Halliburton: Moving up on the Enterprise Data Management maturity scale in upstream Oil & Gas services company

Transcription

1 [ Halliburton: Moving up on the Enterprise Data Management maturity scale in upstream Oil & Gas services company Gerald Hughes, Halliburton Energy Services Nitin Joshi, SAP America

2 [ Learning Points How to align enterprise data management to business value? How to run data track in a major business transformation initiative? How to move up the maturity scale of Enterprise Data Management for sustained data quality? 2

3 [ Agenda Halliburton Energy Services Business Overview Current State of EDM maturity Opportunity To Cash (OTC) Program Overview Data Management track as an integral part of the new OTC Program Future plans for EDM Q&A 3

4 [ Halliburton Business Overview An oilfield services company and a provider of engineering and construction services Overview Founded in Revenue: $24.8 B Employees: 68,000 Operations in number of countries: 80 Services & Products Completion and production segment Cementing, PE, Completion tools Drilling and evaluation segment Baroid, Sperry, Security DBS, Landmark, Project Management 4

5 [ Current State of Data Management - Overview A single global instance of SAP ERP is the system of record for most major master data objects such as Service & Inventory Materials, Customers, Equipment, Employee, Suppliers and Financial master data A Global Master Data (GMD) team exists under a Centralized Services organization in Finance that is responsible to maintain master data in the single instance of SAP ERP Master Data Create & Change processes are based on predefined Excel based forms communicated to a functional mailbox in Microsoft Outlook. No workflows used. Security in SAP is controlled so only GMD has access to master data create transactions 5

6 [ Current State of Data Management - Organization Finance Centralized Services GMD Manager Landmark Channel Operations Master Data Group (10) Business Partner Manager Service Materials (1) Inventory Materials (1) Financial Master Data (6) Customer Master (2) Vendor Master (5) GMD = Global Master Data 6

7 EDMM-1 EDMM-2 EDMM-3 EDMM-4 EDMM-5 [ Current State of Data Management Maturity Scale People Process Information Technology Data Quality accountability at senior management and sponsorship at CXO level exists Process change control (governance processes) is defined and managed Data is of high quality. It is secure. It is governed using data strategy and policies. DQ software, meta data management, external data provider enrichment, web services are used Enterprise stewards are defined per data object Mark for deletion and archiving processes are managed Business usage of data is known and tracked. Data critical to business is known. Enterprise MD applications with workflow and business rules engines are used Business Unit, Regional and Functional data stewards are defined Enterprise wide Search process is managed Data SOR s are defined. Data origination and consumption is controlled. Workgroup software based workflow is used to coordinate flow of work Data maintenance or administration roles are defined Create and Change processes are defined and managed Data is defined and standardized. Forms based on Office software and are used No formalized roles for master data maintenance No formalized definition exists for any of the SCRUD processes Data* is not defined No investments exist in technology to manage master data Service Material Inventory Material Customer Well * Data refers to meta data, reference data and master data 7

8 [ Current state gaps and opportunities Lack of data quality monitoring & fixing errors No reporting on Data Quality No focus on mark for deletion No business rule validations in SAP specific to data quality Lack of governance during local (plant level) extensions 8

9 [ Overview of OTC program High level business objectives of the program Standardize & simplify Opportunity to Cash business processes Improve field productivity using mobile technology Optimize field delivery and support structures Maximize district operations organization structures Simplify supply chain, distribution and procurement 9

10 [ Finding Business Value in Data Management #1 Where to look when making a case for data management? Decision making What is the profitability of this proposal? How much discount should we offer to this customer? What are the relevant case studies available for this proposal? How much production can we achieve for this customer at this location? What is the credit rating and available credit of this customer? Reporting (Internal & External) Report top 10 customers Post job profitability analysis Material gross profit Equipment & Personnel Utilization Customer segmentation by revenue and type Operational Efficiency Find right material faster Find right equipment faster Find right sold-to customer faster Improve cash flow using E-billing Compliance Classify customers and materials accurately for foreign trade compliance Tax Compliance Pricing compliance per contract terms M&A Integration Efficiency Match and reconcile customers, suppliers and materials faster Mass create and update of master data 10

11 Master Data Business Processes Business KPI [ Finding Business Value in Data Management #2 Business Acquisition Operations Invoice & Collection # of opportunities generated % proposals won Revenue from new technology Proposal turnaround time Time to schedule a job Days Sales Outstanding Time to process shipment Invoice completion time Revenue per crew unit Number of credit % utilization for labor & equipment memos and write-off s Cost of foreign trade compliance failure Set base & market prices Evaluate Customers Identify & evaluate leads Develop & submit proposals Process service order Confirm Services Schedule crew & equipment Process material staging order Fulfill material order Invoice packet validation SAP Invoice creation Monitor & follow up on collection Create & manage dispute Prospect Customer Customer Hierarchy New Well Permits Pricing relevant material attributes Inventory Materials Service Materials Ship-to Customer Equipment Personnel Activity Types Business Partner Sold-to & Payer Customers Material Pricing Material Descriptions 11

12 [ Data management track - Organization DMG Migrate Correct source data in SAP and Migrate to target applications Archive Govern Migrate Extract Profile Cleanse Transform Validate Load Reconcile Migration Analyst Functional Material Master 1 Migration Analyst Functional Customer & Well 1 Migration Analyst Functional Equipment & Activity Type 1 Project Manager 1 Migration Analyst Technical SAP Data Services 2 Solution Architect 1 Business Lead 1 Govern Define Sustain Data Quality on an ongoing basis Standardize Design Process Assign Accountability Define DQ Metrics Archive Planned for later Governance Analyst Functional - Material Master 1 Governance Analyst Functional - Customer & Well 1 Governance Analyst Functional - Equipment & Activity Type 1 Governance Analyst Change Management 1 Governance Analyst Technical (ABAP) 3 12

13 [ Data management track - Interactions Business Leads and SME s Requirements Requirements Process Team Reporting Team Requirements Services Data Team Requirements Services Requirements Requirements OCM Team Organization Change Management Services Services Technical Teams Development, Testing and Security

14 [ Data management track - Deliverables 3 months 6 months 4 months 4 months Prepare Analyze Design Build Program Business Case Data Migration Requirements Data Standards RICEFW Build Program Scope EDM Strategy EDM Scope Data Extraction Specification Data Profiling Report Process Flows (SCUD) Business Rules Roles & Responsibilities Security Build Training Build Data Cleansing Proposal & Specs RICEFW Specs EDM = Enterprise Data Management RICEFW = Reports, Conversions, Enhancements,, Forms, Workflows Data Governance Requirements Per master data object Cross object

15 [ Data management track Tools & Architecture

16 [ Customer Master DMG Mark for Delete 58K out of 82K customers marked for deletion Archive Migrate De-duplicate About 500 customer records de-duplicated Govern Cleanse Clean up legal entity names Clean up Sold-to and Ship-to relationships Define data Define data entity and its attributes Define uniqueness criteria for sold-to, ship-to etc. DMG Archive Migrate Govern Define process & rules Enforce business rules Refine roles & responsibilities for request, approve, create and oversight stewardship Define rules that govern creation of a new customer record Implement validations at the time of create and change Control security Monitor Quality Monitor sold-to per legal entity 16

17 [ Material Master DMG Mark for Delete 625K materials marked for deletion out of 1.7M total Archive Migrate De-duplicate No redundant duplicates found Govern Cleanse About 20K new PH codes created and 75K materials updated Clean up customer price catalogs Define data Define inventory material & service material Standardize descriptions based on classification DMG Archive Migrate Govern Define process & rules Enforce business rules Define rules for catch all materials usage Govern local material extensions Use CMIR to eliminate need to create duplicate materials Monitor Quality Generate auto description Validate PH assignment Pick right material for shipment using material determination Control security Monitor material type, PH, material group, classification and description 17

18 [ Well Master & Ship-to Customer DMG Mark for Delete 600K ship-to s out of 825K marked for deletion Archive Migrate De-duplicate About 8% duplicates found and de-duplicated Govern Cleanse Well type and well category fields cleansed Ship-to to sold-to & rig partner relationships cleansed Define data Define well, ship-to, rigs Define global unique identifier for well Define well master attributes with valid values Archive DMG Migrate Define process & rules Define roles and rules for ship-to creation Establish cross-reference between well and ship-to Define process to feed well data from external provider permit feeds Govern Enforce business rules Enforce validations for well attributes Monitor Quality Monitor ship-to completeness and relationships among shipto, wells and rigs 18

19 EDMM-1 EDMM-2 EDMM-3 EDMM-4 EDMM-5 [ Future State of Data Management Maturity Scale People Process Information Technology Data Quality accountability at senior management and sponsorship at CXO level exists Process change control (governance processes) is defined and managed Data is of high quality. It is secure. It is governed using data strategy and policies. DQ software, meta data management, external data provider enrichment, web services are used Enterprise stewards are defined per data object Mark for deletion and archiving processes are managed Business usage of data is known and tracked. Data critical to business is known. Enterprise MD applications with workflow and business rules engines are used Business Unit, Regional and Functional data stewards are defined Enterprise wide Search process is managed Data SOR s are defined. Data origination and consumption is controlled. Workgroup software based workflow is used to coordinate flow of work Data maintenance or administration roles are defined Create and Change processes are defined and managed Data is defined and standardized. Forms based on Office software and are used No formalized roles for master data maintenance No formalized definition exists for any of the SCRUD processes Data* is not defined No investments exist in technology to manage master data Service Material Inventory Material Customer Well * Data refers to meta data, reference data and master data 19

20 [ Challenges faced and lessons learned Upstream data entry users resist change. Bringing about cultural changes needs top level commitment and time. Need to manage organization change very actively from the beginning. Enforcing standardization across all business units globally was challenging Aligning program s regional roll-out scope with master data that is essentially global was challenging Program management and business process teams need data education through the program lifecycle Business users time is very precious. Do more within the data team to use business users time wisely. 20

21 [ Future Plans Cleanse data and extend governance processes to Global scope from the current regional scope Implement SAP MDG for materials, customers, suppliers and financial master data objects Continue to progress on the data management maturity scale across other master data objects 21

22 [ SAP MDG Demo 22

23 [ Q & A 23

24 [ ] Thank you for participating. Please remember to complete and return your evaluation form following this session. For ongoing education on this area of focus, visit the Year-Round Community page at [ SESSION CODE: