What are the tools and techniques for assessing and enhancing your marketing data quality score?

1 Why MDQS matters

Your marketing data is the foundation of your marketing strategy. It helps you understand your target audience, segment your customers, personalize your messages, and measure your performance. However, if your data is inaccurate, incomplete, inconsistent, or irrelevant, you risk losing trust, credibility, and revenue. According to a study by Gartner, poor data quality costs organizations an average of $15 million per year. Therefore, it is essential to monitor and improve your MDQS regularly.

2 How to assess your MDQS

Enhancing your MDQS begins with assessing its current state. To evaluate your data quality, you can use tools and techniques such as data quality audits, which can be automated with software like Informatica or Talend to generate reports on issues. Additionally, data quality metrics such as accuracy, completeness, consistency, and timeliness can be used to measure your data quality across different dimensions and sources. Lastly, data quality scorecards such as the one provided by HubSpot can be used to track your progress and pinpoint areas for improvement.

3 How to enhance your MDQS

The second step to enhancing your MDQS is to implement data quality improvement actions, such as data cleansing, data governance, and data enrichment. Data cleansing is the process of correcting, removing, or enriching data to eliminate errors, gaps, duplicates, and anomalies. Data governance is the process of defining and enforcing data quality rules, policies, and standards across your organization. Lastly, data enrichment is the process of adding or updating data with additional information from external sources. Automating these processes with software such as OpenRefine, Trifacta, Collibra, Alation, Clearbit, or FullContact can help improve your data accuracy and completeness, ensure consistency and relevance, and enhance timeliness and value.

4 How to maintain your MDQS

The third step to enhancing your MDQS is to maintain your data quality over time. You can use various tools and techniques to sustain your data quality, such as data quality monitoring, data quality feedback, and data quality training. Data quality monitoring is the process of continuously checking your data for changes, errors, or anomalies. Data quality feedback is the process of collecting and analyzing input from your data users. Data quality training is the process of educating and empowering your data users on how to use, manage, and improve your data. Automating these processes with software such as Dataiku, Databricks, Qualtrics, SurveyMonkey, Udemy, or Coursera can alert you of any data quality issues, understand your data quality expectations and satisfaction, and increase your data quality awareness and skills.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?