Unlocking AI's Potential: The Crucial Role of Data Governance
Traditionally, data governance refers to managing data from multiple aspects, including usability, integrity, and security. With the rapid development of AI, companies have the ability to use data to build AI models to improve the business in many areas, including in operational efficiency or forecasting consumer demand. As data, often in high volume and variety, is needed both to train and enhance AI algorithms, data governance has increased in importance.
However, the quality and organization of data are two of the main obstacles that are standing in the way for companies to develop successful AI models. Gartner, a consulting company, found that 85% of AI projects fail due to data issues. Many companies do not have the bandwidth and expertise to find a solution and, therefore, are unable to reap the full benefits of AI. According to Dataversity, poor data quality costs organizations an average of $12.9 million annually. With better data governance, companies have the possibility of turning this weakness into an advantage and produce higher profits. A study by IBM showed organizations with fully deployed security automation, which leverages AI, machine learning, and automated orchestration, had a $3.58 million lower average total cost of a data breach compared to organizations without automation.
There are several data governance challenges for companies when creating AI algorithms. First, an immense volume and variety of data are needed. AI systems often require large datasets from diverse sources for the model to function with multiple types of inputs. Another issue is AI systems can potentially misuse sensitive data due to a lack of labeling or anonymization. According to a report by the Ponemon Institute, 80% of data breaches involve personally identifiable information (PII). Bias and fairness are also needed when training AI models, to ensure that AI algorithms have accurate decision-making. For example, Amazon discontinued an AI recruiting tool after discovering it was biased against women. The tool favored resumes that included male-associated terms and downgraded those from all-women's colleges. Lastly, transparency and explainability of models are needed for us to fully understand the decision-making process, in fact, PwC found that only 25% of consumers trust AI models. Stakeholders need to understand how AI models use data to make decisions to be audited and verified. Fortunately, this has started to become a legal requirement in some regions, such as the GDPR in Europe and the CCPA in California.
Next, here are some strategies for mitigating the challenges described above. Implementing data policies will create a good foundation to comply with ethical guidelines and be effective. Limiting the purpose and minimizing the amount of data used will allow data to be only used for its sole purpose. This means, for example, only data about purchase history is used, not detailed personal information. In addition, the data is not for unrelated research or sold to a third party. Bias detection will detect and reduce unfairness in datasets and AI models, checking for any preference against gender or ethnic groups and adjusting the models to eliminate any spotted. Multiple sources agree stakeholders are increasingly demanding transparency in AI decision-making. Data governance frameworks must ensure that AI systems are explainable and that their decision processes can be understood and routinely audited.
In short, the four key benefits of improving data governance are to improve decision-making, adhere to regulatory compliance, and build trust, and operational efficiency