7 Common Types of Dirty Data and How to Clean Them

Using dirty data to fuel your business is like putting the wrong kind of fuel in your car: the engine might start, but you could be doing serious damage. If you want a well-oiled revenue engine, you’ve got to fuel it with clean data.

In this post, we tackle some foundational dirty data questions:

  • What is dirty data?
  • What are examples of dirty data?
  • What are the consequences of dirty data?
  • How do you clean and prevent dirty data?

Time to get messy.

What is Dirty Data?

Dirty data represents faulty bits of information that can present problems in a business’s database.

For B2B businesses using data to fuel successful sales and marketing efforts, having access to clean data is paramount. Data impacts critical go-to-market functions such as developing your ideal customer profile (ICP), territory planning, segmentation, lead routing, and sales prospecting.

What are the Types of Dirty Data and How do you Clean Them?

The following are common types of dirty data that negatively impact sales and marketing teams: 

1. Insecure Data

Data security and privacy laws are being established left and right, imposing financial penalties on businesses that don’t follow these laws to the letter. With steep fines for non-compliance, insecure data is quickly becoming one of the most dangerous types of dirty data.

Digital consent, opt-ins, and privacy notifications are the new norm in an increasingly consumer-centric business landscape. For that reason, non-compliance with privacy regulations like GDPR or CCPA end up costing organizations more in the long run if ignored. 

Industry giants know the real costs of ignoring privacy regulations. Amazon announced an $888 million EU fine in its 2021 earnings report due to data violations. WhatsApp, an application owned by Meta, also received a $271 million fine for alleged GDPR infringements. 

The consequences go beyond a price tag, as non-compliance negatively impacts company productivity, brand reputation, and disrupts business operations. 

How to remain within data privacy regulations:

Disorderly databases are the most likely candidates to house insecure data. There are several data hygiene practices you can implement to combat insecure data.

  • Delete outdated and unusable records 
  • Merge duplicates to prevent fragmented profiles
  • Automate lead-to-account linking
  • Consolidate your stack as much as possible

With a clean, organized and updated database, complying with data privacy regulations becomes far more straightforward.

2. Inconsistent Data

Inconsistent or non-standardized data looks different, but represents the same thing. Just like duplicate records exist in various places within your database, multiple versions of the same data elements can exist across different records in your system. 

How to standardize your data:

First, create standard naming conventions and ensure your organization follows them closely. As for existing inconsistent records, tools like ZoomInfo can normalize records in batches for more unified field names and more accurate segmentation.

Incorporating a data management tool that can standardize data from multiple sources helps create a centralized approach to data management. This enables data to be processed, analyzed, and leveraged across each department. Establishing a successful data-sharing strategy increases accessibility throughout your organization.

3. Too Much Data

Yes, data hoarding is a thing. And even though you won’t find yourself in a reality show for data hoarding, this often overlooked issue is a big problem in many organizations. 

Maintaining a sleek (but not small) database is a big part of data hygiene. It drives alignment between departments and improves accessibility throughout your organization.

How to reduce database size:

While it might seem like “too much data” can never be a bad thing, more often than not, a good portion of the data simply isn’t usable. This means that your team is spending excess time digging through the bad so they can get to the good. 

Data hoarding and outdated data go hand in hand, so you’ll find these two types of dirty data can be solved at the same time: ZoomInfo’s deletion features allows users to delete thousands of records at once.

4. Duplicate Data

In your CRM, duplicates are the doubling of information — for example, a single employee showing up twice under different companies, or with different job titles. They can show up in your prospect lists, contact data, and sales accounts. 

How do duplicates happen? Generally, you’ll muddy your data with copies during data migrations and manual inputs.  

Duplicates have no place in the system of any data-driven organization. Ridding your database of duplicates should be a top priority in any data hygiene campaign.

How to clean and prevent duplicates:

Before the age of mass data accumulation, manpower alone was enough to merge duplicates and link leads to accounts. Nowadays, there are automated solutions for detecting and merging duplicates. 

External solutions to de-duplicate data, like ZoomInfo OperationsOS, allow users to match leads, contacts, and accounts based on customizable criteria. This way, it prevents duplicates at all points of entry into your database. 

How to keep data fresh and up to date:

Purging your database of records created before a certain date can help expedite the process of cleaning outdated records. ZoomInfo’s deletion features let you bypass system restrictions, allowing you to delete thousands of records that have no business use.

5. Incomplete Data

Do you have data gaps? Any incomplete data will certainly poke holes in your outreach efforts. 

Without attributes like industry type, job title, or last name, you risk excluding valuable leads in your campaigns. Additionally, incomplete data hurts your sales team’s call-to-connection rate. 

How to fix incomplete data:

The first option to combat incomplete records is to manually conduct research to append the missing fields. But you will soon find that this strategy is neither realistic nor scalable. 

Enriching your data with a service like ZoomInfo before the lead gets handed to sales is the best way to automate the filling of empty fields and gain a more complete profile of targets and customers.

6. Inaccurate Data

If your data is plain wrong, you run into all sorts of problems — from missteps on cold calls to inaccurate reporting and decision-making:

It’s far cheaper to verify and cleanse data regularly than to do nothing at all.

How to clean incorrect and inaccurate data:

Keeping track of all data entry points and diagnosing the cause of inaccurate data is the first step. If the problem is caused by external data sources, such as web forms or connected systems, seeking an external solution is the best way to maintain accuracy.

Data enrichment software like ZoomInfo corrects mistakes and overrides dirty data with clean data sourced from the most reliable sources. By augmenting existing data with purchased third-party information, organizations can attain more accurate data that may not have been possible before. 

What are the Consequences of Dirty Data?

1. Ineffective Marketing Campaigns

Dirty data creates an inaccurate idea of your ideal customers and throws off your marketing efforts to target the right people. 

Inaccurate data skews your understanding of your target audience, which has a domino effect as it negatively influences your approach to each campaign. The effectiveness of your marketing tactics — in particular email campaigns — depends on accurate data. 

2. Poor Customer Experience

When bad data results in poor customer experience, you’ll lose out on valuable prospects and fail to retain current customers. 

The modern customer has more control over their buying journey than ever before. When they’re interested in buying from your company, they want seamless interactions. These interactions’ success depends on clean data. 

3. Damaged Brand Reputation

Dirty data can hurt your company’s reputation in more ways than simply encouraging negative customer feedback. 

In today’s hyper-connected world, customers don’t just abandon your business when they have a poor experience. They tell their friends, family, and colleagues about it. Instead, build your brand on reliable data and keep hard-earned customers coming back. 

4. Misinformed Decision-Making

When bad data contaminates your sales and marketing metrics and reporting, it can hurt your business on a massive scale.

In the past, executives and key stakeholders relied on instinct and intuition to make important long-term business decisions. Now, clean data provides decision-makers with the tools they need for accurate and comprehensive reporting.  

5. Misaligned Sales and Marketing Teams

Dirty data makes marketing and sales alignment difficult — and lead generation is one of the first initiatives to suffer. 

This means your marketing team will end up sending low-quality leads to sales. Over time, the relationship between the two departments fractures, leading to a decreased lead flow and fewer conversions.

To ensure that marketing sends the most qualified, ready-to-close leads to sales, your teams need data they both trust.

6. Decreased ROI from Sales and Marketing Technologies

Bad data prevents your technology stack from operating at its full potential. When you invest in marketing automation and CRM platforms, you do so to improve the effectiveness and efficiency of your sales and marketing initiatives.

Having access to a constant stream of new data that can provide a comprehensive view of your customers, is critical for meeting your sales and marketing goals.

7. A Slower Sales Cycle

Your dirty data will create roadblocks throughout the sales cycle. That includes poor lead management, with reps contacting high-quality leads too late — or sometimes, not at all. 

This slows down leads moving through the sales process. And as a result, good leads go bad and miss opportunities. 

With ongoing data hygiene, your teams develop faster, more efficient sales processes to ensure every lead touchpoint is phenomenal.

How do You Prevent Dirty Data from Entering Your CRM?

Begin with regular CRM health assessments. You can do this manually or partner with your data provider. 

  • Use a good mix of data sources — first-party and third-party, including intent data. 
  • Cleanse your data regularly and fill in any gaps by enriching each field with the most reliable source possible.
  • Practice ongoing data management.

The key is to identify the types of bad data in your CRM, clear them out, and replenish them with a stream of high-quality, actionable data.