What Is Data Completeness? Why One Missing Piece Matters

Did you know that incomplete data is inaccurate data? Here’s what data completeness means and why it matters for marketing.

Key takeaways

  • What is data completeness?
  • Why is incomplete data bad data?
  • How to ensure data is complete
  • Using a reliable source like BDEX

Poor data quality continues to be one of the biggest challenges for marketers. With the wrong information, you miss opportunities to connect with customers, waste precious time and money, and can even do lots of brand damage. 

In fact, research shows that poor data quality could be responsible for $15 million average annual losses for organizations and cost the U.S. economy an estimated $3.1 trillion each year.

So, what makes data quality good or bad? There are six key characteristics of quality data

  1. Accuracy: Is the information false?
  2. Completeness: Are there gaps in the data?
  3. Reliability: Is the data contradictory?
  4. Timeliness: Is the information up to date?
  5. Relevance: How applicable is the data to what you’re trying to do?
  6. Availability: Is the data accessible to those who need it?

While each factor is important, data completeness is one of the most common challenges in the world of big data. Without all the information you need, your marketing efforts will be all for naught. 

Let’s take a look at what data completeness means and how just one missing piece contributes to poor data quality and hurts your marketing performance. 

What is data completeness?

Data completeness refers to the comprehensiveness or wholeness of the data. There should be no gaps or missing information for data to be truly complete. Sometimes incomplete data is unusable, but often it’s still used even with missing information, which can lead to costly mistakes and false conclusions.

Incomplete data is often a result of unsuccessfully collected data. For example, say a name and email address was supposed to be gathered, but there is no associated email address when the information is imported into your systems. This can happen if your business is gathering information from a survey or gating content in an attempt to get prospect contact information. 

Consumer data isn’t complete unless all the available data is successfully gathered and stored properly. Completeness of data works in conjunction with the other data quality characteristics, too. For example, incomplete data can lead to inconsistencies and errors that impact accuracy and reliability. 

But it’s helpful to dive deeper into why incomplete data is so detrimental.

Why is incomplete data bad data?

Incomplete data only gives you partial information. Many times, having only a name, for example, and no other identifying information doesn’t help you target people effectively. Missing data entries mean you’re losing the potential to contact or follow up with leads who showed interest in your brand. There are thus tons of missed opportunities when you don’t have all the data you need.

What’s more, the business conclusions you draw from data are dependent on having all the necessary information. Incomplete data can lead to inaccurate assumptions that waste time for your team, waste money on campaigns, and waste effort when you have to fix major problems down the road.

These challenges are likely why poor data quality is a top reason many businesses hesitate to use analytics in informing decisions, as Gartner found in their latest Marketing Data and Analytics Survey. If organizations cannot depend on the completeness and accuracy of data, they’re reluctant to use it at all or employ a high level of analytics to process data for valuable insights. 

Only with high-quality data can the opportunities that data analytics provide companies be fully realized. 

6 steps to ensure data is complete

It’s clear that you need complete data to round out your data quality strategy. To prevent incomplete data from infiltrating your systems, take these six important steps:

  1. Determine which information is critical: Sometimes, when you are using a form to gather data, not all of the fields are necessary for data to be complete. Identify which fields are critical to accomplishing your goals. 
  2. Make certain fields required: Once you know which data you really need, like phone numbers, you can make those fields required so people have to enter something to complete an action (like downloading your content). 
  3. Use data profiling techniques: You can analyze your data sets via data profiling, which helps identify missing values, errors in the data, and inconsistencies.
  4. Assemble a data quality team: Have dedicated staff members who are actively responsible for maintaining the completeness and accuracy of the data your business depends on to make decisions. 
  5. Use automation and AI technologies: Automation and AI help data managers and marketers ensure data quality like never before. AI helps you identify duplicates, speed up data entry, standardize data, and detect anomalies. Automation can also help you control and sort through incoming data to instantly analyze information for completeness.
  6. Use the right data source: Remember to only use trusted data sources that you know prioritize quality, accuracy, and completeness. 

These simple strategies will help you keep an eye on the completeness of your data, leading to overall higher data quality for your business. 

Use a reliable source like BDEX

How do you find a consumer data source you can trust? Where can you get the most complete, accurate, and reliable data on the market?

Leave it to the team at BDEX. Our Data Exchange Platform (DXP) gives you more user data than any other platform, and we are committed to providing only the highest-quality information. We’ve implemented our BDEX ID Check that identifies and filters out bad data in our platform. We recognize that all data is not created equal, and we are always improving our approach to get rid of poor-quality data for good. 
To learn more about our commitment to data quality, contact the BDEX team.