1. Emerson – Driving Data Standards Enterprise- Wide Phil Love Manager, Data Quality Liebert Corporation

2. 2 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!)

3. 3 Data Mastering Approaches Considered Custom code –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’ Manual effort –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! DI DQ    

4. 4 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

5. 5 The 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 cycle Standards grow and adapt based on real- world usage Data standards are maintained and enforced by the DataLens System Data Mastering services

6. 6 Data 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 time Reduces cost Prepares the way for System migration Other forms of MDM

7. 7 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

8. 8 Standardize & validate for load Item Hub Search X-Ref External DB Cleanup legacy Transform and integrate between systems Drive Standards System-by-System, Process-by-Process

9. 9 Enterprise Data Mastering Single place to maintain all standards Single place to enforce all standards

10. 10 Drive Standards Division-by-Division Division 1 Division 4 Division 3 Division 2

11. 11 Lessons Learned: Governance, 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!