Data integrity requires maintaining the accuracy and consistency of data over its lifecycle, and is also crucial to enabling predictive AI systems to make accurate predictions and decisions. It prevents errors that can easily arise when AI algorithms are fed corrupted or altered data.
Compliance and management
Regulatory compliance is essential to avoid legal issues and maintain internal and public trust and confidence in the system. Clear records make it vastly easier to demonstrate that the system and its users are handling the data responsibly. This also helps organizations comply with applicable governmental, industry, and internal regulations and controls by providing a clear record of data handling and processing.
Effective data management — organizing and maintaining data efficiently — is another aspect of the data lineage challenge. Good data management practices ensure that data is easily accessible and usable for all (and only!) authorized users. This is vital for the smooth operation of AI systems.
V&V: Two sides of the same coin
Your data lineage approach must also support the verification and validation (V&V) processes. These are two distinct but complementary processes ensure an AI system works properly according to its intended purpose. Verification focuses on the programming of AI algorithms to ensure the system conforms to specifications. Validation is about doing the right thing; making sure the program is actually producing useful output.
Well-implemented data lineage will help ensure the recommendations produced by AI systems make sense to users. They have transparent access to all data sources used to generate outputs and can easily — and independently — verify the outputs. Users can also quickly spot “model drift” and take corrective action when the output begins to conflict with real-world measurements or other criteria.
Developing and implementing a solid data lineage strategy will enhance transparency within your organization; everyone involved will have visibility into the data’s journey. It will make it much easier to understand and trust the output of your AI systems. Knowing the system is using accurate, reliable, and traceable data, your people are free to focus on making the most informed decisions possible. They can rely on (and access, as needed) the data itself as well as the information produced by your AI systems. Clear, documented, and traceable data lineage also helps identify and mitigate risks associated with data handling and processing.
How knowledge graphs improve governance
Knowledge graphs model and represent all data available to them and can handle structured and unstructured data sources of any size — no matter how many terabytes or petabytes are involved. They have become integral to the construction of most search engines and other types of sites and are now a mature technology that organizations of nearly any size can utilize effectively. Along with improving the accuracy generative genAI tools, they can ensure the maintenance of effective data lineage — and therefore governance — in AI systems, from the enterprise down to the workgroup level.
Knowledge graphs keep track of exactly where each piece of data originates and logs every change made to the data, along with records of who or what made each change. Their semantic integration capabilities make use of web standards, including the Web Ontology Language (OWL) and Resource Description Framework (RDF), to facilitate data access and interpretation. They also use PROV-O, a W3C standard, to represent and exchange provenance data.
Knowledge graphs are therefore essential to successful V&V at the enterprise level. They give you the tools you need to track and maintain data lineage that supports trustworthy AI systems.
Click to learn more: altair.com/knowledge-graphs.
This blog post is a contribution from Altair, world leader in computational intelligence, for the readers of Data Expo. You can find more inspiration at altair.com/knowledge-graphs or visit Altair during Data Expo at booth #21.
