Three Data Problems Lurking in your AI Strategy

Many AI initiatives are focused on the behavior of customers. Large organizations typically have customer information spread across many different systems by product line, business unit, geography, or even function. While efforts ranging from master data management to enterprise data governance programs may exist to standardize and promote common customer data across the enterprise, many limitations remain. Are the addresses and locations correct? Are the customers categorized or classified consistently? Are individuals vs. entities treated the same across systems? Is demographic information tracked the same? Are records updated based upon real world events (e.g., death, relocation) on the same timeline?

Consider a large bank with divisions including commercial banking, retail banking, credit cards, mortgage, custody, asset management, and wealth management. Are all of the attributes relevant to the same customers across these divisions sufficient to support an AI initiative? The same is true with a healthcare provider that has many different departments and modalities for treating patients and administering care. Is all of the data collected during a patient interaction consistent across such a wide variety of healthcare providers and situations? In any of these industries, inconstancies always exist.

The data doesn’t have to be perfect, but improvements of inconsistencies across one or two key attributes may dramatically improve the reliability of predictive AI models.

Large language models (LLMs) are all the rage. However, most industries have their own unique language to describe the way businesses transact. Curating data sets to derive LLMs that truly represent the language of an industry is essential to delivering reliable AI outcomes.

Let’s use a simple phrase: “head and shoulders”

• If you’re a healthcare provider, you may be the describing the location of a pain requiring treatment.
• If you’re a retailer, you might be referring to your most popular shampoo product.
• If you’re an investment manager, you may be referencing a technical trading strategy.
• If you’re simply on the internet, "head and shoulders" may just be an adjective or descriptor for writing.

As we look at the highly regulated financial markets, there is a language for fixed income markets and even the municipal sub-market.  Similarly, when considering equities and all of the venues and markets for trading publicly or privately, we see the need for another language. Again, we see another language in the funds market as asset managers create complex documentation to describe all of the terms and conditions for their investment strategies and fund structures.

Each of these distinct use cases requires reference data that fits the overall predictive or generative use cases that are being prescribed. Extracting content out of documents and correctly training NLP to interpret terms and conditions to identify industry-relevant data is a key task in building out reliable reference data.

An AI initiative may have the perfect use case, the greatest support, some incredibly valuable data, and may still struggle for one simple fact: missing data. The most valuable systems for training AI models typically have been in use for years and years, which means each system's data can show the history and trends of important behavior. However, the older the data, the higher the probability of missing attributes. An attribute may have been added 5 years ago, which means for the 10 years prior, the attribute doesn’t exist. Or maybe the governance was lacking, which was improved 4 years ago, but data was inputted manually in a very inconsistent manner over the 20 years prior.

Missing data is simply the reality of most enterprise systems. As AI projects look to leverage data, understanding where data may be missing is hugely important.  Inconsistently populated fields are not always detrimental to a model; however, oftentimes a significant remediation or enrichment effort is required to populate these fields.