Simple Steps to Smarter Data

In our last two blog posts, we explored the critical role of clean data in achieving optimal

via flickr by Alejandro Mallea

outcomes for large language models (LLMs) and AI-powered tools. The adage "garbage in, garbage out" remains as relevant as ever. However, the benefits of high-quality data extend far beyond AI applications—they impact every system across an organization.

What Defines Good Quality Data?

Good data is more than just correct spelling or consistent naming conventions, though those are essential. It also involves sufficient granularity, the use of unique data keys, and the application of appropriate data qualifiers. Ensuring these elements are in place doesn’t just make AI bots smarter—it enhances the efficiency and accuracy of every software system in your organization.

Granularity: The Power of Detail

Granularity in data has two main aspects: structured field input and document management.

1.      Structured Fields: Take the common Address field as an example. If a single field contains the full street address along with the city, state, and zip code, sorting and filtering become cumbersome. Instead, capturing each component separately allows for more precise grouping, filtering, and analysis. Without this granularity, your data is limited to broad, less useful searches.

2.      Document Management: Many organizations transitioned from physical filing cabinets to digital archives by scanning entire folders into single PDFs. While this reduces storage costs, it makes retrieving specific documents time-consuming, especially if optical character recognition (OCR) wasn’t applied. A structured approach—such as separating individual documents and implementing OCR—enhances accessibility and usability.

Unique Data Keys: The Foundation of Consistency

A unique data key ensures consistency and accuracy across multiple systems. The best example is a Social Security number, which remains the definitive identifier across government databases. Similarly, organizations should use a unique Customer ID instead of relying solely on names or addresses, which can change over time. A stable, unique identifier prevents inconsistencies and ensures seamless data integration.

Data Qualifiers: Adding Meaningful Context

Data qualifiers provide additional descriptive context. For instance, a Customer Account record should contain essential fields like Customer ID, Name, and Address but should also include qualifiers such as Account Type, Year Started, and Status (Active/Inactive). The challenge lies in balancing the depth of data collection with the cost and effort required to maintain it.

Smarter Data for a Smarter Organization

Better-structured data with unique identifiers and meaningful qualifiers strengthens every application within an organization. Standardized data keys enable seamless API communication between systems, enhancing overall efficiency. Moreover, when digital documents are linked to these data keys, information retrieval becomes streamlined, ensuring that every piece of data works toward the same goal—making your organization smarter and more data-driven.

imkore Millennia Group and its imkore partners provide workflow, document management services and solutions, IT strategy, data fabric and ERP managed services.