Data Trust: An Overview and Its Significance Explored
In today's data-driven world, the reliability and accuracy of data products play a crucial role in informed decision-making for businesses. However, the efficacy of machine learning models, and by extension, the trust in the data they rely on, hinges on several factors.
The Importance of Data Trust
Data trust involves the faith that data consumers have in the usefulness of data. Maximizing a company's data value relies on data consumers trusting the data's accuracy, completeness, and reliability. A recent study by Salesforce found that more than half of global knowledge workers don't trust the data used to train AI systems, and 56% find it difficult to extract the information they need from AI systems.
Strategies for Building Data Trust
Establish Robust Data Foundations
Central to building trust is creating a strong cross-platform taxonomy and data alignment strategy. Defining and organizing data consistently across platforms reduces discrepancies and helps employees understand and trust the data sources and definitions.
Implement a Comprehensive Data Governance Framework
A robust data governance framework is essential for maintaining data quality, security, and compliance. This includes defining clear data standards, assigning dedicated data stewards, integrating governance into daily workflows, and designing the framework to be scalable and adaptable.
Make Metadata Context-Aware and Business-Relevant
Effective use of metadata enables better search, discovery, and understanding of data context. For instance, Kroger’s domain-driven data architecture created seamless connections between disparate systems and provided semantic search, improving trust by making data easier to find and use accurately.
Proactively Monitor and Manage Data Quality
Regularly detect and address issues in core data quality dimensions: accuracy, consistency, validity, and freshness. Effective governance practices support early detection and continuous resolution of data problems.
Create Collaborative, Cross-Functional Teams
Align data experts across business units, IT, finance, and legal to validate and integrate data securely and efficiently. This fosters trust by ensuring that data is used accurately and consistently across the organisation.
Secure and Protect Sensitive Data
Ensuring cybersecurity and privacy protection is critical to building and maintaining trust. Consumers and employees expect data to be safeguarded above all else.
Tools for Addressing Bias in Machine Learning Models
Companies use tools like IBM's AI Fairness 360 and Google's What-If Tool to identify and address bias in their machine learning models. Addressing bias is crucial for maintaining trust in AI systems, as biased results can lead to poor decision-making and damage to a company's reputation.
Case Studies
Unfortunately, the importance of data trust has been highlighted by several high-profile failures. For example, a simple change of a column name almost led to the failure of over 1,000 models powering an organisation's dashboards, metrics, and reporting tables. Similarly, inaccurate data displayed on a dashboard caused a manager to overbook a transport ship, resulting in a costly delay in a customer's shipment.
The Future of Data Trust
Building trust in your company's data from the outset is more effective and affordable than attempting to restore data trust once it has been lost. Tools like dbt Lab's Semantic Layer, Astronomer and OpenLineage's integration, Fivetran Metadata API, Soda-dbt integration, and Metaplane's data observability products are all contributing to the development of a data ecosystem that prioritises data trust.
In conclusion, building data trust involves aligning data definitions, applying strong governance with empowered stewards, enhancing metadata usability, proactively ensuring quality, fostering cross-team collaboration, and prioritising data protection. These measures together enable employees to rely confidently on data products for informed decision-making and sustained organisational value.
- The reliability and accuracy of data products are fundamental through data-driven decision-making, as the credibility of machine learning models, and thereby the trust in the data, heavily depends on several factors.
- To maximize a company's data value, it's important for data consumers to trust the data's accuracy, completeness, and reliability, as revealed in a study by Salesforce where more than half of global knowledge workers don't trust the data used to train AI systems.
- Strategies for building data trust include defining a strong cross-platform taxonomy, implementing a comprehensive data governance framework, making metadata relevant and easily understood, proactively monitoring and managing data quality, collaborating between cross-functional teams, as well as securing and protecting sensitive data to prioritize cybersecurity and privacy protection.
- Tools like IBM's AI Fairness 360, Google's What-If Tool, dbt Lab's Semantic Layer, Astronomer, OpenLineage's integration, Fivetran Metadata API, and Soda-dbt integration are being utilized to foster a data ecosystem that emphasizes data trust while addressing biases in machine learning models to maintain trust in AI systems.
