Reliable AI requires reliable data
Data reliability is paramount for Artificial Intelligence (AI), with accuracy and trust in insights directly dependent on the quality of the underlying data. From predictive analytics to Natural Language Processing (NLP) advances such as Large Language Models (LLMs), AI revolutionizes how business operates and makes decisions. For many, AI is a black-box, and risks, real or imagined, are challenging its use. The success of AI hinges precisely on trust, and trust relies on the ability for teams to understand, observe and act quickly on data quality (DQ).
The role of data engineers
Data engineers are the architects behind the scenes, responsible for building and maintaining the data infrastructure that supports AI-driven initiatives. Their role encompasses designing data pipelines, assisting data quality, and optimizing data processing systems for efficiency and scalability. Those working closely with the business understand that poor data quality is the primary blocker for accurate insights, strong decisions and reliable AI.
Strategies for ensuring data quality for AI
There are many challenges when enabling data quality, from scalable and manageable rule writing, to the ability to catch unknown-unknowns. To facilitate effective AI, data engineers must seize the opportunity to instill data quality and observability into pipelines, enact the right structure, and work with the business.
Here’s a breakdown of some top strategies for ensuring data reliability:
- Streamline data profiling and remediation workflows: Data engineers use data profiling techniques to help analyze and understand the structure, content and quality of data. This involves identifying inconsistencies, duplicates and missing values within the data and implementing workflows to properly address issues that would directly affect the performance of AI applications. Most commonly, organizations need to avoid modification of production data directly. To ensure work and remain compliant, data intelligence can be employed to share data quality with stewards early on, aiding in detection of DQ issues and providing the ability to request correction through data remediation workflows.
- Employ data governance that integrates with data quality: Data engineers aid in establishing the right data governance frameworks to define policies, processes and standards for data management. This includes defining data ownership, access controls and lifecycle management for integrity and compliance. The best solutions are automated, scale to your organization, and integrate with data quality directly within a common platform, avoiding fragmentation of DQ from governance and streamlining the user experience. This reduces adoption friction, thus allowing data engineers to focus on their own tasks while non-technical users are empowered for business definitions, policies and regulatory standards. In addition, selecting a solution that combines the governance of data and underlying models used in AI (i.e., AI Governance) increases productivity and proactively mitigates harmful AI model risks.
- Automate data quality checks: Anomaly detection and adaptive rules can help build trust in data, informing AI models via automated data quality checks and ML monitoring systems utilized to detect deviations in real-time. The right self-service solution for data quality and observability is industry agnostic, quickly adapting to changes in the data and allows instant checks across select timelines.
- Empower data scientists with quality metrics: To positively impact AI applications, data engineers must collaborate closely with data scientists and business users to understand data requirements, validate data quality, and ensure that AI models are built on reliable and trustworthy data. This means providing these users with access to clean, high-quality data and clear DQ scoring and rules on its use. By having a direct connection between data stewards in the catalog and data quality, data engineers are empowered to assist with data quality, based on immediate requests and needs from the business, for successful outcomes in AI.
- Validate and audit for continuous improvement: Data engineers can lead by adopting a culture of continuous improvement, regularly monitoring and evaluating data quality metrics and implementing feedback loops to address emerging data quality issues. This involves conducting regular audits, implementing data quality best practices, and refining data quality processes and systems over time. Using a data quality and observability solution integrated with data intelligence instantly provides access to all of this, including feedback loops for users and a system for data governance. This eliminates the cost of building out a process from the ground up, and the chance for error.
In summary, data reliability is the foundational component of reliable AI. Data engineers should be the bastions of data quality, and their tools should be an effective set of technologies which employ strategies such as data profiling and remediation, implementing data governance frameworks, automating health checks, collaborating with data scientists and embracing continuous improvement.
As businesses continue to leverage AI technologies to gain competitive advantage, the importance of data quality in driving accurate insights and informed decision-making cannot be overstated. Collibra aids data engineers in their invaluable work, providing a centralized system for automated data quality and observability that easily works across the organization, ensuring reliable AI.
