After highlighting the key benefits of the AI Assistant for enterprise analytics in my previous blog post, I am sharing here a demo of what it looks like to use the AI Assistant. The video below demonstrates how a persona interested in understanding enterprise projects may quickly find answers to their typical everyday questions. The information requested include profitability, project analysis, cost management, and timecard reporting.
A Perficient Demo of AI Assistant for Project Analytics
What to Watch Out For
With the right upfront configuration in place, the AI assistant, native to Oracle Analytics, can transform how various levels of the workforce find the insights they need to be successful in their tasks. Here are a few things that make a difference when configuring the AI Assistant.
- Multiple Subject Areas: When enterprise data consists of several subject areas, for example Projects, Receivables, Payables, Procurement, etc., performing Q&A with the AI Assistant across multiple subject areas simultaneously is not currently possible. What the AI Assistant does in this situation is prompt for the subject area to use for the response. That is not an issue when the information requested is from a single subject area. However, there are situations when we want to simultaneously gain insights across two or more subject areas. This can be handled by preparing a combined subject area that contains the key relevant information from other underlying subject areas. As a result, the AI Assistant interfaces with a single subject area that consists of all the transaction facts and conformed dimensions across the various transactional data sets. With a little semantic model adjustments this is an achievable solution.
- Be selective on what is included in AI prompts: Enterprise semantic models typically have a lot of information that may not be relevant for an AI chat interface. Therefore, excluding any fields from being included in an AI prompt improves performance, accuracy, and sometimes even reduces the processing cost incurred by AI when leveraging external LLMs. Dimension codes, identifiers, keys, and audit columns are some examples of things to exclude. The Oracle Analytics AI Assistant comes with a fine-grained configuration that enables selecting the fields to include in AI prompts.
- Metadata Enrichment with Synonyms: Use synonyms on ambiguous fields, for example to clarify what a date field represents (Is it the transaction creation date or the date it was invoiced on?). Another example of when synonyms are useful is when there is a need to enable proper interpretation of internal organization-specific terms. The AI Assistant enables setting up synonyms on individual columns to improve it’s level of understanding.
- Indexing Data: For an enhanced user experience, I recommend identifying which data elements are worth indexing. This means the AI LLM will be made aware of the information stored in these fields that you chose while setting up the AI Assistant. This is an upfront one-time activity. The more information you equip the AI Assistant with, the smarter it gets when interpreting and responding to questions.
For guidance on how to get started with enabling GenAI for your enterprise data analytics, reach out to mazen.manasseh@perficient.com.
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