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AI tools are only as effective as the data they rely on. Poor structure, unclear labels, or inconsistent formats can derail even the most advanced systems. Jose Plehn Dujowich, founder of BrightQuery (BQ) and BQ AI, has spent years working with government agencies and private companies to build datasets that machines can reliably use. His approach highlights the importance of clarity, consistency, and intelligent structuring.
Format Everything Uniformly Before Use
Before you begin developing any AI application, take time to enforce uniform formatting across all records. Simple mismatches, such as multiple ways of writing a company name, date formats, or phone number structures, can lead to duplicated or misclassified records. Jose Plehn Dujowich has stressed that consistency is the first layer of trust in data, especially in systems drawing on millions of records.
Start by scanning your current database for variation in key fields and use tools to standardize values. For instance, all dates should use the same format (YYYY-MM-DD), and names should follow a fixed structure (First, Middle, Last). If multiple datasets are combined, match field names and values before merging. The more consistent your structure, the less effort is required downstream when training AI models or performing analysis.
Build Connections Between Entities to Provide Context
AI thrives on context. Is an employee connected to a department? Is a purchase linked to a location? These connections tell a broader story that helps AI understand what’s happening.
Use unique identifiers to tie different records together. For example, use a consistent customer ID across sales, service, and feedback systems. Link products to suppliers, projects to clients, or employees to regions. Don’t rely on names or unstructured text to establish these connections. Utilize consistent, trackable keys that don’t change over time. These relationships create networks that AI can analyze more effectively than isolated records.
Establish Clear Metadata for Every Field
Every field in your database should have a definition. Metadata explains what a column means, how it’s formatted, what values are allowed, and when it was last updated. This context is vital for AI systems trying to interpret data relationships. Jose Plehn Dujowich’s approach to government datasets emphasizes making variables transparent and traceable, even across large multi-agency systems.
You can begin by building a centralized data dictionary. This doesn’t require expensive software; a spreadsheet works. List every field, its source, allowed range of values, units of measurement, and how often it’s updated. Include whether it’s personally identifiable or sensitive. Store this document where both humans and systems can refer to it, and review it quarterly. The goal is for someone unfamiliar with the data to use it correctly on the first try.
Strong Data Habits Lead to Smarter AI
AI works best when the data behind it is thoughtfully prepared and consistently maintained. Structuring information carefully through consistent formatting, thorough metadata, and linked records builds the basis for any reliable AI effort. Jose Plehn Dujowich’s experience across government and business shows that reliable data practices lead to better decision-making, smoother automation, and fewer errors. By focusing on clarity, context, and traceability now, organizations avoid costly rework and build systems that can adapt and scale.
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