Back to Blog

Data Quality Dimensions Marketers Should Emphasize in Decision Making

Last updated: September 19, 2019

Data Quality Dimensions Marketers Should

According to the recent Forrester report, Why Marketers Can’t Ignore Data Quality, commissioned by Marketing Evolution, leadership teams across all industries understand that high-quality data is the top factor driving successful marketing performance. By analyzing high-quality data, marketing teams can uncover market insights and gain a more comprehensive understanding of their target audience via marketing models. With these models, marketers can segment audiences, identify behaviors and brand interactions, and outline inefficiencies in existing tactics – enabling the creation of powerful, well-informed campaigns.

On the other hand, poor data quality can hinder an organization’s ability to get accurate, actionable insights that drive results. This leads to misguided marketing models, inaccurate customer targeting, and increased customer attrition – which damages media planning initiatives. According to our report with Forrester, it’s estimated that the average organization wastes 21 percent of their media spend due to bad data. This adds up considerably over time, with the average midsize firm standing to lose $1.2 million per year due to low-quality data, while the average enterprise wastes a whopping $16.5 million.

Since data quality can make or break marketing campaigns - providing a multitude of benefits or drawbacks – it shouldn’t be surprising that 82 percent of marketing leaders put improvements to marketing and media data quality as a high priority, according to our report with Forrester. However, these marketing leaders are still struggling to optimize their data due to challenges with data volume, data integration, regulatory environments, and customer privacy. 

To remedy these challenges, marketing teams should evaluate a number of key data quality dimensions. By breaking down data quality into criteria that’s easy to digest, your organization can systematically tackle data quality – unlocking excellent data to fuel your organization’s next initiative.

The Seven Data Quality Dimensions

Organizations that have been striving to obtain high-quality data may have already realized that attaining perfect data quality is simply not realistic. Instead, organizations should prioritize seven data quality dimensions that have been shown to encourage a greater return on aspects such as potential customer lifetime value, brand value, and overall operational efficiency. 

By breaking data quality down into these categories, organizations can take a realistic, constructive approach to data quality. Let’s take a closer look at each data quality dimension, as outlined by Forrester.

Timeliness

High-quality data should be sourced within a reasonable timeframe, which will be determined by key stakeholders at your organization. For example, an organization may decide that all demographic data must be sourced from the past five years, and all lead data must be sourced within the past six months. To get timely insights, organizations should consistently collect new data while enacting data hygiene practices to avoid relying on old, irrelevant information in marketing models.

Completeness

When evaluating the quality of a dataset, ensure that every necessary attribute and field is filled in on the data record. These necessary fields should indicate information about a consumer’s behaviors, intentions, permissions, and sentiments. This data should be captured with the intention of providing a more pointed and robust analysis – such as understanding channel halo effects or how customers feel about your brand.

Consistency

Consistent data follows a common taxonomy regardless of platform, channels, and campaigns. To ensure consistency in data, try to attain standardization via methods like campaign codes and customer identifiers. By labeling data consistently and correctly, organizations will speed up their data collection process and have the ability to more effectively analyze trends over time.

Relevance

Data should be selected with a specific, overarching goal in mind – like answering business problems, addressing questions about customer behavior, or just informing day-to-day marketing decisions. If data isn’t selected for relevance, your organization’s analysis will either be skewed, or simply not useful for solving the problem at hand.

Transparency 

When sourcing data, ensure that the origin of the data is easy to trace and identify. This will help prove its reliability, while uncovering necessary nuances for certain data types. For example, first-party data is likely more reliable than third-party data – and should be subject to a different level of skepticism in analysis.

Accuracy

Make sure that the data your organization collects reflects the reality of customers in your market. Without accurate data, your organization will have difficulty conducting a relevant, actionable analysis. To ensure that data is accurate, be sure to source it from a reliable vendor. If necessary, acquire additional datasets that can corroborate the data you’ve already found.

Representativeness

Representativeness ensures that the data used for insights is accurately reflective of the marketplace or an advertiser’s targeted audience. This means getting unbiased data – to get a frame of reference as to what unbiased data looks like, try to pull data from independently published studies. While your data and their data do not need to be perfectly aligned, there should be some consistency between the two.

While fulfilling all seven of these categories is a formidable challenge, organizations that begin a data quality initiative will likely enjoy a significant competitive advantage. According to our commissioned Forrester report, only 33 percent of marketing leaders meet at least a single one of these criteria, and only 9 percent of marketing leaders believe they meet all seven. The marketing leaders that do meet all seven dimensions enjoy significantly higher customer lifetime value, brand value, and operational efficiency. 

Getting Data Quality Alignment

Before a data quality initiative can be enacted, both upper-level management and the marketing department must understand the benefits of data quality. This involves positioning and explaining how data quality plays a key role in campaign success.

To get alignment from the marketing department, communicate how data quality results in more accurate and successful campaigns. When our report with Forrester evaluated organizations that properly adhered to all seven dimensions of data quality, 93 percent saw a year-over-year increase in the potential lifetime value of customers, and 92 percent saw an increase in brand value. If a proper approach to data quality is taken, then your marketing department can have confidence in exceeding KPIs through a more informed approach to media planning.

When appealing to upper management, try to illustrate the big picture of how data quality helps an organization. Explain how the average marketing team spends 32 percent of their time managing data quality – creating massive amounts of unnecessary waste. Then, point out that 89 percent of marketing teams with high quality data saw increased operational efficiency year-over year.

Once key stakeholders understand how data quality positively impacts their areas of business, they should be much more receptive to implementing the seven dimensions of data quality across the organization. 

Final Thoughts

Even if your organization leverages both people and processes to support the seven dimensions of data quality, it may still be difficult to collect and analyze data accurately and efficiently. As a result, many organizations choose to use technology that can scale with their data quality initiative. To ensure that data quality is taken into account across the entire data lifecycle, look for a solution with built-in marketing analytics as well as safeguards for data quality – this will ensure that your organization can properly organize and correlate data from disparate sources.

By giving marketing teams all of the resources and information needed to obtain high quality data, organizations will enjoy greater efficiency, a better understanding of customers, and an overall reduction in frivolous marketing expenses.

Want to better understand the impact of high quality on your marketing team? Read through our recently commissioned Forrester report, “Why Marketers Can’t Ignore Data Quality,” This report outlines the resources and information that marketing teams need to obtain high quality data - resulting in greater efficiency, a better understanding of customers, and an overall reduction in frivolous marketing expenses.

Written by Marketing Evolution