What does data quality mean?

Published Jun 20 2024 6 minute read Last updated Jun 25 2024
data quality
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  • Sean Dougherty
    Written by Sean Dougherty

    A copywriter at Funnel, Sean has more than 15 years of experience working in branding and advertising (both agency and client side). He's also a professional voice actor.

Quality is so important, especially as a marketer. Your target audience wants quality content in the form of blogs, articles, or business resources. Likewise, your team needs quality tools to do their job — platforms, SaaS and telecom solutions to reach clients or customers wherever they are.

You also need quality data to help drive the best possible marketing campaigns. But what does data quality mean?

There's no single metric that qualifies data as "quality," but there are plenty of best practices you can put in place to ensure that business data is top-notch. By understanding what data quality is and why it matters, you can drive that high standard into all your marketing efforts.

What is data quality?

To really get into what data quality means, we've been lucky enough to corner Robert Åman and Tomas Hermansson, Analytics Engineers from Funnel's business intelligence (BI) team. So, let's start with the first and most obvious question: What is data quality?

They both agree, "It describes the quality of the data you are working with. There are different parameters that influence or impact data quality."

Some parameters that help ensure data quality include:

  • Uniqueness
  • Accuracy
  • Freshness
  • Completeness
  • Consistency
  • Validity

Let's take a look at each of these parameters in more detail.

Unique data

For data to be useful, it must be unique. If your data storage solutions are filled with twenty copies of each client's address details, this is a waste of resources. Similarly, if sales figures are duplicated, this could artificially inflate results and skew the analysis of business success. This is problematic for many reasons, but primarily because you could inadvertently direct your budget in a way contrary to your goals. Unique data helps prevent this, and it's possible with effective data integration and collation tools.

Accurate data

It stands to reason that business data must be accurate. If duplicated data can skew results, imagine what data that's simply wrong could do. Ideally, businesses should have systems in place to protect data integrity and accuracy. However, mistakes do occur, particularly due to human error.

For example, a new customer might accidentally add an extra digit to their phone number when filling out a contact form. That detail is saved in your customer relationship management (CRM) system and possibly other connected databases. What was a useful lead is suddenly useless because you can't contact that customer.

Fresh data

When you go to the grocery store, you don't want to find moldy oranges and stale bread. Similarly, how can you make some delicious marketing material with out-of-date information? If your current data is telling you that your top blog posts are technical tutorials, you might make hundreds more. However, it turns out that the data was old, and the top blogs are actually human interest pieces. Suddenly, your blog readership has gone elsewhere — all due to bad data.

Complete data

As well as being accurate and up-to-date, data must be complete. Any gaps in the data can create poor business analyses and reduce the potential for effective decision-making. Working from scraps of information is difficult, time-consuming and a prime example of why data quality is so very important.

Consistent data

Good-quality data should be represented in the same format across multiple systems. Data mapping and data transformation can help ensure consistency. Without consistency, you could face challenges that make it impossible to merge or change data as needed. One example of this is the status of customer accounts. If a change is made in your CRM, it should be reflected in other systems. If the data formats are incompatible, you might see the customer as "active" in the CRM but "inactive" in a connected payment system.

Valid data

Similarly, data should conform to specific rules or constraints set by data managers or the systems it's stored within. For example, all dates should show as a date format, not a random string of characters. Microsoft Excel users, we know you all feel this in your soul. Correct formatting and validation create better-quality data.

Why should companies care about data quality?

We asked our BI experts why data quality is so critical. Robert says, "In order to draw the right conclusions from the data, you need high-quality data. Inaccurate data, or low-quality data, may lead you astray."

Tomas adds, "Conclusions can be wrong even if you have the right data. But if you have bad data, you will get a wrong picture of reality."

This is a really good point. Regardless of data quality, the humans analyzing it can still come to different conclusions. Let's head back to that grocery store. You still want oranges, and so does your friend. You go for several small, sweet-looking fruits. But your buddy wants larger, paler oranges, as they might last longer. Neither of you is exactly wrong, but you've interpreted what's in front of you differently based on your own goals.

And (this is important) if some of those oranges were apples, you'd have made some pretty strange marmalade with them.

In other words, you can interpret data differently, but if it's the same data, at least you're working with an accurate foundation. But when the data itself is poor, there's minimal chance of you reaching a fair conclusion.

Robert goes on to say, "When a company has low-quality data, the people working there will at some point stop trusting the data - and even the people working with the data - altogether. That's a big risk"

Conversely, accurate data helps organizations get valuable insights about the market, their customers and other important functions. Quality data is a vital ingredient for business success.

And for marketers, specifically

Poor quality data can cause you to spend your budget for ads on the wrong channels. Let’s look at an example. 

Let's say data from some platforms is imported daily, while other data is imported weekly. Your figures are for different periods of time and don't give you an accurate portrayal of what's happening. You might end up dedicating more of your budget to Facebook when you should actually direct the money to YouTube.

Robert noted, "Poor data quality has another risk for marketers. Imagine mixing cost data from two different data sources. If one of the cost metrics is in Euros, while another is in Dollars, you can not simply add them together. You will need to perform some normalization first."

Marketing costs, impacts, reach and the details of your target audience are all forms of data that you need to be accurate, timely and consistent. Without high-quality data, you're strolling around the grocery store grabbing products in the dark.

Examples of high-quality data

It's almost impossible to give specific examples of what constitutes high-quality data because companies' data stacks are not publicly available.

But in general, companies can promote high-quality data by:

  • Using the right data quality tools and platforms
  • Automating processes to avoid human errors
  • Ensuring processes are in place to collate all relevant marketing data
  • Centralizing data management
  • Having business processes in place to deal with anomalous data and errors

Accurate, consistent, complete data means you can rely on your reporting and analytics tools to give you insights that you can run with. You can also schedule data quality assessment sessions to spot-check for errors.

Examples of poor data quality

Managing data quality means knowing how to spot data quality issues. Some examples of poor-quality data include:

  • Duplicate entries that skew analysis, results and reports
  • Data formatted in such a way that it's hard to understand or merge
  • Gaps in data
  • Data logged over mismatched time periods

An incomplete picture means any business and marketing decisions you make are not fully informed.

Data quality management

Businesses can implement data quality management (DQM) to ensure data integrity. DQM is a set of practices, tools and strategies that aim to keep data accurate, consistent, complete and timely throughout its lifecycle. Data management is critical because good data is essential for good decision-making. Imagine trying to make important choices based on information that's full of errors, missing pieces, or inconsistencies — not a recipe for success!

Robert says, "In our BI stack we have some automated tests to check if the data import is correct. The first time we get our hands on a data set, we do a manual test. And then when we build models to automate the processing of the data, we build automated tests to check the data quality."

Automation is a great way to help reduce human errors and ensure data quality. So are the following six steps.

6 tips to improve data quality

1. Define data quality standards

Start by clearly outlining what "good" data means for your organization. This includes factors like:

  • Accuracy: Is the data correct and free from errors?
  • Completeness: Does it contain all of the necessary elements?
  • Consistency: Is it formatted the same way and free from contradictions?
  • Timeliness: Is the data up-to-date?
  • Relevance: Does the data align with your business needs?

2. Validate data at the source

Don't allow errors in the first place! Implement strict data entry controls and use validation rules (like drop-down menus, input masks and format checks) to catch inaccuracies where the data is initially created.

3. Perform regular data cleansing

Schedule periodic data cleaning processes to remove duplicates, correct inconsistencies and fill in missing values. This will keep errors from accumulating over time. (Read more about data cleaning here.)

4. Standardize data formats

Ensure that data is entered and stored in a consistent format. This prevents compatibility issues, reduces errors during analysis and makes data sharing more efficient.

5. Use data quality tools

Invest in specialized data quality software. These tools can help you automate many of the processes involved in data cleansing, profiling and monitoring, saving you time and improving accuracy.

6. Foster a culture of data quality

Make everyone in your organization aware of the importance of data quality. Emphasize data accuracy as a core value and train your staff in best practices for data collection, entry and management.

With the right data governance and data quality processes in place, marketers have all the ingredients they need for growth, increased brand visibility and business success.


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