“Dirty data” is a term that describes any piece of data you collect that has issues or errors.
For many companies, most records in their database have some issues. A study of clinical data showed error rates as high as 27% in some data fields. This can mean inaccurate data, missing information, or data entry mistakes.
The question is not whether or not you have dirty data. Every database has dirty data. The question is how much dirty data you have.
Poor data quality is a serious bottleneck for organizations. It impacts customers at every step in the customer journey, including marketing, sales, and support engagements. Ultimately, data quality plays a pivotal role in the experience customers have and how they feel about your company.
A whopping 98% of companies use data to improve customer experiences, but nearly one-third say poor data quality is a key challenge. Clearly, organizations need to tackle their dirty data.
But dealing with a dirty data problem isn’t easy or straightforward. Once you dig in and begin to get an idea of the scope of your cleanup projects, often you’ll realize the task is much larger than anticipated. Companies commonly deal with data quality on an ad-hoc basis as problems crop up or in occasional big, inefficient cleanup projects.
Let’s dive deeper into dirty data, how it impacts organizations, and how to clean it.
What is Dirty Data?
Dirty data is a term that describes records that have erroneous data. That erroneous data can be outdated, incorrect, or obviously false data, such as a phone number like “1111111111.”
But, in our experience, dirty data is often used casually to refer to a range of data-related issues including, but not limited to, erroneous data. There are many examples of issues that are commonly included under the dirty data umbrella.
Dirty Data Examples
- duplicate data
- incomplete data
- incorrect data
- poorly formatted data
- incorrectly associated data
- low-quality data
- invalid data
- inconsistent data
- general data clutter
Often, what companies mean when they say “dirty data” is any data that has issues that impede business processes.
Ultimately, the employees that are executing tasks notice the data issues and pass their complaints up the chain. To management, the distinctions over what to call the problem are unimportant. They just know that they have data issues that need to be fixed, and “dirty data” is the perfect catchall phrase to describe that.
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Why Dirty Data Is Killing Your Growth
The impact of dirty data is often underestimated, even by companies that take data management seriously.
Data quality issues impact customers at every step in the customer lifecycle. When customers discover and begin to engage with your brand, their collected data impacts the marketing materials they receive and how personalized those materials are. When they engage with sales, their data impacts the materials they receive, their conversations, and how well sales reps can connect with them.
Even after they become customers, dirty data does not stop negatively impacting their experience. Data quality issues can hamper customer support conversations, depriving them of the context and personalization that makes for a great experience. Then, the ongoing marketing materials that customers receive may miss the mark, impacting their lifetime value.
At the macro level, it’s hard to quantify the dollar value impact of dirty data. But the monumental impact becomes clear when you examine how data quality bleeds into every touchpoint in the customer journey while impacting your internal processes.
Here are a few specific examples of how dirty data can hurt the customer experience and hamstring internal processes.
- Slows down your teams. When your employees need to comb through duplicate records or look to external sources to confirm data, it slows them down and hurts efficiency across your organization.
- Breaks confidence in data. When your teams can’t trust your data, they will either avoid using it or use external sources, hurting the effectiveness of your campaigns and grinding processes to a halt.
- Makes marketing less effective. When your data is unreliable, your marketing teams will avoid using it, rendering your marketing targeting less accurate and personalization less sophisticated. This makes every marketing dollar that you spend less effective.
- Causes sales teams to step on each other's toes. Duplicate data, bad ownership assignment processes, and missing associations between contacts and companies can cause double assignments and other mistakes in your sales processes.
- Leaves sales reps missing context. When your data isn’t reliable, your sales reps will avoid using it in communication with prospects. This results in a less personalized experience and impacts their ability to connect.
- Makes providing effective support difficult. For support teams to truly understand and deliver on a customer's needs, they need an accurate record of a person’s previous engagements with a company.
- Impacts top-level decision-making. Every organization strives to make decisions backed by data. But when that data is dirty, how can you have confidence in your decisions? They could be based on a false premise.
The impact of dirty data reverberates throughout your entire organization. Clean data is imperative to growth.
How to Clean Dirty Data
Luckily, companies have many options when it comes to cleaning their dirty data. However, data management isn’t easy. It takes a solid commitment to go from a chaotic data situation to an optimized one.
But before you can clean your dirty data, you must audit your customer data to understand where your issues lie.
Audit Your Customer Data
Often, auditing your customer data is in itself a big project. Only with the ability to identify specific issues in your database, such as the number of first names that are improperly capitalized, can you understand the scope of your issues and prioritize fixes for your most important issues.
Internal feedback can be a great way to spot data issues that are gumming up the gears in your team’s processes. But, some voices are louder than others. Understanding how widespread the issues are throughout your database allows you to define your data cleanup strategy issue by issue. Without that information, you are throwing darts blindfolded.
But identifying specific issues can be complicated. It is unlikely that you will be able to put together an Excel formula to identify and fix every issue. In most cases, you’ll need to use internal development resources. Even then, you are likely to miss a range of different issues that are difficult to isolate programmatically.
With a solid understanding of where your data issues lie, you can then begin to devise solutions for cleaning that data. There are multiple data cleaning methods that you can use.
Data Cleaning Methods
There are five different roads that companies typically take for their data cleanup projects. Each has its pros and cons.
Employees often resort to by-hand updates to fix data issues. You might find a marketing manager sifting through a CSV to ensure that all of the first names and industries that they reference in an email campaign are accurate and consistent. Or, you might have sales reps reformatting phone numbers by hand to ensure that the records play nice with their sales auto-dialing system.
And while some ad-hoc manual updates from employees are necessary and unavoidable, cleaning an entire database manually is unrealistic.
Let’s say your database has 100,000 records, a very modestly sized CRM database for a midsize company. Each of those 100,000 records might have 25 relevant data fields, which is also a conservatively estimated number. To check and clean the entire database would mean checking 2.5 million individual fields by hand. By the time you complete this task, new data with issues will have entered your database, creating a never-ending situation that is impossible to keep up with.
Excel is a staple tool for data management. And there are many dirty data issues that can be easily identified and fixed directly in Excel using advanced formulas and functions. But performing data cleanups through Excel formulas is not always a simple task.
First, you have to filter the data in your CRM, which is not always as easy as it seems it should be, before exporting it. You’ll never want to export your entire database and open it up in an Excel file, because with hundreds of thousands of records Excel will be slow to load and prone to crashing.
Once you’ve exported a segment of your database as a CSV, you have to open the file up in Excel, choose an issue to correct, then design formulas to identify and fix that issue. Often, this will require two separate formulas. Building these formulas is often not easy, and you’ll require the help of someone with a lot of experience using Excel formulas to tackle more advanced issues. Assuming that you are able to design those formulas, you then have to document them for future use. Some issues may be too complex for standard Excel functions and could require VBA programming.
Collaboration can also be difficult. Data cleanup projects are big projects, so you’ll need to manage assignments and coordinate sharing the most recent version of your CSV files.
Another common direction that companies take for their data cleanup projects is hiring consultants. Hiring data management consultants can be smart, especially when you don’t have the in-house expertise to tackle a full data cleanup project.
Additionally, consultants may also be open to helping with a portion of the project, such as auditing your existing data, designing solutions to problems, or helping to implement data management automation. However, there are some potential pitfalls that come with working with data management consultants.
First, consulting can be expensive, particularly when you work with known entities. It may not always make financial sense to bring in consultants to deal with data issues continually. Then there are privacy concerns. Your organization may not be willing to open up the entirety of your customer data to an outside entity.
Finally, hiring consultants starves your internal teams of the chance to build knowledge and understanding. Data management is a task that is never going to go away. In the long term, it may be better to train your internal teams or hire resources devoted to these tasks, rather than bring in consultants.
Enlisting developers is often required to fix issues unique to your organization’s data. This approach can be beneficial. Internal development resources can devise custom-coded solutions for identifying and fixing the most prominent issues in your customer database.
But there are some considerations that come with using developers.
First, they may not be too thrilled to be pulled from their current projects. As data management is an ongoing commitment, that may mean enlisting them quite often, which can hurt morale.
Additionally, building solutions to fix CRM data problems is never a one-and-done situation. The code and solutions developers build today will need to be maintained and updated to continue working, just like any other code.
Using a software solution specifically designed to help you identify, fix, and automate your data management is a natural choice for most companies.
Investing in a software solution helps you to keep costs down. Assuming that the software can solve most of your major issues, the time your teams spend devising and enacting solutions will be greatly reduced.
Additionally, using data management software means that you’ll be able to add automation to your existing data management processes. This ensures that identifying and fixing many of your most important data issues remains completely hands-free.
Software allows you to create well-defined processes. Over time, you can expand your usage of the software’s features to encompass more of your data management strategy and automate more of your manual tasks.
Ongoing Data Management
Once your database has been cleaned, you still can’t place data management on the back burner. New data with issues, some new and some old, is always flowing into your CRM database.
While it might be tempting to forget about data management and just wait for problems to become apparent again, it is cheaper in the long run to stay on top of data quality on an ongoing basis. Continuing to build out automation where it makes sense, defining processes, and limiting dirty data at the source is the only way to reduce the impact of bad data. Then your organization will always be in a position to deliver the best possible customer experience.
How Insycle Helps You Clean and Manage Dirty Data
Insycle is a complete data management solution, helping companies deal with their dirty data from the top down.
With Insycle, you can:
- identify and track data issues
- explore and analyze your data
- merge duplicate contacts and accounts using any field
- standardize and format records
- link companies, contacts, and deals
- declutter and purge bad data
- import data flexibly
- compare data using CSVs
- bulk-update, delete, and assign records
- streamline data corrections
- collaborate on data management
Insycle’s pre-built templates solve many common data problems.
When you start your free trial, Insycle’s Customer Data Health Assessment will analyze your database, and track common data errors on an ongoing basis.
In the Customer Data Health Assessment, you can click the review button to access a template to fix the specified issue.
You can also build advanced custom templates. For example, you can deduplicate your database using any field with a range of options for more precisely matching duplicates.
Insycle helps at every stage—from understanding your data issues, to fixing them, to implementing automated systems that will keep your database clean going forward.
Take Control of Dirty Data
Insycle is a complete customer data management solution that helps companies clean dirty data
But Insycle is more than just a tool for cleaning dirty data. It's a complete data management tool for taking control of your customer data and implementing data management best practices.
Insycle enables operations teams to fix dirty data issues in bulk and automate their data management processes. Without Insycle, the cost of bad data is a major blind spot for marketing and sales leaders and a roadblock for execution by their teams.
Want to learn more about how Insycle can help with dirty data? Click here to learn more.