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Technology verified Verified Outcome TRL 9

Internal Data Discovery Platform (Databook)

domain Client: A global mobility and delivery platform handshake Provider: Internal Build schedule Deploy: Q2 2018 (Internal Release)
93 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

As the company scaled to exabytes of data, engineers wasted hours finding the right datasets and verifying their quality. The engineering team built 'Databook', an internal metadata catalog that crawls all data stores (Hive, PostgreSQL, Kafka). It governs data usage by surfacing ownership, lineage, and freshness quality scores automatically.

rate_review Analyst Verdict

"An essential tool for hyper-growth tech. It solves the 'tribal knowledge' problem. By automating metadata extraction (rather than asking owners to manually document data), it ensures the governance layer remains up-to-date in a rapidly changing environment."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Data sprawled across thousands of tables. New hires couldn't distinguish between 'test_table_v2' and 'final_production_revenue'. There was no clear ownership, leading to broken pipelines when upstream schemas changed without warning.

psychology The Solution

Databook crawls the data ecosystem to build a knowledge graph. It highlights 'Tier 1' certified datasets vs experimental ones. It integrates with on-call rotation schedules to identify who owns a broken table. It provides a Google-like search for enterprise data.

settings_suggest Technical & Deployment Specs

Integrations
Hive, Vertica, Presto
Deployment Model
Private Cloud
Data Classification
Operational Metadata
Estimated TCO / ROI
Medium
POC Summary (2017-01-01 to 2018-01-01)

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Metadata Staleness Medium Automated crawlers run daily.
Adoption Low Deep integration into query tools.

trending_up Impact Trajectory

Audited value realization curve

Indexing of millions of datasets Verified Outcome
Primary KPIDaily active usage by thousands of employees
Audit CycleReduction in duplicate table creation

policy Compliance & Gov

  • Standards: GDPR (Data Mapping)
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Operational Metadata

folder_shared Verified Assets

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Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for A global mobility and delivery platform without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-868
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-868

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