Is your data ready for AI?

By John Laffey, VP, Product Marketing, DataStax.

It’s become clear that generative AI will play an important role in your organization. And you might know that getting accurate, relevant responses from generative AI (genAI) applications requires the use of your most important asset: your data.

But how do you get your data AI-ready? You might think the first question to ask is “What data do I need?” But that’s the wrong approach to the problem. Effective, accurate genAI needs massive amounts of data to evaluate queries, so the first question is “What data do I have?” The second is “Where is this data?” Let’s explore some of the common data types that present challenges – and how to solve them for AI.

Structured data

Structured data is often the first type of data that comes to mind when people think about databases. Structured data is any ordered data stored in a relational or NoSQL database, Excel spreadsheet, Google sheet, or other medium that stores data in rows and columns. This can include order records, inventory, support tickets, and financial records, to name a few.

Structured data can reside in many different places. The most common are traditional databases like Oracle, DB2, and PostGreSQL. Network drives, Google drives, and even local disks can be the repositories for many smaller collections of data like spreadsheets. Structured data is easily available for use in AI applications.

Yet there’s a common challenge to getting structured data AI-ready: consolidation. Often the data resides in different databases, in diverse data centers, or in different clouds. Migrating the data into similar databases, and replicating data across multiple locations, provides the availability and speed required for AI applications.

Unstructured data

Unstructured data tends to be the bulk of information available to enterprises. This huge category includes any data not residing in a structured database, including emails, text files, PDFs, web pages, media files, spreadsheets, survey responses, and many other formats of data that aren’t easily stored in databases. Most regular organizational assets such as spreadsheets and documents (sometimes referred to as “semi-structured” data) fit into this category. As much as 90% of an organization’s data is unstructured.

Unstructured data poses a significant challenge for AI uses. The widely varied formats of the data, the vast array of storage locations and techniques, and the sheer volume of unstructured data make it nearly impossible to query with a standard query model. Consider the example of doing a query on “company holidays.” Relevant data could be posted on your organization’s internal website, documents, and PDFs on shared drives, and emails stored in the cloud. Designing a single query model to reach all those locations and read all those data formats is not practical.

Getting unstructured data AI-ready requires two main components: normalizing the data into a standard, searchable format, and consolidating the data. This is where vector data and vector databases come in. Vector data solves the problem of handling large volumes of unstructured data to make it AI-ready.

Vector data

The standard data type for AI is vector data. Vector data converts data from text to numerical representations of the data. Vectorizing “normalizes” data regardless of the original format. Vector data can represent text files, PDF files, web pages, or even audio files. Vectorizing and storing this data (as vector embeddings) enables machine learning models to make comparisons of data points mathematically, allowing queries across formerly diverse data types.

While vector data isn’t a new format, it is the data type that makes real-time AI possible. The ability to identify semantic similarities across huge volumes of data quickly gives LLMs query results accurate and complete enough to suit many AI applications. Vectorizing data also enables the data to be stored in a single, scalable database, reducing query time, costs associated with data gravity, and network latency.

Graph data

Graph data enhances vector data for AI by maintaining complex relationships among data that are difficult to describe in other ways. Vector with graph improves the relevancy of AI results by explicitly defining relationships other queries may miss. Graph data is stored as “nodes” and “edges.” Edges define relationships between nodes that other data structures can’t maintain easily at scale. The ability to maintain and process graph data is particularly important to large enterprises with huge amounts of data that need to be used for AI.

Graph databases have existed for many years and are particularly well-suited for complex data analytics. When implementing graph data for AI, greatly improved performance has resulted from the use of “knowledge graphs.” Knowledge graphs represent data points and the relationships between them. They illustrate the relationship between data allowing queries to make connections beyond semantic similarities. For example, a PDF might have an embedded URL to a related document. A simple vector query would not make the symantec connection between the PDF content and the linked document. A knowledge graph maps this connection, allowing queries to traverse the loosely rated data.

Knowledge graphs process graph data much faster than traditional graph database queries. They provide a simpler way to represent the graph data. Knowledge graphs improve AI querying by combining information from many unrelated sources into a larger knowledge graph that still makes sense. This ability to connect distantly related data provides much more accurate query results and greatly decreases LLM hallucinations.

Why get AI-ready now?

Getting your data AI-ready now is more than just a step toward implementing AI. It’s a way to build a competitive advantage whether your AI goals are months (or even years) away. Having AI-ready data means clean and consistent data that performs better in any application. AI-ready data means improved processing and performance as data locations and types are reduced and normalized.

Scaling is easier when data is AI-ready as data normalization makes integration less complicated. This all leads to a competitive advantage by accelerating development and getting to market first. Cost reduction is a natural byproduct of getting AI-ready as tooling is reduced; compliance is simpler; and resources, both on-premises and in the cloud, are used more efficiently. Getting data AI-ready is essential for maximizing the potential and effectiveness of AI technologies, ensuring accurate, reliable, and efficient outcomes.

Learn how DataStax makes creating vector data easy.

About John Laffey

John Laffey has over 30 years of experience in technology as a practitioner and leader with experience in the DevOps, automation, and security spaces. Formerly of Splunk, Puppet, and Pegasystems, John has a deep understanding of the challenges enterprises face when adopting new technologies.