openvue
AI transparency
platform
explainable data pipelines
01 / explainable
02 / traceable
03 / accountable
transparency in AI application development is essential because it builds trust and ensures accountability. by openly sharing how AI systems are created, trained, and used, developers can help users understand potential risks, biases, and limitations. transparency fosters collaboration and demonstrates to internal audit and external regulators how AI is used.
every decision made by AI systems should be traceable back to the data and algorithms that influenced it. this is crucial for understanding and justifying outcomes. data transparency provides an explanation of different these business contexts, unravelling inherent data complexity, making it easier for humans to understand and trust the outputs obtained.
when it comes to agentic AI, data lineage and traceability are critical for ensuring transparency, accountability and trust. this is especially relevant where agents are working in conjunction with other agents. Openvue helps organisations reduce the risk of using autonomous decision making by validating adherence to standards. appropriate governance reduces operational risk.
how does it work?
Openvue describes data pipelines used to supply AI applications. it does this by generating end-to-end data lineage of source to consumer. Openvue can be used to identify gaps in data standards and issues with data pipelines that adversely impact model performance.
unlike many other data observability tools, Openvue has a strong focus on governance and data standards, to ensure the quality and integrity of the data products. Openvue's online platform ensures close alignment with data governance standards and data product deployment.
visibility
openvue provides an easy way of describing the end-to-end journey of data through data pipelines. this provides a business-friendly view of critical data entities while hiding the underlying physical complexity. Knowing where your most important data comes from and how it is used, means key business decision makers are all on the same page.
standards
knowing which policies and standards apply to data means that they can be measured against expected thresholds. whether data quality rules or compliance checks, being able to see where the controls are, and whether data is meeting the minimum requirements, will indicate if action needs to be taken, and how urgent this is to resolve.
collaboration
fixing data issues may involve many different teams. This depends on whether the root cause is to do with business processes problems, IT system issues or data quality remediation. openvue's collaborative platform ensures both business users and technical teams are aligned, with full transparency of improvement activities needed.