DCR Model Strategy Framework
The DCR Model strategy provides a structured approach for customers to evaluate, select, and continuously optimize algorithms used in the Data Classification Recommendation (DCR) engine.
The goal is to help organizations accurately detect PII and sensitive data objects while maintaining privacy, transparency, and control over their models.
Strategic Objective
To build a repeatable and secure process that:
Identifies the most effective algorithm for a given data domain (e.g., PII, financial, customer).
Enables algorithm benchmarking within enterprise boundaries.
Drives continuous improvement by refining models with additional scoring and heuristic layers.
Recommended Strategy Flow
Step 1: Establish the Objective
Define the specific business outcome — for example, “Detect all columns containing Personally Identifiable Information (PII).”
Determine the data scope (connections, schemas, or catalogs) and ensure representative coverage across data sources.
Step 2: Run Comparative Models
Create four DCR models using the available algorithms:
LLM
Deep semantic understanding, excellent for context-heavy names
Higher compute cost
Cosine
High lexical precision, efficient for structured metadata
Limited semantics
Fuzzy
Handles typos and naming inconsistencies
May cause false positives
Levenshtein
Strict character-level comparison
Suitable only for exact matches
Each model runs independently on the same dataset to produce comparable recommendations.
Step 3: Analyze Results Securely
After running all four algorithms (LLM, Cosine, Fuzzy, and Levenshtein), the next step is to analyze and compare their outputs to identify which model performs best. The analysis can be conducted in multiple ways — depending on the tools, policies, and data sensitivity requirements of your organization.
Option 1: Excel or Traditional Analysis Tools
Approach: Export the DCR model results as CSV files and analyze them manually using Excel, Power BI, or other spreadsheet tools.
Advantages:
Easy to start with — no additional platform dependency.
Familiar to business analysts.
Basic comparison and filtering can be performed quickly for small datasets.
Limitations:
Manual & time-consuming: Each comparison must be done by hand, especially across thousands of columns.
Error-prone: Human oversight and formula inconsistencies can skew accuracy findings.
Limited visualization: Static charts and no automated scoring capabilities.
Scalability issues: The system becomes inefficient for large data catalogs or multi-connection environments.
Strategic Assessment: Best suited for one-time validation or smaller datasets and not recommended for enterprise-scale or continuous model optimization.
Option 2: External AI Tools (ChatGPT, Gemini, Claude, etc.)
Approach: Upload CSV files containing model outputs to a public AI platform and use prompts to analyze recommendation overlaps, accuracy percentages, and ranking logic.
Advantages:
Quick and flexible text-based analysis.
Can interpret results semantically and summarize findings effectively.
Suitable for exploratory insight generation.
Limitations:
❌ Data exposure risk: Output files may contain column names, sample data values, or other sensitive information. Uploading such data to external AI systems can breach internal privacy and compliance controls.
❌ No enterprise auditability: Model comparisons and decisions cannot be formally tracked or version-controlled.
❌ Lack of integration: No direct linkage with governance, catalog, or DCR model management systems.
Strategic Assessment: Useful for conceptual analysis or non-sensitive datasets. However, not recommended for regulated environments (PII, financial, healthcare) due to privacy and compliance risks.
Option 3: askEdgi Platform (Recommended)
Approach: Use askEdgi — the internal AI assistant integrated with OvalEdge — to analyze DCR model results directly within your enterprise data governance environment.
Advantages:
✅ Data stays internal: No data leaves the organization’s boundary.
✅ Integrated visualization: Automatically generates charts (accuracy, agreement, and ranking).
✅ Automated comparison logic: Merges, evaluates, and ranks algorithms without manual effort.
✅ Compliant and auditable: Fully aligned with enterprise governance and privacy standards.
✅ Repeatable workflow: The same prompt sequence can be reused for continuous benchmarking.
Limitations:
Requires model outputs to be available in the OvalEdge or AskEdgi workspace.
Some configurations may need governance or technical setup permissions.
Strategic Assessment: Ideal for enterprise-grade, compliant analysis of DCR model outputs. AskEdgi combines automation, security, and interpretability, enabling continuous model optimization without compromising sensitive data.
Step 4: Determine Algorithm Ranking
After analyzing the model results from all four algorithms (LLM, Cosine, Fuzzy, and Levenshtein), the next step is to determine which algorithm performs best for your enterprise dataset. This ranking forms the foundation for model selection, tuning, and future optimization.
Strategic Approach
Establish Evaluation Criteria Define measurable indicators to assess model quality. Typical parameters include:
Accuracy % – How many recommendations were correct or contextually relevant.
Precision – Proportion of correct results among total recommendations.
Context Match – How semantically aligned the recommended term is with the column name or data content.
Execution Speed – Time required for model completion.
Processing Cost – LLM-based models may have higher compute consumption.
Prepare Comparison Dataset Consolidate model outputs into one combined dataset using the same object identifiers: Connection Name, Schema, Table, Column Name, and Recommended Term. This unified dataset allows consistent evaluation across all algorithms.
Execute Ranking Analysis Use any of the following methods depending on your governance and tool availability. AskEdgi is recommended as the secure, automated option (see prompts below).
Option 1: Manual Comparison
Approach: Manually analyze merged results using filters or pivot tables in Excel/BI. Calculate accuracy percentages for each algorithm based on validated recommendations.
Limitation: Manual calculation is prone to oversight and does not scale well across large catalogs.
Option 2: AskEdgi Automated Analysis (Recommended)
Approach: Upload all algorithm outputs into AskEdgi (Enterprise or Public Edition). AskEdgi will perform comparison, scoring, and visualization automatically through guided prompts.
Below are optional prompts TAMs or analysts can use:
🧠 Prompt 1 – Merge All Algorithm Outputs
Merge all uploaded CSV files (Cosine.csv, Fuzzy.csv, Levenshtein.csv, LLM.csv) using these keys: Connection Name, Schema, Table, Column Name. Display columns for all four Recommended Terms side by side.
📊 Prompt 2 – Identify Best Recommendation
For each row, evaluate which algorithm’s recommended term is most contextually correct (based on name similarity, meaning, or PII relevance). Add a new column called Best Algorithm.
📈 Prompt 3 – Calculate Performance Summary
Count how many times each algorithm appeared as the Best Algorithm. Create a table: | Algorithm | Times Best | Percentage | Rank |
📉 Prompt 4 – Visualize Results
Create a bar chart comparing algorithm accuracy (%) with X = Algorithm Name and Y = Accuracy %.
Title: “DCR Algorithm Accuracy Comparison.”
📋 Prompt 5 – Decision Matrix
Build a decision matrix summarizing when each algorithm should be used, based on:
Accuracy
Processing Cost
Speed
Context Understanding
Example Output: | Algorithm | Accuracy | Cost | Speed | Context Awareness | Recommended Use |
Step 5: Optimize the Selected Algorithm
Once the algorithm ranking is complete, the next strategic step is to optimize the selected model for production use. This ensures your DCR engine delivers maximum accuracy with minimal operational overhead.
Depending on your business priorities — semantic precision vs cost efficiency — two optimization paths can be followed.
Path A: Continue with the Best-Performing Model (LLM)
If the LLM model ranked highest during evaluation and your organization is comfortable with the computational cost, you can retain it as the primary recommendation engine.
However, even with LLM, accuracy can be further improved by fine-tuning its behavior using DCR configuration enhancements.
Enhancement Levers for LLM
Boost Score on Column Repetition
Rewards recurring column names (e.g., “email_id” repeated in multiple tables).
Synonym Boost
Increases smart score when synonyms of glossary terms appear (e.g., “DOB” → “Date of Birth”).
Name Regex Matching
Applies regex at the term level to identify pattern-based names (e.g., .*_id$, .*_ssn$).
Data Pattern Heuristic
Enables regex-based data pattern checks (e.g., email format, numeric ID format).
Rejection Weightage
Penalizes previously rejected recommendations to reduce repeated false positives.
Strategic Benefits
Fine-tunes the semantic context sensitivity of the LLM model.
Improves precision in ambiguous cases (e.g., “Address 1” vs “Email Address”).
Minimizes reprocessing cost through targeted recommendations.
Ensures model explainability through transparent scoring logic.
When to choose this path:
The dataset includes unstructured, descriptive, or free-text column names.
The organization prioritizes accuracy over processing cost.
LLM compute usage is acceptable under current infrastructure budgets.
Path B: Optimize the Second-Best Model (Cost-Effective Alternative)
If the second-best algorithm (typically Cosine) performs closely to LLM but offers lower cost and faster execution, it can be strategically enhanced to reach comparable accuracy through DCR’s configuration options.
This approach balances performance and efficiency, making it ideal for enterprise-scale or continuous scanning scenarios.
Enhancement Levers for Cosine (or Other Lexical Models)
Smart Score Configuration
Define custom weightage for Name, Data, and Pattern scores (e.g., 50:25:25).
Synonym Boost
Strengthens recognition of term variations (e.g., “Client ID” vs “Customer ID”).
Heuristic Toggles
Enable or disable Data and Pattern Matching selectively to refine results.
Regex Matching (Term-Level)
Identify PII columns by name or data pattern (e.g., .*email.*, [0-9]{10} for phone numbers).
Boost Score Adjustments
Incrementally increase score for pattern or data matches by a configurable value (e.g., +10).
Rejection Weightage
Deprioritize terms that previously produced inaccurate matches.
Strategic Benefits
Significantly increases contextual accuracy without added compute cost.
Delivers faster runtime and scalable performance across large data catalogs.
Maintains transparency — all score adjustments are visible in the configuration.
Ensures governance compliance by keeping processing fully internal.
When to choose this path:
LLM cost or infrastructure overhead is not sustainable.
Most column names are structured or follow defined naming conventions.
High volume of data sources requires repeatable and fast classification runs.
Outcome Example (Post-Enhancement Comparison)
LLM
47%
49%
Moderate
🥈
Enhanced Cosine
53%
↑ 58%
High
🥇
The enhanced Cosine model outperformed the baseline LLM in contextual accuracy while maintaining lower cost and faster execution — demonstrating that algorithmic tuning can surpass semantic models when configured correctly.
Step 6: Institutionalize Continuous Improvement
Once the optimized DCR model (LLM or enhanced Cosine) is deployed, organizations must treat Data Classification Recommendation as a living system — one that continuously learns, adapts, and evolves with changing data patterns, regulations, and business contexts.
Establishing a structured, recurring evaluation process ensures that the model remains both accurate and compliant over time.
Strategic Implementation Plan
Re-tune Configurations and Heuristics As data characteristics evolve — e.g., new column naming standards, emerging business terms, or regulatory changes — reconfigure:
Smart Score Weightages to balance Name, Data, and Pattern relevance.
Heuristic Controls to toggle specific boosts or regex matches.
Threshold Scores for automatic acceptance or rejection. These iterative adjustments maintain consistent recommendation quality.
Maintain Model Governance & Versioning.
Version each model iteration with metadata tags (e.g., “PII_Detection_v2.3”).
Archive prior configurations and results for auditability.
Establish approval workflows for model updates (Governance Officer sign-off). This ensures traceability and compliance with internal and regulatory requirements.
Summary and Strategic Conclusion
This DCR Model Strategy Framework provides a complete and secure path to build, evaluate, and enhance data classification models. It empowers organizations to balance AI intelligence with data governance — ensuring the DCR engine is not only smart, but also accountable and adaptable.
Key Takeaways:
Evaluate multiple algorithms fairly — every dataset behaves differently.
Use askEdgi for safe, automated, and transparent comparison.
Enhance the chosen model (LLM or Cosine) with Smart Scores, Heuristics, and Boosts.
Establish recurring evaluations to sustain model performance.
Track every version for auditability and compliance.
The Reality of AI-Based Classification
While algorithmic intelligence can significantly accelerate data governance,
no model — not even LLM — can guarantee 100% accuracy.
Every DCR recommendation is probabilistic. Models interpret metadata, not meaning, and therefore require human validation to ensure correct term associations.
Periodic manual review by Data Stewards remains essential for:
Validating recommendations with business context.
Correcting edge cases or ambiguous matches.
Reinforcing the AI model’s learning and reliability over time.
In short:
“AI can classify intelligently, but only humans can classify responsibly.”
By following this strategy, enterprises can achieve a hybrid model of automation and stewardship — where DCR operates efficiently, askEdgi provides intelligence, and governance teams ensure precision and compliance.
Final Strategic Perspective
This article concludes that:
Continuous evaluation defines long-term success.
Even the best AI model benefits from human oversight.
True data governance lies in the partnership between automation, optimization, and accountability.
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