In today's world, where artificial intelligence (AI) increasingly influences our lives and work, there is a crucial success factor that often remains in the background: the quality of the underlying data. Data is the heart of every AI solution. Its quality determines how accurately, efficiently, and reliably an AI can operate. But how important is data quality really? And how can companies ensure that their data meets the requirements?

Why is data quality so important?

Without high-quality data, the potential of AI solutions remains untapped. A well-known principle in data science states: "Garbage In, Garbage Out" – poor data leads to poor results. According to a study by IBM, data scientists spend 80% of their time cleaning and organizing data and only 20% on actual data analysis. This enormous effort highlights the importance of data quality.

Here are the three biggest advantages of high-quality data:

1. More accurate predictions: AI models trained on clean and consistent data deliver better results and are less prone to errors.

2. Trustworthy decision-making: With high-quality data, companies can make informed decisions that positively impact their business strategy.

3. Efficiency improvement: A solid data foundation reduces the effort required for data cleaning and allows for faster project implementation.

The dangers of poor data quality

Insufficient data quality can have far-reaching consequences. For example, a study by Gartner showed that companies lost $12.9 million per year globally due to poor data in 2022 alone. Incorrect or incomplete data can cause not only financial losses but also undermine customer trust and a company's credibility.

A concrete example is the financial sector, where inaccurate or outdated data can lead to poor investment decisions. In healthcare, incorrect patient data can even be life-threatening as it can affect diagnosis or treatment.

How does one define high-quality data?

High-quality data is characterized by the following properties:

• Accuracy: The data must accurately reflect reality.

• Completeness: No important information should be missing.

• Consistency: Data should be uniform across different systems.

• Relevance: The data must be suitable for the specific question at hand.

• Timeliness: Outdated data can lead to incorrect conclusions.

A practical example is training an AI algorithm for image classification. If mislabeled or incomplete images are used, the model learns incorrect associations, rendering the entire application useless.

How can companies improve their data quality?

1. Automated data validation: Tools like Talend or Informatica offer opportunities for automated quality checks. They automatically detect inconsistencies, erroneous entries, and duplicates.

2. Standardized data collection: Uniform processes in data collection minimize errors and ensure that all relevant information is collected consistently.

3. Data cleansing: Regular cleaning and updates ensure that only relevant and correct data is retained. According to Harvard Business Review, companies can increase their productivity by up to 20% through improved data quality.

4. Training: Employees should be made aware of the importance of data quality. This is particularly true for those who manually enter or maintain data.

5. Continuous monitoring: With the help of dashboards and monitoring tools, data quality metrics can be tracked to identify problems early.

Conclusion: Data quality as a competitive advantage

Data quality is not an option but a necessity in the data-driven economy. Companies that prioritize high-quality data from the outset save not only time and resources but also build trust with customers and partners.

With the right strategy and appropriate technologies, data quality becomes a crucial competitive advantage – and the foundation for the success of every AI solution.

Sources:

• IBM: "The Data Scientist’s Workbench" (2022)

• Gartner: "Cost of Poor Data Quality" (2022)

• Harvard Business Review: "Why Data Quality Matters" (2023)