TL;DR
- Identify the business data that can actually support your AI use case.
- Clean and organize information before using it with AI.
- Remove outdated, duplicate, and irrelevant data.
- Structure documents and metadata so information is easier to work with.
- Set clear permissions for different types of information.
- Protect sensitive business and customer data.
- Prepare the data according to how the AI application will use it.
Introduction
Generative AI can work with large amounts of business information, including documents, customer data, product details, policies, reports, and internal knowledge. But simply giving an AI system access to large amounts of data does not guarantee useful results.
The quality, structure, security, and accessibility of the data all matter. Preparing this information before implementation can make it easier to build an AI application that provides useful and reliable results.
Identify Useful Data Sources
Start by identifying where the information your AI application needs is stored. This could include databases, documents, knowledge bases, CRM systems, internal websites, product catalogs, support records, or business applications.
Not every available data source needs to be included. Focus on information that directly supports the intended AI use case and remove sources that are irrelevant or difficult to maintain.
Businesses that are new to Generative AI can first review a Generative AI guide to understand common use cases and how business data can be used in AI applications.
Clean and Organize the Information
Business data can contain incomplete records, inconsistent formatting, duplicate information, or outdated content. These issues should be addressed before the data is used by an AI application.
Review the information for accuracy and consistency. Standardize important fields, remove unnecessary records, and organize related information so it is easier to manage and maintain.
Remove Outdated and Duplicate Data
Old information can create problems when an AI system uses it to generate responses. For example, an outdated company policy could lead to an incorrect answer even if the AI system processes the information correctly.
Identify information that is no longer valid and remove or archive it. Duplicate content should also be reviewed so the system does not encounter multiple versions of the same information.
Structure Documents and Metadata
Documents are often stored in different formats and structures. PDFs, spreadsheets, web pages, presentations, and text documents may all contain useful information, but the information needs to be organized properly.
Metadata can provide additional context about a document, such as its title, category, author, date, department, or access level. Good structure and metadata can make it easier for an AI application to find and use the right information.
Manage Data Permissions
Not all business information should be available to every user or AI application. Financial records, employee information, customer data, internal policies, and other sensitive information may require different access levels.
Define who can access each type of information before connecting the data to an AI system. Permissions should also be reviewed when employees, systems, or business requirements change.
Protect Sensitive Information
Data preparation should include privacy and security considerations from the beginning. Identify sensitive information and determine whether it needs to be removed, anonymized, restricted, or handled through additional security controls.
Businesses should also consider how information is transferred, stored, processed, and logged throughout the AI workflow. Protecting data is important both during development and after the AI application goes into production.
Prepare Data for the AI Use Case
The best data preparation approach depends on what the AI application needs to do. A customer-support assistant may need product information and support documentation, while an internal knowledge tool may need company policies and operational documents.
Define what information the AI needs, how it will access that information, and how frequently the data should be updated. This makes the data preparation process more focused and avoids collecting information that does not support the intended use case.
Connect Prepared Data With the AI Application
Once the data is cleaned, structured, and secured, it can be connected to the AI application. Depending on the project, this may involve databases, APIs, document repositories, search systems, or other business platforms.
The technical approach should allow the application to retrieve the right information when needed while respecting access permissions. This is an important part of Generative AI development when building AI solutions around a company's existing knowledge and data.
Keep Business Data Updated
Data preparation is not a one-time task. Business information changes regularly, and outdated data can eventually reduce the usefulness of an AI application.
Create a process for reviewing and updating important information. Track changes to documents, policies, products, and other knowledge sources so the AI application can continue working with current information.
Conclusion
Preparing business data for Generative AI is about more than collecting as much information as possible. The data should be relevant, accurate, organized, secure, and accessible to the right systems and users.
By identifying useful sources, cleaning outdated information, managing permissions, protecting sensitive data, and planning how the AI will use the information, businesses can create a stronger foundation for their Generative AI applications.
FAQs
1. Why is data preparation important for Generative AI?
AI applications depend on the information they can access. Poor-quality, outdated, or unstructured data can make it harder for the system to provide useful and reliable results.
2. What types of business data can be used with Generative AI?
Depending on the use case, businesses may use documents, databases, product information, policies, customer-support records, knowledge bases, reports, and other internal information.
3. Should outdated business data be removed?
Yes. Outdated information should generally be removed, archived, or clearly separated from current information so it does not cause incorrect or conflicting AI responses.
4. How can businesses protect sensitive data?
Businesses can use access controls, encryption, data anonymization, secure storage, authentication, and other appropriate security measures based on the type of information being handled.
5. Does business data need to be updated after an AI application is launched?
Yes. Regular updates help ensure that the AI application continues to work with current business information and reduces the risk of outdated responses.
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