one.  

Next, identify what insights you’re trying to get with the data that you currently have. Maybe you just want to get a representative sample that shows the general metrics of your customers’ behaviors—if that’s the case, you’ll probably be able to get that from your current data set.  

But if you’re trying to find, say, an organizational profit margin across your whole business, you’ll need more extensive, clean information. If you only have clear data for one product group, for example, then that’s not going to help you get that insight. You’ll need to have clean information on: 

  • Pricing  
  • Costs  
  • Product groups 

You’ll need to track down all of the different sources for each of the product groups. We run into this challenge a lot with our clients. The data for different product groups is housed in different places, which can complicate the analytics process. The process of cleaning and reorganizing dispersed data is a tricky one and requires the help of an experienced internal team or third-party organization (like us!) to identify solutions.  

What Do I Need To Start Collecting Good Data?

On this blog, we’ve spoken before about the ways that an incentive program is a great way to capture data on your customer buying behaviors and needs, but how do you make sure you’re starting off on the right foot with data collection? 

  1. Work with a single data source if possible: as we’ve said, more data sources means a messier bigger picture, and more opportunities for duplicate data or data that conflict with one another. If you’re not working with a single data source, make sure that you have a way to connect data across all of your sources. Additionally, if you are working with multiple data sources, investing in a tool that will accumulate it and create a single source of truth across the sources might be worthwhile.
  2. Use a single sign-on tool like Salesforce for your incentive program to connect participant data with your CRM, which makes it easier for them to get started in the program and prevents data inconsistencies.  
  3. When considering what data your customers or participants are going to give you, ask yourself, “how could I use this information in the future?” Make sure you understand from the beginning why you’re collecting the data. Don’t waste their time by having them fill in information that you’ll never use—nobody likes filling out endless forms, and plus, it takes up room on your servers.  


Messy, “bad” data isn’t the end of the world, but working with it is a bit more labor-intensive. To get the clean “good” data that deliver valuable insights takes a centralized system for collecting and storing data, clear communication across your teams about what information needs to be stored where, and experts who know how to collect, curate, and analyze it.  


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