Generative AI and Data Dictionary Descriptions

Today I’m thrilled to delve into a fascinating realm where artificial intelligence (AI) revolutionizes the creation of data dictionary descriptions. Have you ever wondered how AI can make complex data more understandable and accessible? Let’s explore this using the Titanic dataset from Kaggle as our guide. Here is the demo of Data Dictionary Descriptions Automation using a thin-layer application such as Microsoft Excel, Excel Labs and Open AI API.

Understanding the Basics: The Titanic Dataset

The Titanic dataset is a blend of personal and socio-economic data, presented in twelve insightful columns. Each column offers a glimpse into various aspects, with values meticulously chosen to preserve privacy and highlight essential metrics. Think of “Titanic” as an umbrella, sheltering these subcomponents – the columns. Our journey begins with understanding this dataset’s physical metadata
Physical metadata refers to information that describes the physical characteristics and context of data, particularly in the realm of physical storage and access. This type of metadata is often associated with traditional, non-digital data storage met..., examining values in each column to uncover the rich stories they tell.

Crafting Dictionary Descriptions with AI

Creating dictionary definitions demands not just expertise but also finesse. Here’s where Generative AI steps in, transforming the tedious into the manageable. Our first task involves extracting metadata from the dictionary. This metadata is then fed into a system where AI assists in integrating physical metadata into an accessible format. But what about the times when mere data isn’t enough to elucidate a column’s meaning? This is where the real data owners come into play, providing necessary context.

The Role of Personal Data Classification

In the intricate world of data management, personal data classification is a critical step. AI, with its ability to process both physical and business metadata, becomes a pivotal tool. By combining metadata into key-value pairs, we lay the groundwork for AI to generate initial descriptions.

First Drafts and the Importance of Context

Our AI journey then leads us to draft initial descriptions. Remember, these first drafts, while essential, might not have the complete picture and thus require a human touch for refinement. Once all necessary inputs and explicit contexts are gathered, we proceed to finalize our drafts, transforming technical jargon into user-friendly terms.

Linking Data and Unveiling Connections

An intriguing aspect of this process is linking various datasets and uncovering hidden connections. By isolating significant contextual attributes, AI helps us recognize similarities and differences across datasets, a vital step in data modeling.

AI’s Role in the Drafting Process

We’re now set to demonstrate the prowess of GPT-4. When fed with the right prompts and metadata, it’s remarkable how AI can interpret and generate accurate descriptions. This process explained throughout our video not only emphasizes the efficiency of AI but also highlights the need for explicit context in certain cases, ensuring that the AI’s interpretations align with our requirements.

The Transformation from Physical to Tailored Descriptions

As we move through our process, we witness the transformation of physical descriptions into tailored ones, thanks to AI’s ability to understand and integrate various data sources. The final step involves crafting a description for the Titanic table that resonates with the style preferences of our data dictionary’s consumers.

Implementation Scenario of Gen AI Data Dictionary Descriptions

Your objective is to develop an “immediate” solution (within 3 weeks) for generating Draft Data Dictionary Definitions under the following conditions and environment:

1. Fast Implementation: Develop a Gen AI “Plugin” to automate the creation of Data Dictionary definitions within your existing toolset.
2. Increase Time-To-Publish Speed: Leverage the writing speed of Gen AI to produce Draft Data Dictionary descriptions 98% faster than human writers.
3. Low AI Maturity: Without a team highly experienced in AI architecture, the solution should be a user-friendly plugin with a short learning curve. Equip your Data Dictionary teams with basic tools, allowing them to focus more on human reviewing and less on drafting definitions.

Let’s examine the steps from our video and explore options to implement Gen AI as an extension to your custom solution:

🔧 STEP 1: Acquire Physical Dictionary from Its Source

Options to collect the physical data dictionary:

1. Scan the data source using your existing connectors.
2. Automate the collection of your physical dictionaries from the sources using your own code.
3. Alternatively, do it manually through database tool queries.

🔧 STEP 2: For Each Data Dictionary Entry, Obtain Data Samples

Options to obtain sample data:

1. Utilize your existing profiling tools.
2. Create your own code for data profiling automation and ingest samples into your physical metadata dictionaries.
3. Or, do it manually through database tool queries.

🔧 STEP 3: Collect Technical or Business Context for Required Data Dictionary Entries

Workflow scenario to obtain “explicit context” (detailed information about data elements from experts or data owners):

1. Annotation Data Entry (Minimum Requirement): Provide a data entry mechanism for experts to annotate Data Dictionary elements requiring explicit context.
2. Validation Process (Desirable): Verify the provided context for accuracy and completeness.
3. Cross-Referencing Existing Repositories (Desirable): Ensure consistency by cross-referencing data elements and their context with existing documentation or metadata repositories. This may require a RAG architecture.
4. Feedback Loop (Desirable): Implement a system for reviewers to suggest modifications or additional context.
5. Final Approval (Desirable): Designate an authority (like a quality owner) for the final approval before AI processing.
6. Version Control (Desirable): Maintain a version history for the dictionary, tracking changes and updates.
7. Automated Checks (Desirable): Utilize tools to identify common errors or inconsistencies in dictionary entries.

🤖 STEP 4: Identify and Classify PII->SPI

Create a comprehensive prompt definition for Personally Identifiable Information (PII) and Sensitive Personal Information (SPI), drawing from industry standards and best practices. The definition should outline the key characteristics of PII, such as any information that can be used to identify an individual, including but not limited to names, addresses, and social security numbers. Additionally, define SPI, focusing on data that is more sensitive in nature, such as health records, financial information, and biometric data. The prompt should emphasize the importance of understanding and differentiating between these two types of data for proper handling and protection in a corporate environment

Scenario for PII classification:

1. For each data element, submit a Gen AI prompt including minimal inputs such as the physical name, sample data, and explicit context.
2. Gen AI returns PII classification values as “PII”, “SPI”, or “No PII”. This classification is essential for subcomponents like “columns”.

🔧 STEP 5: Prepare Physical Data in a Dictionary Template

Design a metadata dictionary template with your schema layout, ensuring it includes parent/child relationships and typical metadata columns (parent component, component, component type, data type, mandatory, primary key, sample data, and PII classification). Consider using unique identifiers for each row.

🤖 STEP 6: Generate Physical Descriptions Based on Physical Attributes

In this scenario, Gen AI generates a paragraph summary that does not include the conversion of physical names into natural names, nor does it adapt the format and style for a specific audience. Nonetheless, Gen AI does incorporate any available explicit context into its output. The primary objective is to supply human reviewers with initial draft definitions at an early stage, which they can then enhance by adding any missing explicit context.

Design:

1. Gen AI “Column” level physical descriptions: Include column-level prompt instructions, input of row-level physical metadata, and any available explicit context.
2. Gen AI “Table” level physical descriptions: Include table-level prompt instructions, limited row-level metadata (component name and type), Gen AI column-level output, and any explicit table-level context.

🤖 STEP 7: Generate Tailored Descriptions Based on Explicit Context

In Step 7, Gen AI’s output is a paragraph summary that transforms physical names into natural, human-friendly terms. Additionally, it customizes the content’s format, style, and tone to suit the intended audience. At this stage, having received all necessary inputs from human reviewers, we can prompt Gen AI to create the final draft definitions.

Design:

1. Gen AI “Column” level tailored descriptions: Include column-level prompt instructions for final drafts, row-level physical metadata, and available explicit context.
2. Gen AI “Table” level tailored descriptions: Include table-level prompt instructions, limited row-level metadata (component name and type), Gen AI column-level output, and any table-level explicit context.

🤖 STEP 8: Use Tailored Descriptions to Extract Natural Names

From a technical perspective, the AI developer designs a natural names prompt that extracts names from the output produced in step 7. This aids in exploring business concepts and relationships between various data dictionary tables or columns.

Summary for above implementation scenario

The proposed implementation scenario for Gen AI in data dictionary descriptions aims to enhance efficiency and accuracy. Key steps include acquiring physical dictionaries, obtaining data samples, collecting context, classifying PII/SPI, preparing data in templates, generating both physical and tailored descriptions, and extracting natural names. This streamlined approach, leveraging Gen AI, significantly reduces the time-to-publish and simplifies the process for teams with limited AI expertise.

Reflections on AI’s Efficiency and Future Directions

Finally, let’s reflect on AI’s efficiency. In minutes, AI can generate what would take a human writer considerably longer, underscoring the shift in the role of technical writers towards refining AI-generated drafts. This shift is significant, especially when dealing with complex topics requiring nuanced understanding.

Wrapping Up with a Note of Thanks

As we conclude this journey, I hope you’ve gained valuable insights into how AI is reshaping the way we approach data dictionary descriptions. It’s a world where efficiency meets accuracy, and complexity is transformed into clarity. I invite you to stay connected with us at datatunnel for more exciting explorations into the world of data and AI.

Resources

1. Titanic Dataset on Kaggle: Link to Titanic Dataset
2. Overview of Data Dictionaries: Link to Data Dictionary Information
3. Understanding Generative AI: Link to Generative AI Information

I encourage you to connect with me on social media and share your thoughts. What other ways do you think AI can revolutionize data management? Let’s keep the conversation going. Remember, in the world of data and AI, the possibilities are endless!