Data plays a crucial role in various aspects of business, particularly within sales departments, extending its impact across the entire company and the broader economy. In contrast to relying on intuition, businesses increasingly depend on data for significant decision-making processes, encompassing accurate forecasting, crucial business choices, and team feedback.

The significance of high-quality data cannot be overstated. Using unreliable information can have detrimental effects on productivity and morale, making it imperative to ensure the data's accuracy, freshness, completeness, uniqueness, and compliance.

In the realm of sales and marketing, where building a newsletter audience or creating a prospecting list is common, the need for reliable data becomes even more pronounced. The consequences of utilizing subpar data in such foundational tasks can result in decreased productivity and morale.

Surveying sales professionals revealed a unanimous desire for better data, with 30% emphasizing the importance of data for personalized conversations and 22% seeking more accurate contact information to initiate connections with leads. Relying on inaccurate data can lead to ongoing sales losses (52%) and reputational damage (49%).

To ensure the quality of data, these five fundamentals should be checked:

1. Accuracy

  • Obtain accurate data from reputable sales intelligence platforms rather than relying on purchased lists.
  • Minimize manual data entry to reduce human error, employing automated processes to connect CRM and sales tools.
  • Maintain data accuracy by regularly enriching it using tools like SMARTe.

2. Freshness

  • Regularly clean and update data to combat the natural decay of about 2.1% of the B2B database per month.
  • Connect CRM to tools like SMARTe to ensure continuous updates and additions to the database.

3. Completeness

  • Assess data quality by ensuring it contains all necessary information for informed decision-making.
  • Utilize reputable databases that prioritize completeness, providing comprehensive contact profiles.

4. Uniqueness

  • Avoid duplicate data, which can lead to confusion and waste valuable time.
  • Implement data enrichment to automatically clean up duplicated records in CRM.

5. Compliance

  • Acknowledge the legal aspect of data usage, especially when dealing with personally identifiable information.
  • Obtain data from compliant sources, such as SMARTe, to ensure adherence to regulations like GDPR and CCPA.

In conclusion, maintaining high-quality data is essential for maximizing sales productivity, preserving company reputation, and fostering positive team morale. SMARTe's data is a reliable source of accurate and global B2B data, offering data enrichment services to enhance CRM data quality. Book a demo to see it in action.

Nitesh Sharma

Nitesh is SMARTe’s Head of Growth Marketing. He writes on topics within B2B marketing and sales, providing readers with real life, actionable tactics.

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All your questions, answered.

What criteria define high-quality B2B data?

Good B2B data is characterized by accuracy, relevance, and completeness. Accuracy ensures that the information is up-to-date and reliable. Relevance means the data aligns with your target audience and business objectives. Completeness ensures all necessary fields are filled, providing a comprehensive understanding of each lead or prospect.

How does data enrichment contribute to superior B2B data quality?

Data enrichment enhances B2B data quality by appending additional information to existing datasets. This process fills in gaps, corrects inaccuracies, and adds valuable insights such as firmographics, technographics, and purchase intent. By enriching data, businesses gain a deeper understanding of their target market, enabling more personalized and effective marketing and sales strategies.

What role does data cleansing play in maintaining good B2B data integrity?

Data cleansing, or data scrubbing, is crucial for maintaining the integrity of B2B data. It involves identifying and correcting errors, inconsistencies, and duplications within the dataset. Regular cleansing ensures that the information remains accurate and reliable over time, preventing wasted resources on ineffective marketing campaigns and improving overall business decision-making processes.

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