Is Data Quality Essential for Creating Value from AI?

One of the most decisive factors in the success of artificial intelligence projects is data quality. While organizations are rapidly increasing their AI investments, a critical point often overlooked is the accuracy and reliability of this data. Research shows that many AI initiatives fail due to poor data quality. This brings us to the fundamental question: How can companies ensure data quality to generate real and sustainable value from artificial intelligence?

Data Quality: The Cornerstone of AI Projects

According to research, 91% of companies state that poor data quality directly and negatively affects business outcomes. Dael Williamson, CTO of Databricks EMEA, summarizes this situation as follows: Data should be approached just like a supply chain. Inspired by production processes, this perspective requires that data, like products, go through quality control stages. Just as the meat packaging industry overcame hygiene issues in the past by adopting disciplined quality standards and gained trust, data management can provide a reliable foundation for AI projects when handled with the same care and oversight.

Three Key Steps to Improve Data Quality

For organizations to achieve meaningful results from artificial intelligence, data quality has become a priority that cannot be ignored. Below are three critical steps to improve data quality:

1. Define the Data

If there is no common understanding across the organization about what data means, how it is classified, and how it will be managed, it becomes significantly more difficult to generate value from it. Especially today, when unstructured data (approximately 90% of corporate data) is rapidly increasing, this uncertainty creates even greater risks. The first step to preventing this disorganization is to establish standard definitions and governance rules for data. Data dictionaries, ownership definitions, and organization-wide accepted standards streamline the data ecosystem and provide the following benefits:

• Create a common language and alignment across teams

• Clarify who manages the data and how

• Establish a strong and reliable foundation for AI and analytics projects

2. Define Quality Metrics: Quality That Is Not Measured Cannot Be Managed

Many organizations embark on AI projects without assessing the quality of their data. However, poor-quality data is the biggest risk factor for technological investments. As a result:

• Analytical outputs are incomplete and misleading

• Models produce biased and unreliable results

• Strategic decisions are based on faulty foundations

To minimize these risks, organizations must first define data quality metrics. These metrics should cover fundamental dimensions such as accuracy, completeness, timeliness, accessibility, and consistency.

According to research, only 16% of companies describe their data as “very good.” (Source: Experian, 2023) This striking figure reveals that many organizations still do not approach data quality systematically.

3. Implement Quality Controls

For AI systems to succeed in the long term, data quality must be continuously monitored. Quality controls:

• Ensure the accuracy and consistency of data

• Provide transparency and trust in processes

• Facilitate regulatory compliance

In the words of Databricks CTO Dael Williamson: “The important thing is not to restrict every point with strict rules, but to ensure the right quality assurance.” For this reason, quality controls should not only be seen as a technical requirement but also as a part of corporate culture. In this way, internal teams gain greater confidence in data, and the AI solutions offered to customers become reliable and sustainable.

Competitive Advantage Through End-to-End Data Management

In AI investments, companies mostly focus on technology platforms, use cases, or talent development. However, this approach alone is not sufficient. True success comes from having high-quality data and managing it end-to-end with discipline.

Organizations inspired by production processes can adopt a similar approach in data management to establish the necessary checks and balances. This way, they:

• Build reliable and uninterrupted data flows

• Transform AI investments from being merely a cost item into a revenue-generating strategic program

• Gain sustainable competitive advantage in the market

This discipline not only meets today’s needs but also lays a solid foundation for companies’ future AI strategies.

In conclusion, data quality is no longer just a technical issue but a competitive advantage at the heart of business strategy. Companies that want to create value from artificial intelligence must first secure and manage their data.