Accurate data is critical to AI as without it systems can produce biased or misleading results
Data has always been the foundation of informed decisions. Now, in the era of artificial intelligence (AI), feeding generative AI models and applications with accurate data is critical. Without it, AI can produce biased or misleading results, leading to flawed decision-making and wasted resources.
And so, the challenge today is making all the right data available to AI. Simple right? Not so much. There is inherent complexity in integrating data from diverse sources.
The logical starting point to source data for AI is data that exists in the organization. The biggest fundamental problem, in every organization, is that there are custom data silos in every business unit, so the challenge becomes providing a holistic view of all data while ensuring data accuracy, availability, and compliance.
Now, let’s look at what is involved. We’ll start with getting the right data in one place. Data movement platforms can take data from a source and move it to a destination to build data pipelines. The challenges here are:
Having connectors for all the different data sources and destinations that are needed to create data pipelines
Having access to all data – structured (think databases) and unstructured (documents, for example). Also, all the data that is extracted needs to be reformatted, indexed, chunked, embedded, and loaded to vector databases, before generative AI can derive value from it.
Here is where the importance of “open data” comes into play. No single data movement vendor can support off-the-shelf all the connectors that every company needs. For example, in the marketing function, there are more than 10,000 data sources to potentially pull data from. The only solution to that problem is through an open platform that lets the user community easily build connectors and share them to be used by anyone through a marketplace. This will enable the data movement platform to cover most connectors off-the-shelf over time, in addition to enabling teams to cater to their own custom needs with the connector builder technology that is available to them. What becomes key here is how easy it is to build a new connector, as the easier it is, the more connectors there will be in the marketplace.
A recent report from Wakefield Research revealed that data engineers spend an average of 44% of their time maintaining data pipelines, costing organizations approximately $520,000 annually. So, it is important for every company that this problem gets solved.
Last but not least is respecting the access control list (ACL) so no employees have access to data and insights they shouldn’t have access to through the generative AI prompts and responses.
To summarize, when you think about your data and AI infrastructure and how to empower generative AI use cases on top of your data, here are some critical considerations we’ve identified for your data movement infrastructure:
Support vector database destinations (like Pinecone, Weaviate, Milvus) and AI-optimized warehouses (like Snowflake Cortex and PGVector).
Support both structured and unstructured data sources (S3, Google Drive, etc.)
Support conversion into documents for all data types, chunking and embedding out of the box.
Support transformations such as PII masking and access control list, as all teams shouldn’t have access to all the insights that the AI should be able to provide.
To fuel the next wave era defined by AI, machine learning, and unstructured data, resilient data infrastructure with an open approach is the only way that organizations can keep pace with the volume and complexity of structured and unstructured data that support AI.
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