Johnson Controls. Accelerate Performance with Global Information Governance. April 2015

Transcription

1 If the you can read this Click on the icon to choose a picture or Reset the slide. To Reset: Right click on the slide thumbnail and select reset slide or choose the Reset button on the Home ribbon (next to the font choice box) If the you can read this Click on the icon to choose a picture or Reset the slide. To Reset: Right click on the slide thumbnail and select reset slide or choose the Reset button on the Home ribbon (next to the font choice box) Johnson Controls Accelerate Performance with Global Information Governance April 2015

2 Wherever they live, work or travel, people all over the world are touched by Johnson Controls products and services every day.

3 We are where our customers need us to be 170,000 employees serving customers in 150+ countries 3

4 Overall System Landscape SRM PLM ECC ERP k 30 BW Sites Users Countries 4

5 Overview of EIM Requirements / Processes Provide a platform to support the establishment of data governance practices; specifically around master data and metrics Implement a repeatable factory approach for ingesting data from our various data sources Elevate the data quality, prior to migration onto Unity during high velocity deployments Consolidate data and resolve conflicts across our business units prior to deployment wave 5

6 Unity Overview Support the Johnson Controls Operating System. Give Johnson Controls one integrated technology backbone. Make it easier to add and reorganize business segments. Deliver a shared source of data to help the company make more informed decisions. Equip the company with a consistent set of capabilities to run our businesses and serve our customers. 6

7 EIM Solution Unity SAP Suite (HANA) APO Unity EWM Unity SRM Unity MII PS PLM HANA Live ILM (Retention Policy) ILM (Archiving) CPS Redwood (SAP Job Orchestration) Fiori and Persona based U/X (Mobility) IT Tools Solution Manager Tivoli TWS (Enterprise Job Orchestration) Presentation Platforms Fiori Smart Business Analytics Microsoft BI tools (Self Service) SSRS Power BI SSAS PPS o Lake Johnson ( Lake) SDA Enterprise Warehouse (HANA) Predictive Analytic Library XS Application/API Engine Graph base Engine Lumira Visualization Server SDA SAP SLT SDA Unity BW (HANA) Baseline Solutions Harmonization (Hive - Hadoop) Initial Domain ILM (Decomissioning) Frozen Archive (HDFS Hadoop) Obfuscated (Risk Mitigation) Tokenized (Risk Mitigation) Ingestion Area (HDFS - Hadoop) Governance (HANA) Domain s Info Steward Partner Domain Material Domain Finance Domain Reference Domains Services Application Linking Enablement (ALE) Services Fiori U/X (Mobility) Consolidation Central Number Service Domain Search Key Mapping X-Ref Value Mapping X-Ref EI Enterprise Integration (EI) BE-PLM (TBD) Workday Operational Monitoring (Manage Engine, SSCM) Modeling Tools AE-PLM (Team Ctr) Peoplesoft Hana Studio ARIS Architect Taulia (einvoicing)... SAP BO (BI Platform) Lumira Infographics (ext-d3/js) Web Intel Explorer Crystal Design Studio HTML5/Responsive (bespoke) JDBC/ ODBC REST API Normalized Warehouse Extended Rosetta Stone (Cross-reference) Secondary Dimensions Domain Dimensions Virtual marts Encyglonary (Enterprise Metadata) SDA S D A Functional Transactional Innovation (HDFS - Hadoop)... F T L Raw Application Legacy Warehouses (Co-existence) Business DWs Privacy Fortress Legacy BWs EL Geocoding Service Address Cleansing Service Back Office Associates (BOA) Facilitated Cleanse JDBC/ODBC WIP Conversion Staging (MS SQL) Deduplication Consolidation JDBC/ODBC Staging Migration Staging Final Conversion Staging (Oracle) BODS Adapter EL CRM (SFDC) PS-MFGPro (QAD) AE-ASIA (QAD) BE-MFG Enterprise Applications (To Remain) Legacy ERPs (To Be Retired) BE-NA (Oracle ebiz) PS-ES PS-EMEA AE-Saturn BE-Symix... SDA Metadata Repositories Troux SAP Metadata Manager ARIS SAP Metapedia Datum IVM SAP Solution Manager Hadoop HCAT SAP Universes Embarcadero Team Server 7

8 MDM Deployment Cycle Approach Load Source Extract Deployment to the MDM hub and adoption of the master data business process is a key to quality deployments Verify Rationalize Cycle Validate Stage Cleanse The longer a system is under master data processes the lower the conversion effort Early landing of legacy source data to the data lake allows for quality and completeness checks and remediation (health assessments) Consolidation, rationalization and archiving reduce the size and complexity of master data and Unity deployments Rosetta Stone completeness increases with each deployment Will support & enable interim state/co-existence capabilities Pull-forward from future Unity s will be enabled by this iterative approach Rosetta Stone is an enabler to unified enterprise reporting and analytics being delivered to the business earlier then actual deployments Unity Deployment Unity Deployment Unity Deployment Unity Deployment Unity Deployment 8

9 Governance The SAC is considered JCI Business Best Practice for managing Suppliers Objective meld SAC and MDG leading practices together allowing JCI to leverage existing benefits while gaining the benefits of an industry leading packaged MDM solution Prototype built in eight (8) weeks; from platform installation though testing. MDG-S effort was less than ¼ what it would have taken with bespoke ABAP Prototype demonstrated to the Procurement,, and JCI Unity Program Leadership; resulting in approval to proceed and project added to the Unity program 9

10 MDG Platform Agility and Flexibility Rules Revision 1 Revision 2 Revision 3 Revision 4 Revision 5 Process Version 1 Version 2 Version 3 Application Release 1 Release 2 AGILITY The MDG configuration based process and business rule lifecycles allows for reduced delivery time and effort when implementing business requirements FLEXIBILITY Rule based approach to define and execute validation and routing requirements within the process workflows provides a declarative method to meet complex use cases. 10

11 Significant Accomplishments & Challenges MDG on HANA (one of the earliest production installs) Deployment of lean create and approval applications on Fiori platform Smart Business Analytics implementation as part of MDG deployment Successful prototype leading to re-scheduling to implement ahead of major enterprise ERP consolidation/standardization. Initial data profiling scalability; IS/DS platforms provide excellent capabilities for interactive and even Enterprise scale, these capabilities were challenged when used in the context of our data lake. 11

12 What we like? MDG flexibility and development time (compared to home grown systems being replaced) Information Steward s user experience empowers users to drive more active governance and control of data quality Mobility capabilities provided by MDG allows organization to drive processes out to the edge Use of HANA as Enterprise Semantic Layer for our Enterprise initiative SAP willingness to partner - SAP Development and Solutions Teams supporting newest technology 12

13 Recognition of the Implementation Team JCI Leadership for providing the support and having the confidence to allow us to innovate. JCI Team Basis, Unity Technical Architecture, Architecture, Quality, Integration, Governance, and Unity Design-Build teams SAP Services MDG Architecture, Fiori, HANA implementation resources DATUM EIM Architecture, Strategy, MDG implementation resources Migration Resources (DMR) Information Steward/ Services implementation and execution resources 13