What is Master Data Management and why is it so important? Date: February 2016

What is Master Data Management and why is it so important? Date: February 2016

Agenda Introductions KPMG Approach to Master Data Management Panel Discussion Questions from the Audience 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 1

Why do we need to improve our Master Data Management now?

Introduction to our MDM vision & methodology Common master data issues and business impact Low quality master data has direct negative business impact Indicators of poor MDM Organisational Discussions around data ownership, for example with data migrations Complaining about not having the right management information Technical Discrepancies in data over multiple systems Widespread access to master data transactions Corrupted or out of date information, without periodic data quality reporting Tangible Process re-work High volume of invoice disputes Failed marketing projects Intangible Complaints regarding incorrect master data Unknown who to contact for issues with master data Business impact of poor MDM Process re-work Unreliable reporting Missed cost saving opportunities Customer dissatisfaction Missed business opportunities Compliancy issues; no single view of master data objects Increased time to market Damage to reputation brand image Failed data migrations on average, re-work takes 10 times the effort of first-time-right (source: Philips) 30% of item data used for stock replenishment is in error (source: A.T. Kearney) database management has an 80% impact on the success of the marking campaign (source: FEDMA) and at 90% data quality, only 53% process efficiency is achieved (source: Philips) 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 3

What is Master Data Management and why is it so important? KPMG s view on MDM

Technology focus Business focus Introduction to our MDM vision & methodology KPMG s view on master data management Master Data Management (MDM) is the set of procedures, governance, policies, standards and tools that manages master data in an organisation. We recognise four key building blocks of MDM: Governance, Process, Content & Quality, and Systems & Tooling. Strategic Governance Set clear (global) ownership and accountability for master data. Establish hierarchy of responsibilities that provides the oversight to resolve issues, enforce standards, improve the overall process, and enhance the quality of the master data. Create MDM organisation and operating model. Provide overall vision, strategy and guidance. Tactical Operational Proces s Consistent and standardised process models for master data management, aligned to business processes. Clear operational roles, tasks and responsibilities. Process documentation: models, descriptions and flow diagrams. Focused on four essential processes: data migration and integration, data maintenance, data quality assurance and control, data archiving. Content & Quality Set and monitor content and quality standards for master data and its attributes. Master data inventory and classification. Standard master data definitions. Master (and meta) data model and architecture. Metrics and business rules for master data quality. Data cleansing and data enhancement procedures Integration with business controls and risk compliance. Master data quality reporting. Systems & Tooling IT systems and tooling to support governance, compliance with quality standards, and operational processes. Identification of source systems and target systems. Master data (technical) architecture Mapping of master data attributes in source/target systems Operational procedures for source/target systems Workflow tooling Interfaces, ETL data quality management tooling 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 5

Master Data Management in perspective: Enterprise Data Management

Introduction to our MDM vision & methodology The bigger picture: Enterprise Data Management At KPMG we consider Master Data Management to be part of a bigger picture: Enterprise Data Management (EDM). This contains all elements around data lifecycle management, data analytics and data architecture. Data lifecycle management This domain contains master data management and data quality. But it also contains Enterprise Content Management (ECM), the management of unstructured data (documents, records, digital assets and web content). Data security is a ever growing worry for organizations. And Big Data is more and more a trend to gain insights in the outside world. Data governance is the function that organizes and controls the data life cycle. Data analytics Electronic document discovery is used to retrieve information around a certain topic out of all documents and records of a company. This is often used in legal disputes. Data mining is the process that attempts to discover patterns in large data sets. The goal is to extract information and to transform for further use. Business intelligence aims to support business decision making by providing reports, analytics and dashboards. Data architecture Hierarchy and taxonomy management provides grouping which, when used in transactions, can give insights to the organization. Metadata management describes the definitions (both technical and business) of data elements. Within the data structures segment data models are defined and implemented. 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 117

Is Data Quality a One Time Thing?

Data cleansing and enrichment Our approach to data cleansing KPMG has developed an approach to data cleansing that has been used for a number of clients. We have specialists in these kind of services. Although not directly part of the phases of the enterprise master data strategy you have indicated that this topic needs to be addressed during the oral presentation. It is thereforethat we already have included our approach to master data cleansing in the proposal for the enterprise master data strategy. 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 9

Data, People and Organizational Change?

People & change management Communication, Stakeholder Management, and People & Change Management is an important part of a program. These aspects must handled methodological and structured instead of ad-hoc. KPMG has specific Change Management and Behavioural Change Methodologies, including lots of tools, templates and techniques. Benefits of effective communication: Enables consistent messaging across the organization and time Engages stakeholders Optimizes awareness and ownership Mitigates change resistance Drives behavioral change by managing expectations of segmented audiences 2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. 11

Thank you

2015 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ( KPMG International ), a Swiss entity. All rights reserved. The KPMG name, logo and cutting through complexity are registered trademarks or trademarks of KPMG International.