BIG DATA KICK START Troy Christensen December 2013
Big Data Roadmap 1 Define the Target Operating Model 2 Develop Implementation Scope and Approach 3 Progress Key Data Management Capabilities 4 Transition to Operate
Target Data Operating Model the Data Solution Target Operating Model Begin with the End in Mind
Data Solution Define a content based scope then standardize to common process requirements
Data Processes 1. Manage Data Strategy, Performance & Capability: Define and execute data management strategy, including governance, performance management and organizational discipline. 2. Manage Data Issues & Risks: Identify, mitigate, and resolve issues and Risks within the Data Solution. This process refers only to the data related issues and risks which have an impact on the Data Solution (other issues are managed through normal issue resolution process. 3. Manage Data Objects: Defines Data Objects, Manages Metrics, Manages Policies, Manages Metadata, Manages Data Attributes and Manages Data Standards. 4. Manage Data Template: Enables data to be bulk loaded into the Data Solution in compliance with defined standards. 5. Manage Data Content: This process manages data content via agreed on hierarchy (e.g. global vs. regional) 6. Manage Data Audit: Enables the data audit requirements of the business processes to be traced and analyzed 7. Monitor Data Quality: Enables the data quality of each data object to be managed to requirements of the collective business processes
Data Roles DRAFT DATA MANAGEMENT ORGANIZATION Role Assignments USERS SUPER USERS REGIONAL ROLES P2P PROCESS & DATA REGION P2P REGION O2C REGION R2R REGION PROCESS P2P DATA STEWARD O2C PROCESS & DATA O2C DATA STEWARD R2R PROCESS & DATA R2R DATA STEWARD PROCESS & DATA DATA STEWARD IT&S ROLES GLOBAL SERVICE MANAGER SERVICE DELIVERY MANAGER USER MANAGEMENT SUPPORT LEVEL, 1-3 APPLICATION SMES FUNCTIONAL SMES SEGMENT ROLES P2P PROCESS & DATA P2P DATA STEWARD SEGMENT P2P SEGMENT O2C SEGMENT R2R SEGMENT PROCESS P2P PROCESS LEAD P2P VENDOR RELATIONSHIP MGMT O2C PROCESS & DATA O2C DATA STEWARD O2C PROCESS LEAD R2R PROCESS & DATA R2R DATA STEWARD R2R PROCESS LEAD PROCESS & DATA DATA STEWARD PROCESS LEAD GBS ROLES GLOBAL P2P PROCESS GLOBAL P2P SPECIALIST GLOBAL O2C PROCESS GLOBAL O2C SPECIALIST GLOBAL R2R PROCESS GLOBAL R2R SPECIALIST GLOBAL PROCESS GLOBAL SPECIALIST GLOBAL DATA DIRECTOR GLOBAL DATA SPECIALIST Today's Date: 18-Sep-13 Without organizational roles, data process won t execute
Data Tools Big Data tools will likely enable the target operating model, but we simply can t define any tool requirements until the operating model requirements are landed
Big Data Implementation Implementation involves four objectives: Standardization The standards, policies or rules that define the target state (what good data looks like) are gathered and documented. Standards are then mapped to the business processes that use them. Standardize Harmonization Data sources are documented and each data source is mapped to the data standard. Scorecards are developed to compare data sets from disparate sources against common standards. Alignment Scorecards are analyzed to identify gaps in current data source against common data standards. Gaps are prioritized into remediation projects and funding secured. Operate Harmonize Operate Harmonization and alignment activities are periodically repeated as data standards continue to mature. Tools are implemented to improve SLA s and reduce costs. Align 8
Key - Defining the Start Point Pilot an Achievable Objective Data is a critical input to every business process we execute daily. We can ensure it does not cause our business processes to fail by managing our core data to specific standards. We can demonstrate its value by deploying a proven model to a limited set of data subject areas. Land Initial Scope Vendor data is a good data subject area candidate to pilot a data management operating model because: Relevancy Vendor data impacts many business processes across many different segments and functions. There is value to ensure it does not impede someone s ability to quickly procure the right materials or services from the most cost effective source. Reusability Vendor data standards and data quality scorecards already exist, so we can quantify and address the vendor data quality gap with fewer resources. Data silos weren t built overnight and they won t be removed overnight, but common processes won t function properly until data silos are removed. 9
Big Data Dependency Matrix Maturity Level Common Process Data Requirements Organization/ Governance Data Tools Architecture Level 1 Common Business Processes Documented Data Process Requirements Defined Common Data Maturity Model Landed Data Tool Requirements Documented Data Flows Diagrammed Level 2 Data Scope Defined Data Standards Common Data Organization Landed Tool Strategy Defined Data Architecture Strategy Defined Level 3 Data Sustain and Support Processes Defined Data Metrics and Scorecards Common Data Governance Landed Common Data Tool Gaps Identified Data Architecture Gaps Identified Level 4 Data Sustain and Support Processes Integrated Data Quality Performance Functional Data Management Capabilities Developed Common Data Tool Roadmap Developed Data Architecture Roadmap Developed Level 5 Common Business Processes Optimized Data Quality Enforcement Functional Data Management Capabilities Optimized Common Data Tool Set Implemented Common Data Architecture Implemented Dependencies generally run left to right, top to bottom. To understand the requirements for a common data architecture and tool set, we first need to develop common data processes, data standards and governance capabilities. 10
Big Data Activity Sets Standardization Gather data standards across domain Determine common format for capturing global data standards Document data standards using common formatting Share with segment data stewards and gain consensus on group standards Map standards to global process Harmonization Identify all data sources Map data sources to group data standards Develop scorecard to measure data quality gaps Run scorecards against each data source Alignment Identify data quality gaps from each scorecard Use global process mapping to assess impact and risks of each gap Prioritize remediation activities Build scorecard to describe impact of improved vendor data quality Secure funding, execute remediation and monitor progress Operate Monitor benefits and costs Develop data management strategy Identify cost effective data management tools Prioritize implementation across remaining data objects 11
Transition to Operate Measure impact of the pilot program in terms of the business case. For example, for vendors, metrics could be: Safety and Cost Drivers Invalid D&B Numbers result in orders from vendors who are not authorized to do business with company Incorrect shipping addresses cause delays in receipt of supplies that results in increased down or idle time Safety Drivers Incorrect vendor data result in non-oem part being shipped or services in lieu of OEM parts or services Cost Drivers Missing vendor data result in increased invoice cycle time resulting in additional processing costs and inability to realize payment discounts. Maintenance of large number of inactive vendors results in erroneous analytic reports and higher maintenance costs. Improper vendor addresses lead to incorrect tax calculations and collections Incomplete vendor data requires additional resources to maintain and process orders and invoices. Poor data quality in vendor names results inability to search for and find appropriate vendor in a pick list. Invalid contact information results in inability to contact vendor to correct errors which results in processing delays 12
Summary - Big Data Kick Off Milestones Define the Target Operating Model Solution Process Roles Tools Develop Implementation Scope and Approach Standardize Harmonize Align Operate Progress Key Data Management Capability Navigate the Dependency Matrix Execute the Activity Set Transition to Operate Ensure Big Data benefits are measurable and traceable
Final Take Away For most companies, kicking off a Big Data initiative is really about developing foundational data management capabilities (standardization, harmonization, alignment and operations) across respective business lines. Unless these core foundational capabilities exist or are developed as part of the program, Big Data can t deliver its full potential.
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