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Capability | Data and Artificial Intelligence

AI on bad data is error, automated.

We structure data governance with quality, lineage and traceability so that AI, analytics and decision run on a base you can trust: not on tidy garbage.

Data governance by the numbers

47%

of records created contain a critical error; only 3% meet minimum standards

Nagle, Redman & Sammon / HBR 2017 ↗
65%

of inventory records are incorrect; 28% of stock value compromised

DeHoratius & Raman / Management Science 2008 ↗
8–12%

of revenue destroyed each year by poor quality data

Redman / ACM 1998 ↗
78%

of clinical data unreadable when moving between systems from different vendors

Bernstam et al. / JAMIA 2022 ↗

The risk nobody traces

47% of records contain a critical error. Does your operation know where data breaks, or does it find out once the dashboard has already lied?

When data runs without quality, without lineage and without traceability, every report tells a different story. The result is widespread distrust, decisions built on noise, and AI amplifying error at scale: cycle after cycle.

The real scenario

Four failures that make any analytics or AI useless in practice

Each one runs in silence. Together, they guarantee the company will not trust its own numbers.

01

Duplicate data with no deduplication

The same customer shows up three times under different names. The same product under distinct codes. With no governed deduplication, every report adds up what it should consolidate, and the final number does not represent reality.

02

Conflicting sources with no source of truth

CRM, ERP and spreadsheet disagree on the same indicator. With no single definition per metric, each area defends its own number, and the executive meeting turns into a debate about which source is right.

03

Transformations with no lineage record

Data goes in one way and comes out another, with no trace of who transformed it, when and why. When the number does not add up, nobody knows where the error started, and the investigation eats days that should have produced a decision.

04

Revenue destroyed by poor quality data

8–12% of revenue destroyed each year by bad data. With no continuous quality monitoring, the cost stays invisible until the accumulated total shows up at closing, and a retroactive fix no longer recovers the value lost.

Redman / ACM 1998 ↗

Trace­able Data Gover­nance

Bunker

We have seen this before. And we know where data complexity hides.

Data-driven operations do not fail for lack of information. They fail because quality, lineage, catalog and monitoring run as disconnected dimensions. The Bunker Protocol connects those layers into a single architecture: with governance, traceability and institutional trust.

We do not clean data. We design the operation that keeps every data point trustworthy from the source.

  • 40+ B2B operations with data governance installed
  • 300+ CRM projects with structured data quality
  • 8 countries with active data traceability
  • Documented reduction of inconsistencies in 65%+ of cases

The Bunker Protocol applied to Data Governance

Four phases. One data architecture. Auditable governance.

Phase 01

Quality Diagnosis

We map the data ecosystem end to end: sources, transformations, points of consumption and quality levels. We identify where data breaks, where duplication starts and where inconsistency between systems creates distrust in the reports. The diagnosis reveals the real cost of informality in data.

Outcomes
  • Quality map by source, entity and critical field
  • Estimated cost of decisions based on inconsistent data
  • Prioritization of fronts by impact on reliability and operation
Phase 02

Lineage Architecture

With the diagnosis in hand, we design the lineage architecture: traceability of every data point from source through transformation to final point of consumption. Every transformation gets a record, every field gets an owner and every metric gets a single definition.

Outcomes
  • Lineage documented from source to consumption per critical entity
  • Transformations with record, owner and criterion
  • A single definition per metric and field in the data catalog

Lineage

Every data point with a traceable source. Every transformation with a record.

Phase 03

Catalog and Rules

We formalize the data catalog with a business glossary, validation rules and quality criteria per field. We install deduplication rules, validation on entry and anomaly monitoring. Data comes to have an owner, a criterion and a control before it feeds any report or model.

Outcomes
  • Data catalog with glossary and owner per field
  • Validation and deduplication rules active in the operation
  • Anomaly monitoring with configured alerts
Phase 04

Monitoring and Hand-off

We install a governance dashboard with visibility of quality by source, adherence to the rules and evolution of the indicators. The operation advances in waves, with progressive autonomy. The goal is for your team to govern data without depending on us.

Outcomes
  • Governance dashboard with quality by source and entity
  • Adherence and anomaly indicators on a defined cadence
  • Operational autonomy handed over to the internal team

Transformation

From data nobody trusts to governance with institutional traceability

Without Bunker

Data nobody trusts

  • Duplicate records with no governed deduplication
  • Conflicting sources with no single metric definition
  • Transformations with no lineage record
  • Reports telling different stories about the same indicator
  • 8–12% of revenue destroyed by poor quality data

With Bunker

Data with governance and trust

  • Governed deduplication with rules active in the operation
  • Catalog with a single definition per metric, field and owner
  • Traceable lineage from source to point of consumption
  • Reports converging on the same source of truth
  • Continuous quality monitoring with alerts and cadence

Every month of ungoverned data is a decision made on noise, and trust that keeps eroding.

The first step is a quality diagnosis. No commitment, no generic slide deck. Assess whether your data scenario justifies a different governance architecture.

Talk to a specialist Request a comparable case

This service is part of the capability Data and Artificial Intelligence.

See also: Analytical Modeling and Segmentation | Applied AI for Commercial Operations | Predictive Intelligence and Forecasting