Data Trust: Its Importance Explained
Building Trust in Company Data: Best Practices and Tools
In today's data-driven world, trust in company data is paramount. A mere 52% of organizations have begun addressing the importance of trust and transparency between data producers and consumers, according to Deloitte. Here are some best practices and tools to help build and maintain data trust within a company.
Establishing a Comprehensive Data Governance Framework
A strong data governance framework is the foundation of trustworthy data. This involves defining roles, responsibilities, procedures, and accountability for data stewardship and management across the organization. Appointing data stewards who oversee data quality and accessibility is essential.
Implementing Stringent Data Security Policies
Data security is another crucial aspect of building trust. Implementing policies such as encryption of data at rest and in transit, role-based access control, firewall protection, and intrusion detection can help ensure the security of sensitive data. Dynamic access controls like attribute-based access control can further reduce risks.
Ensuring Proactive Data Quality Management
Proactive and continuous data quality management is essential for maintaining high standards. This can be achieved through automated quality assurance, regular profiling, validation rules at data entry, manual audits, and remediation workflows. Data quality scorecards and real-time monitoring can help maintain these high standards.
Maintaining Transparency and Ethical Data Use
Transparency and ethical data use are vital for building trust. Companies should clearly communicate data collection, usage, and sharing practices, respect privacy, gain informed consent, and minimize data collection to what is necessary. Regularly conducting ethical impact assessments and adhering to evolving data ethics frameworks is also important.
Engaging Stakeholders Actively
Engaging stakeholders across the data lifecycle is essential for building trust. This includes internal business units, employees, external consumers, and affected communities. Providing feedback loops, regular training programs, and maintaining accessible, up-to-date guidelines on data usage can help foster trust.
Enforcing Regular Compliance Checks and Audits
Regular compliance checks and audits internally and externally are necessary to verify adherence to policies and regulations. Plans to remedy any non-compliance promptly are also important.
Providing Ongoing Training and Leadership Support
Fostering a culture of responsibility and accountability around data ethics and governance requires ongoing training and leadership support. Senior management should model ethical behavior and resource allocation for these efforts.
Tools for Building Data Trust
Several tools can help companies build data trust. Metaplane's data observability products, for example, feature column-level lineage across the entire data stack, quickly identifying the products impacted by a data anomaly. Fivetran's Metadata API tracks data as it moves through pipelines, reporting on its source, impact, access, and the effect of upstream schema changes.
Astronomer and OpenLineage have integrated their products, allowing Astronomer's Astro platform to work with OpenLineage's data lineage and data observability framework. Soda-dbt integration provides the ability to visualize data quality over time, create an alert system for failed dbt results, and report and track data quality anomalies.
Tools like IBM's AI Fairness 360 and Google's What-If Tool help companies identify and address bias in their machine learning models. Monte Carlo Data offers end-to-end field-level data lineage that tracks column-level dependencies from initial ingestion to dashboards and reports. dbt Lab's Semantic Layer allows teams to define data metrics centrally and dynamically, promoting consistency in results.
The Importance of Data Trust
Data trust is crucial for bridging the gap between knowing and doing, according to Deloitte. Building trust in data from the outset is more effective and affordable than attempting to restore data trust once it has been lost. A proactive approach to instilling quality as the foundation of data management frameworks promotes loyalty among staff members, facilitates governance, and makes the business function more efficiently.
Unfortunately, the lack of trust in data can have serious consequences. A batch processing system for a company's monthly reports has failed, and a simple change of a column name almost caused more than 1,000 models powering an organization's dashboards, metrics, and reporting tables to fail. In one case, a global supply chain company's dashboard system has crashed, causing a manager to overbook a transport ship due to inaccurate data.
In conclusion, building and maintaining data trust is essential for any company looking to succeed in today's data-driven world. By implementing these best practices and using the available tools, companies can ensure their data is managed responsibly, securely, and ethically, fostering trust among employees, customers, and partners.
- To establish a foundation for trustworthy data, a comprehensive data governance framework should be developed, encompassing roles, responsibilities, procedures, and accountability for data stewardship and management.
- In addition to a strong governance framework, implementing stringent data security policies, such as encryption, role-based access control, firewall protection, intrusion detection, and dynamic access controls, is crucial for safeguarding sensitive data.
- Proactive data quality management is necessary to maintain high standards. This can be achieved through automated quality assurance, regular profiling, validation rules at data entry, manual audits, remediation workflows, and tools like data quality scorecards and real-time monitoring.
- Companies using machine learning models should utilize tools like IBM's AI Fairness 360 and Google's What-If Tool to identify and address potential biases in their models, ensuring ethical data usage and transparency.
