Adding Explainability to your Data

As we drive towards data-driven decision-making, organizations are amassing vast amounts of data. However, the value of this data is often diminished by its lack of explainability. Without proper documentation, naming conventions, and context, analysts and engineers spend more time deciphering datasets than deriving insights. Making data intuitive and accessible is critical, especially as organizations increasingly rely on analytics and AI. Here’s how technical practices can enhance data explainability and make your organization’s data more usable and actionable.

The Foundation: Documentation and Metadata

The first step in making data more explainable is maintaining thorough technical documentation. Every dataset, table, and column in your organization’s ecosystem should have an associated record that answers fundamental questions:

• What does this data represent?
• Where did it come from?
• How is it updated, and how frequently?
• What are the constraints, transformations, and anomalies to be aware of?

This documentation should include metadata such as data types, units of measurement, lineage (how data flows from source to destination), and usage examples. To keep this information accessible and up to date, consider storing it in a code repository, such as GitHub. By integrating your metadata into version-controlled repositories, you can:

• Track changes to schemas and definitions over time.
• Ensure teams across the organization have a single source of truth.
• Automate synchronization with CI/CD pipelines to reflect updates in real time.

For larger organizations, leveraging tools like DataHub, Amundsen, or Collibra can centralize metadata management and offer search-friendly interfaces. These platforms help users discover, understand, and trust data assets quickly.

Intuitive Naming Conventions

A well-documented dataset is of little use if its elements are cryptic or poorly named. Intuitive naming conventions are essential to making data understandable at a glance. Here are a few guidelines:

1. Use descriptive and consistent names for tables, columns, and variables. For example, instead of naming a column col1, use customer_age to clarify its purpose.

2. Avoid abbreviations unless they are universally understood within your organization.

3. Adopt a consistent casing style across datasets. Common formats being snake_case or camelCase. It is best practice to pick one early in the process and enforce it for consistency accross the organization.

4. Prefix or suffix names with context where appropriate. For instance, use sales_region instead of region to differentiate it from other regional datasets.

5. Document naming conventions in your metadata repository to ensure consistency across teams and projects. This will depend on your organization's size, however at a minimum the data documentation should exist in your code repository along with your other technical documents.  

Create Data Dictionaries and Cheat Sheets

Building a data dictionary for every dataset is another powerful method of adding explainability. A data dictionary lists all fields in a dataset, along with their: Definitions, the data class (PI, SPI, etc.), Data types, example values OR list of allowed explicit values. While these metrics are well known there are some technical documentations that should be captured as well: 

• What makes a record unique. This will tell users how to accurately count the number or records as there might not always be one unique identifying key. This will also be key to coding logic to iterate through all records.
  
• Timestamps for each record documenting the create time or last update time. Not only is this important for compliance and auditability, but it is also essential to make sure you are making use of recent and relevant data. Noting this timestamp will also allow for the automation of retention policies so data can be removed from the system as defined by and organizations retention policy.
  
• Relation to other data objects. How one object can join or look-up to another is essential. This will help analytics developers and AI professionals alike as a data object is often not useful when looked at in isolation.

Cheat sheets or quick-reference guides for common datasets or processes can also reduce onboarding time for new team members and streamline collaboration.

Standardized Data Formats and Units

Consistency in formats and units of measurement is crucial for reducing confusion. For instance:

• Use standardized date formats (e.g., ISO 8601: YYYY-MM-DD).
• Ensure numeric fields use consistent units (e.g., dollars instead of mixing cents and dollars).
• Clearly define categorical variables, such as coding gender as M/F or male/female, but not both.

These standards should be outlined in your documentation and enforced via validation rules in data pipelines. These are also important to enforce to make sure all data is in the right format to reduce the amount of data cleaning later. The ability to automate or do large scale data processing depends on your data being in a predicable format.

Steps for Reducing Time Spent on Data Preparation

The ultimate goal of explainable data is to reduce time spent on data gathering, cleaning, and preparation. By integrating the practices above with automation, you can streamline workflows:

1. Automate metadata collection and lineage tracking as part of your ETL/ELT pipelines.

2. Use schema validation tools to ensure datasets conform to expected structures.

3. Implement data quality checks to flag anomalies or inconsistencies early.

Explainability for AI and Analytics

Explainability is especially critical for analytics and AI initiatives, where trust in data is paramount. Models are only as good as the data they’re trained on, and a lack of transparency can lead to biases or errors. By making data explainable, organizations can:

• Increase confidence in analytics and AI outputs.
• Accelerate experimentation and prototyping by reducing ambiguity.
• Enable cross-functional teams to collaborate more effectively.

To start, teams can rely on GitHub to version control metadata files, enforce naming conventions through code reviews, and document datasets in Markdown files alongside their repositories.

Overall, adding explainability to your data isn’t just a best practice—it’s a necessity for modern organizations striving to make data-driven decisions. By maintaining proper documentation, adopting intuitive naming conventions, and leveraging metadata management tools, you can ensure your data is clear, consistent, and actionable. These efforts not only reduce time spent on data preparation but also build a strong foundation for analytics and AI initiatives, empowering your organization to focus on insights rather than cleanup.