Emerson – Driving Data Emerson – Driving Data Standards Enterprise-Standards Enterprise-Wide Wide

Phil LoveManager, Data QualityLiebert Corporation

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Emerson Corporation Emerson Corporation

$20B diversified global manufacturer

Growth through acquisition

Historically autonomous operations– 22 Divisions– 40 Systems– 268 Locations

Heterogeneous IT landscape

Siloed operations

Good News: Across the board migration to

Oracle Applications

Bad News: Data inconsistency will make the

transition difficult

The Answer: Single-instance MDM (eventually) Enforce standards across the

business (NOW!)

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Data Mastering Approaches ConsideredData Mastering Approaches Considered

Customcode

– Expensive to build and maintain– Performs poorly with

unpredictable/unstructured data– Difficult/expensive to maintain

Traditional software

– Performs poorly with unpredictable/unstructured data

– Relies on custom code ‘extensions’

Manualeffort

– Expensive– Non-scalable– One-time fix– Inconsistent result

Custom code

Semantic-based Data Quality (Oracle Product Data Quality)

– Scalable, consistent results– Designed for non-standard data

in many categories– Actually works!

DIDQ

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Why is the data so poor?Why is the data so poor?

Enterprise Data

Enterprise Standards

• Data spread across many systems

• Many differing objectives• Much good-faith effort,

but no consistency or scalability

• Inconsistent• Missing information• Different formats • No standards or different

standards

?No practical way for standards to be •Agreed •Coordinated•Enforced

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The Missing Link: Standards EnforcementThe Missing Link: Standards Enforcement

Enterprise Data

Enterprise Standards

• Standards are built into Data Mastering Process

• Constant feedback improves the standards

• Data is evaluated against standards

• Immediate integrated remediation, as required

• Custom publishing for systems that need data in a different form

Enforcement is a virtuous cycleStandards grow and adapt based on real-world usage

Data standards are maintained and enforced by the DataLens System Data Mastering services

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Data Mastering BenefitsData Mastering Benefits

Automation of– Classification

• For procurement (UNSPSC, DRI)

• Import/export regulations (HTS)

– Attribute standardization

– Description standardization

– Enrichment– Validation

Increases the value of our data!

Drives quality & consistency

Reduces lag timeReduces cost

Prepares the way forSystem migrationOther forms of MDM

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Data Mastering – Phased Rollout Example Uses Data Mastering – Phased Rollout Example Uses

Phase 1 – Build the engine– PLM clean-up – enrich, standardize, identify duplicates

Phase 2 – Expand across the Division– Interim Item hub – cleanse, de-dup & load– International divisions – translation, systems cut-

over– Migrate to Oracle Apps – load, standardize, validate – Expand coverage – to Assemblies & Finished goods

Phase 3 – Expand across Divisions/Enterprise – Corporate material catalog – standardize and validate – Data Warehouse – standardize, classify for reporting– Procurement – standardize classifications and feeds – Partner Portal – translate languages, optimize for search– Pricing system – standardize & validate load data

Productivity: Broad automation allows focus on exceptions

Leverage: Ultimately, Data Mastering will touch ~75% of enterprise systems

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Standardize & validate for load

ItemHub

Search

X-RefExternal

DB

Cleanup legacy

Transform and integrate between systems

Drive Standards System-by-System, Drive Standards System-by-System, Process-by-ProcessProcess-by-Process

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Drive Standards System-by-System, Drive Standards System-by-System, Process-by-ProcessProcess-by-Process

Enterprise Data Mastering•Single place to maintain all standards

•Single place to enforce all standards

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Drive Standards Division-by-DivisionDrive Standards Division-by-Division

Division 1

Division 4

Division 3

Division 2

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Lessons Learned: Lessons Learned: Governance, Standardization and MDMGovernance, Standardization and MDM

Think Big – start small

‘Traditional’ approaches won’t work – not generalizable or scalable

Semantic-based Data Mastering with the DataLens System– Delivers rapid tactical benefits– Allows for phased rollout – Avoids traditional data management ‘gotchas’

Necessary starting point for any MDM strategy– Data Standardization– Data Remediation– Data Governance

– Development and Enforcement of Standards!