What Does ‘Better Data Quality’ Mean for Marketers and How Can We Achieve It?

Data is central to everything we do in marketing, and it’s the subject of many well-known phrases. One of the earliest was “Data is the new oil,” highlighting how data was expected to revolutionize the way we create wealth, similar to how oil transformed manufacturing, transportation, and economies.

However, with that came another saying: “Garbage in, garbage out.” While data can be a powerful tool for marketers, its effectiveness depends on accuracy. Poor-quality data will only create chaos. To make marketing successful, it’s essential to focus on using high-quality data.

What Does Good Quality Data Look Like?

In marketing tools like CRMs, CDPs, and automation platforms, data quality means:

• Accurate: Correct and up-to-date information such as contact details, purchase history, and preferences.
• Complete: All relevant fields are filled with meaningful data.
• Consistent: Standardized formats for names, dates, and other details across platforms.
• Clean: Duplicate and irrelevant data are removed.
• Timely: Data is updated promptly to reflect real-time interactions.

How to Achieve Good Data Quality?

Getting to a state of high-quality data requires a step-by-step approach. Here’s how marketing professionals can improve existing data and ensure its future quality:

1. Data Assessment
   • Data Profiling: Analyzing data in each platform to understand volume, types, and quality issues.
   • Data Mapping: Identifying data flow between platforms and pinpointing inconsistencies.
2. Data Cleaning and Standardization
   Inconsistent data is common, and here’s how to tackle it:
   • De-duplication: Merge duplicate records using algorithms.
   • Standardization: Establish consistent formatting rules (e.g., date formats) and automate data cleansing.
   • Enrichment: Use third-party providers to fill in missing details and gain deeper insights.
3. Data Governance
   Once cleaned, keeping data pristine requires long-term management:
   • Data Quality Policies: Develop company-wide rules for data ownership, access, and quality standards.
   • User Training: Train marketing and sales teams on proper data entry procedures.
4. Data Monitoring and Maintenance
   Regular checks are necessary to maintain data quality:
   • Schedule Audits: Conduct periodic checks to address any emerging issues.
   • Data Quality KPIs: Track metrics like data accuracy and completeness to measure progress.
5. Data Management Tools
   You’ll need the right tools for efficient data management:
   • Data Integration Platforms (DIPs): Automate data movement between platforms.
   • Master Data Management (MDM) Tools: Centralize customer data, eliminating duplicates.
   • Data Quality Management Tools: Provide functionalities for profiling, deduplication, and cleansing.
   • Data Visualization Tools: Help identify trends in data quality.

How Data Quality Impacts Marketing Outcomes

Improving data quality leads to significant marketing gains:

• Increased Campaign Effectiveness
  • Improved Targeting: Accurate data allows for precise audience segmentation, leading to better campaign resonance.
  • Better Personalization: Complete customer profiles enable tailored messaging, boosting engagement and conversions.
  • Reduced Campaign Waste: Eliminating irrelevant or duplicate contacts saves money and improves ROI.
• Enhanced Lead Generation and Qualification
  • Better Lead Scoring: Accurate data leads to improved lead scoring models, helping prioritize high-potential leads.
  • Improved Lead Nurturing: Tailored nurture campaigns based on specific customer journeys enhance lead qualification.
• Stronger Customer Relationships and Insights
  • Improved Customer Experiences: Personalized interactions build stronger customer relationships and loyalty.
  • Deeper Customer Understanding: Clean data enables better segmentation and insights, informing future strategies.
• Increased Efficiency and Productivity
  • Reduced Manual Work: Data management tools free up time for strategic initiatives.
  • Improved Collaboration: Consistent data across platforms enhances communication within teams and between departments.

Data Quality: A Marketing Lifestyle, Not a Project

Like healthcare professionals talk about “lifestyle changes” instead of diets, data quality is a continuous process, not a one-time effort. Marketing teams must commit to maintaining data quality across the organization, ensuring the long-term benefits of clean, accurate data.

In summary, quality data enhances marketing efficiency, improves decision-making, and ultimately drives better business outcomes.