Improve your AI with good data: find out what good data is and how to use it

AI can add enormous value to your organization, but without qualitative data, the result will disappoint. After all, good data is the basis for reliable AI insights and smart decision-making. But what exactly is good data, and how do you ensure that your organization makes optimal use of this data? In this article, we explain concretely how to prepare and apply data for maximum AI impact.

What is actually good data?

Good data has specific properties that make it useful for AI applications. This goes beyond simply collecting “a lot of data”. It concerns data that:

• precise are: data without errors or inconsistencies. This means accurate and up to date information, without inaccuracies.
• fully are: the data should provide a complete picture of the situation you want to analyse, without gaps or missing values.
• Relevant are: only collect data that contributes to your specific AI goal. Avoid redundant data that clouds your analytics.
• Consistent are: data must be collected and stored in a uniform manner. Variations in units of measurement, terminology, or data formats disrupt AI processes.

Practical steps to ensure good data

Step 1: organize and label data correctly

Make sure data is clearly and consistently organized in your systems. By providing a clear structure and labeling data (e.g. “customer data,” “transactions”), your AI system can interpret and use this data efficiently.

• Hint: use data storage tools that automatically assign labels and metadata to ensure consistency.

Step 2: Perform regular data audits

Regularly check the quality of your data by means of data audits. This allows you to identify inaccuracies and make quick improvements.

• Action point: plan monthly audits to ensure both data quality and relevance and to resolve errors in a timely manner.

Step 3: Define key metrics together

Together with your team, determine which metrics and data points are important for your AI project. For example, if you're measuring customer loyalty, determine whether it's based on purchase frequency, visit length, or other variables.

• Action point: document each metric clearly so that everyone in the organization uses the same definitions and interpretations.

3. Why giving context and meaning to data is essential

Data only becomes valuable when you give it meaning. Numbers and facts without context are useless for AI. A clear example is a customer profile. For example, data can show that a customer buys regularly, but it's the labels and context that tell you why this happens and what it means for your goals.

• How to do that: provide metadata that adds extra information to your datasets. This way, the AI system knows how data relates to each other and what the specific context is.

4. The role of data governance

Data Governance is a system of policies, rules, and procedures that ensure that your data remains accurate, secure, and usable. Without good data governance, you run the risk of inconsistent and unreliable analyses.

• Implementation tip: appoint a data governance team that is responsible for maintaining quality standards and compliance.

5. Avoid common data errors

AI systems often run into problems due to data errors such as inconsistencies, missing values and lack of standardization. These errors cause models to provide unreliable results. To prevent this:

• Ensure uniform storage: use the same data formats and units of measurement consistently.
• Collect data carefully: minimize errors by implementing standardized data collection processes.

Conclusion: qualitative data as a basis for successful AI

Good data is the foundation of a reliable AI solution. With accurate, relevant, and well-organized data, you lay a solid foundation for AI that adds value to your organization. By taking the right steps in data audits, labeling, and data governance, you make data usable and transparent. This is how you ensure that your AI systems deliver real results, based on data that you can rely on.

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