The Best Practices And Pitfalls To Avoid When Buying Quality Datasets

When you purchase datasets from a third-party provider, it’s important to make sure you get the value from high-quality data for the price you pay.

There are certain factors that should be taken into account during the buying process to ensure that you get the most out of your investment. You should be able to do a careful assessment of data, know the best practices for data maintenance, as well as how to avoid common pitfalls. 

The aspects to consider when assessing data quality

Buying datasets may be quick and easy compared to going through the data collection process yourself. However, there are important aspects to keep in mind when you purchase data to ensure you’ve got the best deal and advantage.

• Define your quality standards. According to research from Garner, poor quality data lowers an organization’s competitive advantage. Establish your quality standards in detail before buying any datasets. What standard of precision, thoroughness, and timeliness do you demand? Your evaluation process will be guided by particular criteria.
• Research data providers. Conduct thorough research on the data provider’s reputation. Look for reviews, customer testimonials, and case studies to gauge their track record in delivering high-quality data.
• Request sample dataset from the chosen provider. Dun and Bradstreet’s Annual Marketing Data Report estimates that over 70% of B2B data is either incomplete or inaccurate. Always request sample data before making a purchase. This allows you to assess the dataset’s content, structure, and quality. Evaluate whether it meets your predefined standards.
• Data validation and cleaning. Inquire about the provider’s data validation and cleaning processes. High-quality datasets undergo rigorous validation to remove errors, duplicates, and inconsistencies.
• Documentation and metadata. Assess the dataset’s documentation and metadata. Clear documentation helps you understand the data’s source, methodology, and any potential biases or limitations.
• The diversity of data sources. Verify if the dataset draws from diverse and reliable sources. A well-rounded dataset is less likely to suffer from bias and inaccuracies.
• Ask about data updates. Timely data is more valuable. Ensure that the provider has a process for keeping the dataset current.

How you can maintain the quality of purchased data

Maintaining the quality of purchased data is crucial to ensure that it continues to provide accurate and valuable insights over time. Here are several strategies to help you maintain data quality effectively:

1. Smooth data integration into systems. Ensure seamless integration of the purchased dataset into your systems and workflows. Compatibility issues can lead to data quality degradation.
2. Regular data audits. Implement regular audits and quality checks on the dataset. Monitor for errors, inconsistencies, and any drift from your predefined quality standards.
3. Data enrichment. Consider enriching the dataset with additional data sources to enhance its completeness and accuracy. Be mindful of data integration challenges during this process.
4. Data governance. Establish data governance practices within your organization. Clearly define roles and responsibilities for data quality oversight, and document processes for handling data discrepancies.
5. Feedback loop. Maintain a feedback loop with the data provider. Report any data quality issues promptly and seek resolution. A good provider should be responsive to your concerns.

The most common pitfalls to avoid when buying data

With data analytics, 62% of retailers stated they’ve seen an increase in competitive advantage over their competition. When buying data, there are several pitfalls that organizations should be aware of and avoid. Here are four of the most common ones:

1. Neglecting due diligence before purchase

Rushing into a purchase without thorough research and evaluation of the dataset and data provider of your choice can lead to wasted resources and bad data quality.

2. Overlooking data privacy

Ensure that the dataset complies with your local as well as global data privacy regulations. Failure to do so can result in legal and reputational risks.

3. Ignoring licensing terms

Carefully review licensing terms and restrictions, as violating these terms can result in costly legal consequences and wasted resources.

4. Failing to monitor data quality

Once integrated, it’s essential to continuously monitor data quality. Neglecting this can lead to poor decision-making based on inaccurate data. Buying and maintaining high-quality datasets involves a combination of due diligence, clear standards, and ongoing monitoring. 

Final Thoughts

By following these strategies, you can maximize the usefulness of your purchased datasets and avoid common pitfalls that may compromise data quality and the return on your investment. High-quality datasets are a valuable asset, enabling data-driven organizations to make informed decisions and gain a competitive edge.