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Diving Deeper on Metadata Best Practices
Implementing metadata management the right way ensures it delivers maximal business value on an ongoing basis. Here we share several advanced best practices:
Consider Security and Privacy Implications
Metadata can contain sensitive artefacts exposing vulnerabilities or revealing personal information:
- Data schema can unintentionally reveal PII
- Model details could enable adversarial attacks if leaked
- Retaining excessive metadata heightens compliance risks
Engineering leaders must conduct thorough risk reviews addressing these factors through access controls, aggregation, encryption, tiered retention policies, auditing procedures, and personnel background checks.
Build Upon Data Governance Foundations
Attempting to track metadata without a strong data governance strategy typically fails. Core components needed include:
- Data cataloging protocols
- Dataset certification workflows
- Data quality automation
- Issue escalation processes
- Universal unique identifiers
With governance established, appending ML metadata emerges more naturally.
Start Small, Fail Fast
When initially implementing metadata tracking, beginning with a minimal viable product that provides utility without excessive complexity pays dividends.
Solicit user feedback, monitor usage, and iteratively enhance functionality. Avoid "boil the ocean” attempts overly reliant on manual inputs. Prioritize automation with enough flexibility to accommodate future enhancements.
Develop Standardized Taxonomies
Carefully curated dictionaries of terms, tags, and attributes enable easier metadata grouping, searching, analysis, and maintenance.
For example, standardizing that "input_dataset" indicates a training dataset while "eval_dataset" refers to model evaluation establishes vital consistency.
Although time-intensive upfront, developing thoughtfully constructed taxonomies creates order enabling more impactful usage of metadata.
Maintaining High Quality Metadata
High proportions of stale, incomplete, inaccurate, or irrelevant metadata substantially reduces utility.
Strategies to safeguard quality include:
- Automatically flagging unchanged metadata after 6 months for confirmation checks
- Monitoring and publishing key quality KPIs around freshness, completeness, and relevance
- Spot checking metadata veracity through objective audits
- Seeking ongoing user feedback on misleading or missing metadata
With quality checks built-into workflows, metadata remains a useful rather than stagnant asset.
Industry Use Cases
Metadata tracking proves broadly valuable but has particularly transformative impacts on these industries:
Autonomous Vehicles
Self-driving depends on continual model improvements driven by fleets collectively generating over 3 petabytes of data daily.
Metadata tracking here delivers:
- Faster root causing of incidents via complete log lineage
- Accelerated transfer learning by precisely documenting model changes
- Auditable model governance through granular performance insights
For example, Mercedes Benz uses an ML metadata platform achieving:
- 2x increase in label quality
- 50% faster dataset debugging
- 30% more automated pipelines
Enhancing safety requires meticulous metadata management.
Precision Medicine
Customizing patient treatment given genetics and biomarkers relies on metadata providing:
- Reproducible models despite evolving medical research
- Clinical documentation for regulatory approvals
- Patient data privacy safeguards
A 2021 study found institutions deploying structured clinical metadata repositories experienced (Figure 1):
- 35% more enrolled clinical trials
- 65% faster trial recruitment
Figure 1. Quantitative benefits of metadata tracking for precision medicine.
Financial Services
For trading firms where minute model degradation causes million dollar losses, metadata delivers:
- Early warnings of decreasing predictive power to prevent losses
- Auditable model risk analysis for regulators
- Improved collaboration between data scientists and traders
Banks using model metadata repositories witness (Figure 2):
- 2-4% increased trading revenue
- 57% faster outage analysis
- 22% lower model risk exposures
Figure 2. Quantitative benefits of metadata tracking for financial services.
The competitive advantages conferred by metadata tracking here run into the millions.
Retail Forecasting
For retail chains, metadata enables:
- Rapid debug of demand anomalies
- Comparison between model versions
- Tracking model accuracy differences across regions
Walmart utilizes their "DataHub" metadata repository achieving (Figure 3):
- $20 million in reduced excess inventory
- 5 percentage points lower product spoilage
- 75% speedier insights for suppliers
Figure 3. Quantitative benefits of metadata tracking for retail forecasting.
The enablement of advanced forecasting techniques unlocks major value.
This small sample illustrates the transformative performance and productivity metadata unlocks across key industries.
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