Ensuring data reliability for AI-driven success: The critical role of data engineers


Trust in AI requires trust in data

Data reliability is paramount for Artificial Intelligence (AI). Accuracy and trust in AI generated insights is 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 businesses operate and make 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, ensuring 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 remain compliant, a data intelligence platform can 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 integrated data governance and 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, scaled to your organization, and integrate with data quality directly within a common platform. This combination helps avoid fragmentation of DQ from governance and streamlines the user experience, reducing adoption friction.  An integrated platform allows data engineers to focus on their own tasks while non-technical users are empowered to create business definitions, policies and regulatory standards. In addition,  a solution that combines governance with AI models (i.e., AI Governance) will help your organization increase productivity and proactively mitigates harmful AI model risks.
  • Automate data quality checks: Anomaly detection can help build trust in the 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 checks instantly across select timelines.
  •  Collaborate with data scientists: 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.
  • Build trust through verification 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 both AI success and market differentiation of AI features. Data engineers are the bastions of data quality, and their tools are 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 a 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 system for automated data quality and observability that allows organizations to unlock new innovation and growth for trust in AI. 

We would love to show you how all of this is possible.

Reach out to us here for a full demo and to learn more.

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